RAS genotyping is mandatory to predict anti-EGFR monoclonal antibodies (mAbs) therapy resistance and BRAF genotyping is a relevant prognosis marker in patients with metastatic colorectal cancer. Although the role of hotspot mutations is well defined, the impact of uncommon mutations is still unknown. In this study, we aimed to discuss the potential utility of detecting uncommon RAS and BRAF mutation profiles with next-generation sequencing. A total of 779 FFPE samples from patients with metastatic colorectal cancer with valid NGS results were screened and 22 uncommon mutational profiles of KRAS, NRAS and BRAF genes were selected. In silico prediction of mutation impact was then assessed by 2 predictive scores and a structural protein modelling. Three samples carry a single KRAS non-hotspot mutation, one a single NRAS non-hotspot mutation, four a single BRAF non-hotspot mutation and fourteen carry several mutations. This in silico study shows that some non-hotspot RAS mutations seem to behave like hotspot mutations and warrant further examination to assess whether they should confer a resistance to anti-EGFR mAbs therapy for patients bearing these non-hotspot RAS mutations. For BRAF gene, non-V600E mutations may characterise a novel subtype of mCRC with better prognosis, potentially implying a modification of therapeutic strategy. Tumor mutation hotspots associated with clinical resistance to anti-EGFR mAbs like KRAS exon 2 (codons 12 and 13), exon 3 (codons 59 and 61), exon 4 (codons 117 and 146) and NRAS exon 2 (codons 12 and 13), exon 3 (codons 59 and 61) and exon 4 (codons 117 and 146) are now well identified and are systematically assessed prior to anti-EGFR mAbs prescription5. KRAS and NRAS are both Ras serine-threonine kinases, located downstream of EGFR in the Ras/Raf/MAPK pathway. Mutations in these codons cause constitutive activation of the RAS-MAPK pathway. KRAS and NRAS mutations are reported in 40–50% and 5–8% of patients with mCRC, respectively6.Combination of targeted therapies like anti-EGFR monoclonal antibodies (anti-EGFR mAbs) with chemotherapy regimen (FOLFOX or FOLFIRI) improves progression-free (PFS) and overall survival (OS) in patients with metastatic colorectal cancer (mCRC)KRAS and NRAS mutational statuses are usually assessed using polymerase chain reaction (PCR)-based assays designed for detection of major hotspot mutations. Most PCR-based assays, by their design, are not able to detect non-hotspot mutations; thus, patients with a tumor bearing a non-hotspot mutation are labeled as “wild-type” even though a tumor mutation exists. Next generation sequencing (NGS) assays allow the analysis of full exons and are able to detect uncommon mutational profiles. The impact of non-hotspot mutations of KRAS, NRAS and BRAF on anti-EGFR mAbs resistance is still unclear and it may be useful to study their impact in patients with mCRC.Tumor 7. BRAF mutations are reported in 5–10% of patients with mCRC8. p. (V600E) is the main BRAF hotspot mutation9. This mutation, located on exon 15 of the BRAF protein kinase activation domain leads to an increase of BRAF activity, 130 to 700 times higher than in wild-type (WT) BRAF11. In mCRC, this hotspot mutation is recognized as a poor prognosis factor9. Non-hotspot mutations have also an impact on BRAF protein activity, leading to high, intermediate or impaired kinase activity13.BRAF is a serine-threonine kinase, located downstream of EGFR in the Ras/Raf/MAPK pathwayRAS and BRAF mutations detected during routine sequencing by NGS and discuss their potential role in treatment resistance and prognosis for patients with mCRC using in silico prediction tools.The aim of this study was to identify isolated or concomitant non-hotspot We retrospectively collected data from 857 mCRC samples including 779 samples with valid NGS results. DNA quality was suitable for 91% of the samples for NGS and uncommon mutational profiles were reported in 22  samples.The histological subtypes were: adenocarcinoma for 11 samples, liberkunhian adenocarcinoma for 9 samples, mucinous carcinoma for 1 sample, and ductal carcinoma for 1 sample. Twenty samples were from primary tumors and 2 were from liver metastases , 1 a NRAS mutation (#6) and 4 a BRAF non-hotspot mutation . Seven concomitant RAS mutations  and 7 concomitant RAS and BRAF mutations  were detected. One sample (#12) bore an HRAS mutation not previously described and no sample carried MAP2K1 mutations. Twelve of these mutations are not yet described as somatic nor as single nucleotide polymorphism (SNP) in public database.All observed mutations are described in Table KRAS and NRAS mutations observed in this study are located in the catalytic domain. The majority of these mutations are localized in the GTP binding site. For the BRAF gene, all mutations described in our study are found in the protein kinase domain. Predictions of the impact of each mutation are given in Table For each mutation, the PolyPhen-2 score, SIFT score, their interpretation, and protein domain impacted are described in Table KRAS, NRAS and BRAF mutational profiles associated to in silico projection to evaluate their therapeutic and clinical implications in 779 samples of patients with mCRC. We identified 22 uncommon mutational profiles with a few missense variants that have not been previously reported in the literature, to our knowledge. Structural modelling of the observed missense variants in BRAF, KRAS and NRAS shows that most of the observed mutations can be accommodated in the protein structures without clearly adverse impacts on protein stability (Table KRAS Gly115Glu). While some such mutations may indeed lead to destabilization and inhibition of protein folding, it should be noted that the modelling procedure in FoldX does not consider backbone conformational changes. It is possible that some such mutations could be accommodated in the protein structure once backbone conformational changes are considered; an example is the RAS Gln61Leu mutation (not observed in this study)14. Despite this limitation, FoldX provides a quick, relatively accurate and parsimonious means to evaluate the impacts of mutations on protein stability.In this study, we assessed the value of NGS-based testing to identify uncommon KRAS  are located on the protein surface and lead to minimal impacts on stability when KRAS is considered in isolation. The same is true of the NRAS Asp33Glu mutation, although this is located in the Switch 1 loop and is not directly involved in substrate binding. Examination of structures of RAS proteins in complex with RASGAP and SOS  showed that most of these residues in KRAS and NRAS were not directly involved in the interaction with RASGAP and SOS. The exception is NRAS Asp33Glu, which may affect the interaction with SOS  (sample #21) is described as pathogenic but without published data on response to anti-EGFR mAbs therapy.RAS and/or BRAF mutations. KRAS and BRAF mutations have been frequently described as mutually exclusive in CRC and concomitant KRAS and BRAF mutations are rare, occurring in less than 0.001% of cases20. In our study, in 10 samples with uncommon BRAF mutations, 6 are concomitant with a RAS mutation, and 1 BRAF hotspot mutation had a concomitant RAS mutation. The percentage of concomitant KRAS and BRAF mutations in our dataset is higher than previously described27. Since these studies only assessed BRAF codon 600 hotspot mutations, we may infer that hotspot KRAS mutations are more frequently associated with rare BRAF mutations than with hotspot mutations. This inference agrees with a previous study which found that patients with BRAF non-V600E mutations were more likely to have concomitant RAS mutations than patients with the BRAF V600E mutation16. Due to the infrequent observation of this phenomenon, it is actually not clear whether or not these doubly mutated tumors have a different biology and natural history than KRAS or BRAF mutant tumors. We should illustrate the potential significance of this concomitant mutation on sample #12. Sample #12 bears a KRAS p. mutation described as pathogenic. Whether resistance to anti-EGFR mAbs is conferred by this mutation is unknown, however codon 14 belongs to the same domain as codons 12 and 13. As with mutations on codons 12 and 13, this mutation may be associated with a clinical resistance to anti-EGFR antibodies2. In this case, the knowledge of this rare mutation may be significant in guiding a therapeutic decision and to explain potential resistance to anti-EGFR antibodies. However, the presence of a BRAF non-V600 mutation could modify response to therapy and may even lead to potential treatment resistance. Even if first observations show that BRAF plays only a slight role in resistance to anti-EGFR mAbs, some BRAF non-hotspot mutations might contribute to reduced efficacy of anti-EGFR mAbs28.We now turn our attention to the 14 mutational profiles in our dataset with concomitant In silico prediction of the functional effects of the observed mutations using both sequence- and structure-based approaches suggests possible biochemical mechanisms related to uncommon mutational profile and relevant in cancer. These in silico results warrant further in vivo examination to assess the relevance of detection of non-hotspot RAS mutations and their implication in resistance to anti-EGFR mAbs therapy. For the BRAF gene, non-V600E mutations may describe a novel subtype of mCRC with better prognosis, implying potentially different treatment management strategies.KRAS, NRAS and BRAF mutations from May 2017 to May 2018 in Institut de Cancérologie de Lorraine (France)14. All samples were FFPE tissues from mCRC primary tumor or metastases. Determination of percentage of tumor tissue content and area for macrodissection were based on examination of hematoxylin-eosin stained sections by a senior pathologist. All patients involved in this study gave their informed consent for the research of KRAS, NRAS and BRAF mutation. The experimental protocols of these study have been approved by the ethical and scientific board of Institut de Cancérologie de Lorraine. All methods were performed in accordance with the relevant guidelines and regulations. All data were anonymized prior to analysis.Data from 188 formalin-fixed paraffin embedded (FFPE) samples of histologically proven colorectal cancer tumor tissue previously published were pooled with new data from 669 FFPE samples from patients with mCRC routinely analyzed for 14. After extraction of the 669 new samples, TruSeq FFPE DNA Library Prep QC kit  and qPCR using Cobas z480  were used for quality DNA assessment. Cycle quantification (Cq) values were calculated using LightCycler® 480 Software W UDF 2.0.0 (Roche Diagnostics). For samples showing a ΔQC score lower than 6, DNA libraries were then prepared using TruSeq® Custom Amplicon Library Preparation Kit v1.5 (Illumina). Fifty-three samples failed to yield sufficient DNA quality and 616 samples qualified for DNA library preparation.For all samples, DNA was extracted as previously described using QIAamp DNA FFPE Tissue Kit 14. Library preparation was not possible for 11 samples.For the first 188 samples, library preparation and DNA sequencing were performed using the GS Junior deep pyrosequencing system as previously describedAKT1 (exon3), ALK (exons 23 to 25), BRAF (exons 11 and 15), EGFR (exons 18 to 21), ERBB2 (exon 20), ERBB4 (exons 10 and 12), FGFR2 , FGFR3 , HRAS , KIT , KRAS , MAP2K1 (exon2), MET (exons 2 and 14 to 20), NRAS , PDGFRA  and PIK3CA (exons 10 and 21). The two oligo pools were hybridized to DNA samples. The specific hybridized targets were ligated, extended and PCR amplified with adaptors containing index with specific barcode sequences. Two complementary libraries were generated by targeting the forward and reverse DNA strands. The PCR-amplified amplicon libraries obtained were then purified using AMPure XP beads in order to remove non-specific products and reaction components.For the 616 new samples, libraries were prepared using the TruSeq® Custom Amplicon Library Preparation Kit v1.5. This kit consists of two separate oligo pools (CATA and CATB) and allow the full exon analysis of 16 genes: KRAS, NM_002525.4 for NRAS and NM_004333.5 for BRAF were used for alignment and variant calling. Fourteen samples had insufficient coverage to be interpretable and 602 samples had interpretable sequencing data.Library DNA concentrations were quantified using Qubit 3.0 Fluorometer  and their quality was assessed on Fragment Analyzer  using the Standard Sensitivity NGS Fragment Analysis Kit . PCR product sizes have to be around 260 base pairs in length. All 616 validated libraries were normalized to enable similar amplification and sequencing levels for each sample library in the same run. Sequencing was performed according to the manufacturer’s instructions. All libraries were pooled before sequencing on the MiSeq instrument (Illumina). Sequencing data analysis was performed on Sophia DDM® software . Reference sequences NM_033360.2 for KRAS and NRAS hotspot mutations, or ii) a KRAS, NRAF or BRAF non-hotspot mutation (associated or not associated with other mutations). A threshold of 1% allele frequency has been reported clinically relevant for KRAS mutations linked with lack of response to anti-EGFR therapy29.Uncommon mutational profiles were defined as i) a concomitant MAP2K1 and HRAS genes (available in our gene panel and implicated in the MAPkinase pathway) have been also identified.For samples with an uncommon mutational profile, mutational status of 34. PolyPhen-2 score predicts the possible impact of an amino acid substitution on the structure and function of a human protein. PolyPhen combines amino acid composition analysis in multiple sequence alignments with information from solved protein structures (where available). Sequence composition is evaluated using the Position Specific Independent Counts (PSIC) tool, which calculates sequence profiles35. Differences in the profiles calculated by PSIC for the different variants at a site are indicative of damaging impact. In cases where structural data are available, the impact of a mutation is assessed by considering physicochemical properties such as residue size and hydrophobicity, and the maintenance of contacts with ligands, metals or other interacting proteins. Sequence- and structure-based features are combined to produce predictions using a naïve Bayes classifier. PolyPhen-2 produces scores between 0 and 1 along with annotations of whether the mutation is predicted to be benign or damaging.We used PolyPhen-2 (Polymorphism Phenotyping) and SIFT (Sorting Intolerant from Tolerant) scores to predict mutation impact on the protein, as well as FoldX to model the observed variants in protein structures36.SIFT also predicts whether an amino acid substitution affects protein function. SIFT scores are used to predict the damaging effect of nucleotide substitutions and frame shifts (insertions/deletions) on protein function based on the maintenance of amino acid composition in alignments of the target sequence with closely related sequences. The SIFT server assigns scores for each residue from 0 to 1, where mutations with a score of ≤0.05 are predicted to not be tolerated, and mutations with score >0.05 are predicted to be tolerated34. FoldX estimates the impact of point mutations on the folding energy or stability of the protein using rigid-backbone modelling and a classical forcefield whose parameters are trained to reproduce experimental observations of mutational impacts on folding energy. Starting from the wildtype structures for KRAS, BRAF and NRAS , the missense mutations observed in these proteins were modeled using the FoldX BuildModel tool, and the structural and energetic impacts of each mutation were assessed as the predicted change in folding energy (∆∆G). Synonymous (silent) and stop mutations were not modeled. All FoldX modelling results are available at 10.5281/zenodo.1467311.SIFT and PolyPhen-2 scores were determined using Sophia DDM® software (version 5.0.7). Structural modelling of observed missense mutations was carried out using FoldX version 4KRAS and NRAS protein domains impacted by the different mutations described were identified using UCSF Chimera37. The COSMIC database was used to identify protein domains impacted by BRAF mutations38.


Higher predictive power was observed for methylation loss than mutation burden. We also found that genomic hypomethylation correlates with the immune escape signatures of aneuploid tumours. Hence, DNA methylation alterations implicate epigenetic modulation in precision immunotherapy.Mitotic cell division increases tumour mutation burden and copy number load, predictive markers of the clinical benefit of immunotherapy. Cell division correlates also with genomic demethylation involving methylation loss in late-replicating partial methylation domains. Here we find that immunomodulatory pathway genes are concentrated in these domains and transcriptionally repressed in demethylated tumours with CpG island promoter hypermethylation. Global methylation loss correlated with immune evasion signatures independently of mutation burden and aneuploidy. Methylome data of our cohort ( Demethylation of the genome is found in cancer. Here, the authors show that genomic demethylation entails changes in promoter methylation and gene expression associated with immune escape and suggest that the epigenetic alterations may be an important determinant of responses to immunotherapy. In particular, antibody-mediated interventions targeting cytotoxic T lymphocyte antigen-4 (CTLA-4) and programmed death receptor-1 (PD-1) on T lymphocytes and the principal ligand (PD-L1) on tumour cells can reverse tumour-induced immunosuppression and induce durable clinical responses5 and pre-existing T cell infiltration7 are indicators of clinical benefit of checkpoint blockade. On the other hand, somatic copy number alterations (SCNAs)10, tumour heterogeneity11, and the genetic alteration of specific genes12 or pathways13 have been identified as resistance factors.A major challenge facing current immunotherapies is the identification of biomarkers that predict clinical responses to CTLA-4 and PD-1/PD-L1 blockade. Overall, the mutational or neoantigen load5. Mutation load increases as a result of replicative errors during cell division. Not only mutations but also methylation losses accumulate during successive rounds of cell division14. Global hypomethylation and CGI hypermethylation represent the hallmark methylation changes in cancer15. Considering its association with late replication timing, progressive methylation loss may occur due to the failure of methylation maintenance machinery to remethylate newly synthesized daughter stands during DNA replication16. However, the influence of genomic methylation loss through cell divisions has never been investigated in the context of cancer immunotherapy.Tumour cells produce neoantigens or antigens that the immune system never encountered without cancer. The epitopes of neoantigens are displayed on the surface of cancer cells and provoke immune response. Therefore, tumours with high mutation load are more likely to respond to anti-immunosuppressive strategies based on checkpoint blockade10. A pan-cancer analysis discovered the association of SCNAs with molecular signatures of cytotoxic immune activity across diverse tumour types10. Particularly, highly aneuploid tumours with extensive chromosome- or arm-level SCNAs showed a lower expression of markers indicating infiltrating immune cells. In contrast, focal SCNAs mainly correlated with cell proliferation markers instead of immune activity signatures. However, the mechanism by which aneuploidy affects immune cell infiltration remains unknown. Global demethylation in cancer promotes chromosomal instability20, particularly involving large-scale alterations leading to aneuploidy23. Therefore, we investigated the relationships between methylation changes and aneuploidy.Meanwhile, SCNAs emerged as a resistance parameterHere, we performed large-scale systematic analyses of the molecular data of TCGA samples across a variety of tumour types. We examined the relationships of global methylation levels with markers of cell proliferation, mutation burden, SCNA levels, markers of infiltrating immune cells, and activity of immune-response genes. Importantly, we tested our hypotheses developed from the molecular analyses by using our lung cancer cohort. This is the first study that inspected DNA methylation patterns in the molecular and clinical data with regards to cancer immunotherapy. As a result, we suggest that as an important predictive marker in immunotherapy, genomic demethylation implicates epigenetic modulation as a combination regimen for precision immunotherapy.25 probes , cytokine–cytokine receptor interaction, interferon or other cytokine signaling, and complement and coagulation Fig. . There ines Fig. . We confnes Fig. .16, we examined whether the transcriptional activity of late-replicated genes are affected in the tumours that underwent global demethylation. By using cell line data, we identified genes that are replicated earlier or later in cancer compared with normal cells  as opposed to highly methylated domains (HMDs)16. Our inspection of the methylation variability and replication timing of various PMDs led to three distinct subclasses   methylation and increased CGI/shore methylation Fig. . In agre5. However, when we stratified patient samples according to the L1 methylation levels, the global low methylation group showed poor prognosis despite high mutation load. In the combined lung cancer cohort (n = 141), the hazard ratio (HR) was 0.56   and HR = 0.52 , respectively. Whereas the P values decreased as sample size grew, the effect size (HR) remained a similar level.High mutation load is associated with the clinical benefit of checkpoint blockade−3) Fig. . The IDI−3) Fig.  and SMC −3) Fig.  resultedP = 7.0 × 10−3), mutation burden failed to show significant explanatory power   and IDIBELL cohort (n = 22) resulted in significant stratification by global methylation but not by mutation burden    Fig. . The sam40) Fig.  and poor10. Thus, we examined the possibility that global methylation is able to explain this association. Global demethylation in cancer promotes chromosomal instability20. DNA hypomethylation-related instability is mainly of chromosomal nature and involves large-scale alterations leading to aneuploidy rather than widespread amplifications or deletions23. As in ICF syndrome , failure of methylation maintenance in pericentromeric sequences can cause erroneous chromosomal segregation in cancer42.Our results on aneuploidy from the clinical data contradict the previous findings on the association of aneuploidy and immune evasion signatures10, and compared this result with that of focal SCNAs (fSCNAs). The correlation was stronger with cSCNAs than with fSCNAs . TCGA aliquot barcodes flagged as DO NOT USE in the Merged Sample Quality Annotation file were discarded. We selected cancer types for which there were >100 patient samples with all the molecular data and age information. The selected tumour types encompassed 6968 samples across 21 types (Supplementary Table 16 (https://zwdzwd.github.io/pmd). Aneuploidy level and tumour purity values were obtained from a Table of Taylor et al.49. The cSCNA and fSCNA levels from 11 cancer types were obtained from Davoli et al.10. For glioblastoma multiforme (GBM), microarray-based gene expression data were obtained from the UCSC Xena public data hubs.The batch-corrected and normalized DNA methylation data based on Infinium Methylation 450k technology), together with mRNA expression and gene mutation data, generated by the PanCancer Atlas consortium were obtained from the publication page . We first selected evolutionarily young LINE-1 repeat elements containing at least three different CpG sites that were covered by at least 10 aligned reads and then computed the averaged beta value for each repeat element. The number of the selected repeat elements for each sample ranged from 67,732 to 71,958  mapped to the young subfamilies of LINE-1 (L1HS and L1PA). Probe mapping information and LINE-1 family annotation were obtained from GPL16304 bp mappen = 15; Supplementary Table For each gene per cancer type, a linear regression model was fit using mRNA expression level as the response variable, and global methylation, mutation burden, aneuploidy, tumour purity, age, and tumour stage as predictors. We included tumour stage in the model for tumour types for which at least 100 patient samples with tumour stage information were available . Genes ranked by the z score from the linear regression model were used for input into the preranked module of the GSEA software48 with the immune and proliferation gene sets (see below). GSEA was run with default settings, except for the minimum number of gene sets, which was set to 10.For each of three predictors  per tumour type, GSEA53 and ENCODE project portal54. Average wavelet-smoothed signal values in each 5-kb window were scaled and quantile-normalized. Windows harbouring missing values in any of the cell lines were excluded. We performed the student’s t-test for each 5-kb window to assess the replication timing difference between the normal and cancer cells. We then assigned the P value and t statistic to each gene. For genes spanning multiple replication timing windows, we assigned a combined P value using the Fisher’s method and average t statistic. We excluded genes located on sex chromosomes or specifically expressed in the immune system (see below). Genes with Bonferroni-adjusted P value < 0.05 were defined as early- or late-replicated genes in cancer. Genes ranked by the t statistic were used for input into the preranked module of the GSEA software48 with the canonical REACTOME and KEGG pathways from MSigDB55.Repli-Seq measurements  of ENCODE 5 normal  and 10 cancer cell lines  were downloaded from the UCSC Genome Browserth percentile) and high (>70th percentile) global methylation groups for each cancer type. After a z-score normalization of mRNA expression data per gene per cancer type, we computed the average expression level of the genes for each group and compared the groups. To calculate the fraction of enriched cell-cycle and immune pathways  were used for input into the preranked module of the GSEA software with REACTOME pathways (provided at https://reactome.org/) belonging to the Cell Cycle or Immune System category. Pathways harbouring at least 10 genes were used for this analysis.To investigate the expression differences of early- and late-replicated genes between tumour samples with high and low global methylation levels, we partitioned tumour samples into the low  in short PMDs for which methylation level is greater than two standard deviations from the mean.By employing previously defined locations of PMDs and HMDs per 100 kbn = 13; Fig. 16) and then estimated its P value by generating a background distribution. We randomly picked 77 PMD genes and calculated the average distance of them to the nearest HMD. This procedure was repeated 10,000 times.For a total of 77 immune genes in the pathways enriched for short PMDs  were derived from Charoentong et al.57. Hallmark immune and proliferation gene sets were obtained from MSigDB55. Antigen presentation and cytokine signaling pathways were derived from canonical KEGG pathway in MSigDB55. The gene set for the immune signature score was obtained from the aneuploidy study10. To filter out genes that are specifically expressed in the immune system, we used gene expression data from the Illumina’s Human BodyMap 2.0 project . Genes for which the average expression level in leukocytes and lymph nodes was five-fold higher than that in the remaining tissues (n = 14) were considered as genes specifically expressed in the immune system  were excluded. The normalized expression levels were standardized per tumour type for comparison.To quantify the LINE-1 and ERV expression levels, we aligned RNA-seq reads against the LINE-12. Partial response (PR) or stable disease (SD) that lasted more than 6 months was considered as DCB/responder. Progressive disease (PD) or SD that lasted less than 6 months was considered as NDB/non-responder. Progression-free survival (PFS) was calculated from the start date of therapy to the date of progression or death, whichever is earlier. Patients were censored at the date of the last follow-up for PFS if they were not progressed and alive. We complied with all relevant ethical regulations for work with human participants. Informed consent was obtained. This study was approved from the institutional review board at Samsung Medical Center (2018-03-130 and 2013-10-112).A total of 60 advanced non-small cell lung carcinoma patients who were treated with anti-PD-1/PD-L1 from 2014 to 2017 at Samsung Medical Center were enrolled for this study , AllPrep DNA/RNA Micro Kit , or QIAamp DNA FFPE Tissue Kit  for library preparation for whole exome sequencing. Library preparation was performed by using SureSelectXT Human All Exon V5  according to the instructions60 with default parameters. PCR duplicate reads were marked using Picard62. We used Strelka263 to call somatic variants and selected single nucleotide variants (SNVs) and indels covered by at least ten and five reads in tumour, respectively. We further filtered out common germline variants present in dbSNP 15064 and annotated somatic variants using ANNOVAR65. The list of filtered SNVs and indels is provided in Supplementary Data 66 with the circular binary segmentation algorithm  to detect amplifications/deletions affecting at least 10% of a chromosome arm or 5% of a chromosome. The aneuploidy level was the sum of the absolute segment log2 ratio, each weighted by its length10.The sequencing reads were aligned to the human reference genome (hg19) with BWA mem module (v0.7.12)RNA was extracted from same tumour tissue using Allprep DNA/RNA Mini Kit . RNA was extracted from formalin fixed paraffin embedded (FFPE) using Rneasy FFPE kit . RNA was assessed for quality and quantity using nanodrop 8000 UV-Vis spectrometer (NanoDrop Technologies Inc) and 4200 TapeStation Instrument (Aglient Technologies). RNA integrity number (RIN) of > = 5 were selected for further library preparation. In total 500 ng of RNA from fresh tissues and 100 ng of RNA from FFPE were used for library preparation using Truseq RNA library prep kit v2  or Truseq RNA access library prep kit , respectively. The library was generated according to the manufacturer’s instructions. RNA libraries were multiplexed and sequenced with 100 bp pair end reads on HiSeq2500 platform (Illumina).67 and gene expression values were quantified using RSEM68. Genes ranked by t-values obtained from comparing mRNA expression levels between tumours with low (n = 14) and high global methylation level (n = 13) were used for input into the preranked module of the GSEA software with KEGG pathways and the MHC gene set.The RNA-seq reads were aligned to the human reference genome (hg19) with STAR69. The methylation data were treated as described in the Estimating global methylation levels section. The PMD levels of our cohort samples were calculated based on the average of EPIC probes for Solo-WCGW CpGs in common PMDs16 (provided at https://zwdzwd.github.io/pmd). Redundant probes such as multi-hit probes by using the filter function of the ChAMP package70. We used MethylCIBERSORT71 and ESTIMATE72 to estimate tumour purity and leukocyte fraction . Briefly, 500 ng genomic DNA (gDNA) was used for bisulfite conversion using the EZ DNA methylation kit . The bisulfited gDNA was denatured and neutralized for amplification, and was further processed for fragmentation. After fragmentation, DNA was eluted and resuspended in a hybridization buffer, and then hybridized onto the BeadChip. The BeadChip was prepared for staining and extension after washing out unhybridized DNA, and it was imaged using the Illumina iScan System. The raw intensity files were then preprocessed into beta values using the preprocessIllumina function in minfin = 15) were obtained from Ock et al.9. We included additional 25 patients that received other types of immunotherapy using drug data from the GDC legacy archive. We selected samples for which the therapy type (CDE_ID:2793530) column indicated immunotherapy while excluding samples from patients that received multiple drugs. The molecular data for these samples were obtained as described in the TCGA molecular and clinical data section.Progression-free survival data for melanoma patients who received immune checkpoint inhibitors (drug name labelled as Ipilimumab, Yervoy, or Pembrolizumab; Global methylation and mutation burden were combined in a multivariable Cox proportional hazards model using the coxph function in R. The multivariable logistic regression model was used to assess the impact of global methylation and mutation burden on the objective response using the glm function in R.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting SummarySource DataSupplementary Software 1


Understanding any brain circuit will require a categorization of its constituent neurons. In hippocampal area CA1, at least 23 classes of GABAergic neuron have been proposed to date. However, this list may be incomplete; additionally, it is unclear whether discrete classes are sufficient to describe the diversity of cortical inhibitory neurons or whether continuous modes of variability are also required. We studied the transcriptomes of 3,663 CA1 inhibitory cells, revealing 10 major GABAergic groups that divided into 49 fine-scale clusters. All previously described and several novel cell classes were identified, with three previously described classes unexpectedly found to be identical. A division into discrete classes, however, was not sufficient to describe the diversity of these cells, as continuous variation also occurred between and within classes. Latent factor analysis revealed that a single continuous variable could predict the expression levels of several genes, which correlated similarly with it across multiple cell types. Analysis of the genes correlating with this variable suggested it reflects a range from metabolically highly active faster-spiking cells that proximally target pyramidal cells to slower-spiking cells targeting distal dendrites or interneurons. These results elucidate the complexity of inhibitory neurons in one of the simplest cortical structures and show that characterizing these cells requires continuous modes of variation as well as discrete cell classes. Single-cell RNA sequencing allows scientists to count the number of copies of each gene expressed in multiple individually isolated cells. Because different cell types express genes in different amounts, “clusters” of cells with similar expression patterns are likely to correspond to different cell types. As well as discrete classes, however, cells also show continuous variation in gene expression. To study the relationship between cell classes and continua in a well-understood brain system, we applied new analysis methods to a dataset of inhibitory interneurons from area CA1 of the mouse hippocampus. Thanks to decades of intensive work, at least 23 classes of CA1 interneurons have been previously defined. We were able to identify them all with our transcriptomic clusters but unexpectedly found three to be identical. Because the connectivity of these cells has already been established, we were also able to identify the primary mode of continuous variation in these cells, which related to their axon target location. This in-depth understanding of the relatively simple cortical circuit of CA1 not only clarifies the cellular composition of this important brain structure but also will form a solid foundation for understanding more complex structures, such as the isocortex. Cortical circuits are composed of highly diverse neurons, and a clear definition of cortical cell types is essential for the explanation of their contribution to network activity patterns and behavior. Cortical neuronal diversity is strongest amongst GABAergic neurons. In hippocampal area CA1—one of the architecturally simplest cortical structures—GABAergic neurons have been divided so far into at least 23 classes of distinct connectivity, firing patterns, and molecular content . A complCA1 GABAergic neurons have been divided into six major groups based on connectivity and expression patterns of currently used molecular markers. Parvalbumin (PVALB)-positive neurons  target pyramidal cells’ somata, proximal dendrites, or axon initial segments, firing fast spikes that lead to strong and rapid suppression of activity ,8. SomatThis already complex picture likely underestimates the intricacy of CA1 inhibitory neurons. Currently defined classes likely divide into several further subtypes, and additional neuronal classes likely remain to be found . FurtheSingle-cell RNA sequencing (scRNA-seq)—which can read out the expression levels of all genes in large numbers of individual cells—provides a powerful opportunity to address these questions. This method has successfully identified the major cell classes in several brain regions –46. NeveHere, we describe a transcriptomic analysis of 3,663 inhibitory neurons from mouse CA1. This analysis revealed 49 clusters, of which we could identify 41 with previously described cell types, with the remaining 8 representing putative novel cell types. All previously described CA1 GABAergic classes could be identified in our database, but our results unexpectedly suggest that three of them are identical. The larger number of clusters occurring in our transcriptomic analysis reflected several previously unappreciated subtypes of existing classes and tiling of continua by multiple clusters. Our data suggest a common genetic continuum exists between and within classes, from faster-firing cells targeting principal cell somata and proximal dendrites, to slower-firing cells targeting distal dendrites or interneurons. Several classes previously described as discrete represent ranges along this continuum of gene expression.Slc32a1-Cre;R26R-tdTomato mice, three of age p60 and three of age p27. Cells were procured using enzymatic digestion and manual dissociation  is added to the log likelihood.Given a dataset lleagues . Note thTo relate the correlations of each gene with the latent factor to their GO categories , we usedS1 Figp = 3 × 10−5; χ2 test), but post hoc analysis  did not identify any clusters with individually significant differences between mice. We suspect that the weakly significant difference might reflect slightly differential dissection of layers in the four samples. nbtSNE, negative binomial t-stochastic neighbor embedding.Each plot shows the location of all cells collected in a sample on the nbtSNE plots. Top row: mice of age p60; bottom row, age p27. Statistical testing of homogeneity of cluster IDs between mice yielded a weakly significant overall result ((TIF)Click here for additional data file.S2 FigA) Standard deviation versus mean expression in a population of CA1 pyramidal cells . Each point represents a single gene. Red curve shows prediction of negative binomial distribution with r = 2. (B) Example negative binomial probability distribution. Low probability is assigned to very small counts, but for larger counts, there is no strong dependence on precise count value. (C) Symmetrized Kullback-Liebler divergence between two negative binomial distributions, as a function of their mean values. High values (indicating poor fit) are obtained when one mean is close to zero, and the other is large; when both means are far from zero, their precise values do not have a major effect.((TIF)Click here for additional data file.S3 FigProMMT, Probabilistic Mixture Modeling for Transcriptomics.(TIF)Click here for additional data file.S4 FigLeft columns show results when all genes are used; right columns show results with the 150 genes selected by ProMMT. Top row shows a Euclidean metric; bottom row shows Euclidean metric after log(1+x) transformation. All methods were initialized from the same starting point as nbtSNE. Of the four methods, only the log(1+x) transformed data with gene subset gave comparable results to nbtSNE. This indicates that the primary effect of the negative binomial distribution is to downweight differences in expression between strongly expressed genes, similarly to the log(1+x) transformation, and that gene subsetting produces more interpretable results whether or not transformation is used. nbtSNE, negative binomial t-stochastic neighbor embedding; ProMMT, Probabilistic Mixture Modeling for Transcriptomics; tSNE, t-stochastic neighbor embedding.(TIF)Click here for additional data file.S5 FigA) nbtSNE algorithm applied to 761 interneurons of mouse V1, from Tasic and colleagues (2016). Symbols indicate 23 clusters assigned by Tasic and colleagues. (B) Same data, with symbols representing 30 clusters assigned by ProMMT algorithm. (C) Confusion matrix relating cluster assignments made by the two algorithms. Right; cell classes identified with ProMMT clusters (black), and genes used to make the identification . (D) Reprint of figure from Cadwell and colleagues (2016) showing expression of selected genes in layer 1 SBCs and eNGCs. (E) Scatterplot matrix showing expression of these genes in Tasic and colleagues’ data support the identification of SBCs made by ProMMT algorithm. eNGC, elongated neurogliaform cell; nbtSNE, negative binomial t-stochastic neighbor embedding; ProMMT, Probabilistic Mixture Modeling for Transcriptomics; SBC, single-bouquet cell.((TIF)Click here for additional data file.S6 FigLeft and right panels show scatterplot matrices for sets of genes with near-exclusive expression in further subdivisions of the Vip Parm1 and Sst Cbln4 clusters. Red and green points indicate which subcluster the cell was placed in by the ProMMT algorithm. ProMMT, Probabilistic Mixture Modeling for Transcriptomics.(TIF)Click here for additional data file.S7 FigA) To investigate how the number of detected clusters might change with the number of cells analyzed, we reclustered random subsets of different numbers of cells. The number of clusters identified increased with cell count. (B) To investigate how the number of detected clusters might change with read depth, we resampled reads independently for each cell and gene, following a binomial distribution with probability between 0 and 1. Again, cluster count increased linearly with read depth; although a marginally sublinear trend was potentially visible, this was not statistically significant . (C) Expected gene count , computed as a function of the binomial probability. Color scheme indicated below.  Similar analysis as  for the data of Tasic and colleagues (2016).((TIF)Click here for additional data file.S8 FigA) Mean latent factor values differ between cell classes (cf. B) Correlations of genes with the latent factor for isocortical Pvalb cells (y-axis) are similar to those of their CA1 counterparts Click here for additional data file.S9 FigSst neurons , weaker but significant for Pvalb neurons , and insignificant for Vip neurons . Only genes of strong mean expression were analyzed . A small number of example genes of particular interest are highlighted with red text.Each panel represents a cell type analyzed by Mardinly and colleagues (2016). Within each panel, every point represents a gene, and the y-axis value shows the log ratio of its expression level after 7.5 h of light exposure, compared to dark housing, in the corresponding subtype of visual cortical interneurons. The x-axis value shows that gene’s latent factor weighting as determined from our CA1 data. Blue line shows linear regression fit, which was strongest for (TIF)Click here for additional data file.S10 FigThe main group of cells exhibited a continuous gradation of gene expression, consistent with previous reports of graded expression of genes such as Wfs1 and Dcn between dorsal and ventral CA1. No cells were identified as CA2 or CA3, as we did not detect populations consistently expressing genes such as Cacng5, Sostdc1, S100b, Ccdc3, Iyd, or Coch. However, three small clusters together containing 62 of the total 357 excitatory neurons were identified as either occurring at the dorsomedial lip of stratum oriens or subiculum, because of their expression of genes such as Tshz2, Tle4, Lxn, and Car10, whose Allen atlas expression patterns are shown at bottom.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 Text(PDF)Click here for additional data file.


Background: Androgen steroid hormones are key drivers of prostate cancer. Previous work has shown that androgens can drive the expression of alternative mRNA isoforms as well as transcriptional changes in prostate cancer cells. Yet to what extent androgens control alternative mRNA isoforms and how these are expressed and differentially regulated in prostate tumours is unknown.Methods: Here we have used RNA-Seq data to globally identify alternative mRNA isoform expression under androgen control in prostate cancer cells, and profiled the expression of these mRNA isoforms in clinical tissue.Results: Our data indicate androgens primarily switch mRNA isoforms through alternative promoter selection. We detected 73 androgen regulated alternative transcription events, including utilisation of 56 androgen-dependent alternative promoters, 13 androgen-regulated alternative splicing events, and selection of 4 androgen-regulated alternative 3′ mRNA ends. 64 of these events are novel to this study, and 26 involve previously unannotated isoforms. We validated androgen dependent regulation of 17 alternative isoforms by quantitative PCR in an independent sample set. Some of the identified mRNA isoforms are in genes already implicated in prostate cancer , or in genes important in other cancers (e.g.NUP93 andMAT2A). Importantly, analysis of transcriptome data from 497 tumour samples in the TGCA prostate adenocarcinoma (PRAD) cohort identified 13 mRNA isoforms  that are differentially regulated in localised prostate cancer relative to normal tissue, and 3  which change significantly with Gleason grade and  tumour stage.Conclusions: Our findings dramatically increase the number of known androgen regulated isoforms in prostate cancer, and indicate a highly complex response to androgens in prostate cancer cells that could be clinically important. Altered splicing patterns have been suggested as a new hallmark of cancer cells8, and in prostate cancer there is emerging evidence that expression of specific mRNA isoforms derived from cancer-relevant genes may contribute to disease progression11.A single human gene can potentially yield a diverse array of alternative mRNA isoforms, thereby expanding both the repertoire of gene products and subsequently the number of alternative proteins produced. mRNAs with different exon combinations are transcribed from most (up to 90%) human genes, and can generate variants that differ in regulatory untranslated regions, or encode proteins with different sub-cellular localisations and functions13. The AR belongs to the nuclear receptor superfamily of transcription factors, and is essential for prostate cancer cell survival, proliferation and invasion16. Classically, androgen binding promotes AR dimerization and its translocation to the nucleus, where it acts as either a transcriptional activator or a transcriptional repressor to dictate prostate specific gene expression patterns23. The major focus for prostate cancer therapeutics has been to reduce androgen levels through androgen deprivation therapy (ADT), either with inhibitors of androgen synthesis  or with antagonists that prevent androgen binding to the AR 24. Although ADT is usually initially effective, most patients ultimately develop lethal castrate resistant disease for which there are limited treatment options12.Androgen steroid hormones and the androgen receptor (AR) play a key role in the development and progression of prostate cancer, with alternative splicing enabling cancer cells to produce constitutively active ARsin vitro and within patients following ADT identified a set of 700 genes whose transcription is regulated by the AR in prostate cancer cells25. However, in addition to regulating transcriptional levels, steroid hormone receptors can control exon content of mRNA29. In prostate cancer androgens can modulate the expression of mRNA isoforms via pre-mRNA processing and promoter selection30. The AR can recruit the RNA binding proteins Sam68 and p68 as cofactors to influence alternative splicing of specific genes, and studies using minigenes driven from steroid responsive promoters indicate that the AR can affect both the transcriptional activity and alternative splicing of a subset of target genes32. Other steroid hormones also coordinate both transcription and splicing decisions29. The thyroid hormone receptor (TR) is known to play a role in coordinating the regulation of transcription and alternative splicing27, and the oestrogen receptor (ER) can both regulate alternative promoter selection and induce alternative splicing of specific gene sets that can influence breast cancer cell behaviour35.Androgens and other steroid hormones have also been associated with alternative splicing. Recent RNA-sequencing-based analysis of the androgen response of prostate cancer cells grown10. However, to what extent androgen-regulated mRNA isoforms are expressed in clinical prostate cancer is unclear. To address this, here we have used RNA-Sequencing data to globally profile alternative isoform expression in prostate cancer cells exposed to androgens, and correlated the results with transcriptomic data from clinical tissue. Our findings increase the number of known AR regulated mRNA isoforms by 10 fold and imply that pre-mRNA processing is an important mechanism through which androgens regulate gene expression in prostate cancer.In previous work we used exon level microarray analysis to identify 7 androgen dependent changes in mRNA isoform expression36. All cells were grown at 37°C in 5% CO2. LNCaP cells  were maintained in RPMI-1640 with L-Glutamine  supplemented with 10% Fetal Bovine Serum (FBS) . For androgen treatment of cells, medium was supplemented with 10% dextran charcoal stripped FBS  to produce a steroid-deplete medium. Following culture for 72 hours, 10 nM synthetic androgen analogue methyltrienolone (R1881)  was either added (Androgen +) or absent (Steroid deplete) for the times indicated.Cell culture was as described previously25 was performed according to the Tuxedo protocol37. All reads were first mapped to human transcriptome/genome (build hg19) withTopHat38/Bowtie39, followed by per-sample transcript assembly withCufflinks40. The mapped data was processed withCuffmerge,Cuffdiff andCuffcompare, followed by extraction of significantly differentially expressed genes/isoforms; expression changes between cells grown with androgen and cells grown without androgens were assessed. Reference files for the human genome (UCSC build hg19) were downloaded from the Cufflinks pages: (UCSC-hg19 package from June 2012 was used.). The software versions used for the analysis were: TopHat v1.4.1, SAM tools Version: 0.1.18 (r982:295), bowtie version 0.12.8 (64-bit) and cufflinks v1.3.0 (linked against Boost version 104000). The Tuxedo protocol37 was carried out as follows: For steps 1–5, no parameters (except for paths to input/output files) were altered. In step 15, additional switches -s, -R, and -C were used when running cuffcompare. Steps 16–18 (extraction of significant results) were performed on the command line.RNA-seq transcript expression analysis of previously generated data10. Real time PCR was performed in triplicate on cDNA using SYBR® Green PCR Master Mix  and the QuantStudio 7 Flex Real-Time PCR System (Thermo Fisher Scientific). Samples were normalised using the average of three reference genes, GAPDH, β -tubulin and actin. Ct values for each sample were calculated usingSDS 2.4 software (Applied Biosystems) and relative mRNA expression was calculated using the 2-ΔΔCt method. All primer sequences are listed in41.Cells were harvested and total RNA extracted using TRIzol  according to manufacturer's instructions. RNA was treated with DNase 1  and cDNA was generated by reverse transcription of 500ng of total RNA using the Superscript VILO cDNA synthesis kit . Alternative events were analysed by either reverse transcriptase PCR or real-time PCR. Exon profiles were monitored and quantified using the Qiaxcel capillary electrophoresis system (Qiagen) and percentage inclusion was calculated as described previouslyThe following commercial antibodies were used in the study: anti-RLN2 rabbit monoclonal , anti-TACC2 rabbit polyclonal antibody , anti-NDUFV3 rabbit polyclonal antibody , anti-actin rabbit polyclonal , anti-α-Tubulin mouse monoclonal , normal rabbit IgG  and normal mouse IgG .42. Enrichment of GO terms (with b500 annotations) was calculated using thegoseq R package (version 1.18.0). Genes were considered significant at a p-value threshold of 0.05 after adjustment using the Benjamini-Hochberg false discovery rate.Gene ontology (GO) analysis of RNA-Seq data was carried out as described previouslyhttps://doi.org/10.7908/C11G0KM943). Transcriptome data from the TCGA PRAD cohort were analysed for alternative isoform expression, with transcript models relying on TCGA GAF2.1, corresponding to the University of California, Santa Cruz (UCSC) genome annotation from June 2011 (hg19 assembly). This annotation encompassed 42 of the 73 androgen-regulated alternative mRNA isoform pairs identified. These were studied using two types of analysis: 1) differential transcript expression between tumour and normal prostate tissue and 2) correlation between isoform expression in tumour samples and Gleason score or tumour stage.Available clinical and processed RNA-Seq data from The Cancer Genome Atlas (TCGA) prostate adenocarcinoma (PRAD) cohort, comprising 497 tumour samples from as many patients with different stages / Gleason grades and 52 matched samples taken from normal prostate tissue  following its RNA-Seq analysis workflow44. This workflow was also used for differential isoform ratio analysis, relying on logit-transformed ratio (see below). An FDR-adjusted p-value of 0.05 for the moderated t-statistics was used as threshold for significance of differential expression. Individual isoform expression was estimated in TPM (transcripts per million mapped reads). The expression ratio, henceforth called PSI (percent spliced-in), of each annotated androgen-regulated isoform pair in each TCGA sample was calculated as the ratio between the expression of isoform 1 and the total expression of isoforms 1 and 2 combined, i.e. the sum of their expressions. For each isoform pair, ΔPSI is the difference of median PSI between the tumour and the normal groups of samples.Differential isoform and gene expression analysis was performed on estimated read counts using theTwo-tailed Spearman’s rank correlation tests were used to study the association between isoform expression and both Gleason score and tumour stage (these were used herein as numeric variables). An FDR-adjusted p-value of 0.05 was used as threshold for significance. Isoform expression differences between tumour and normal samples were considered equivalent to those detected in LNCaP cells under androgen stimulation when there was a statistically significant consistent change in the levels of the expected induced or repressed isoform (1 or 2), concomitant with no contradictory change in the PSI. Isoform “switches” were considered equivalent when there was a minimum (ΔPSI > 2.5%) and statistically significant consistent change in the PSI. Equivalent criteria were used to evaluate the equivalence between androgen-dependence and the associations with Gleason score and tumour stage.Statistical analyses were conducted using the GraphPad Prism software (version 5.04/d). PCR quantification of mRNA isoforms was assessed using the unpaired student’s t-test.Data is presented as the mean of three independent samples ± standard error of the mean (SEM). Statistical significance is denoted as * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001 and **** p ≤ 0.0001.25 to globally profile how frequently androgens drive production of alternative mRNA isoforms in prostate cancer cells. This analysis identified a group of 73 androgen regulated alternative mRNA isoforms, which could be validated by visualisation on the UCSC Genome Browser45  of the androgen regulated alternative events detected in response to androgen stimulation are predicted to change the amino acid sequence of the resulting protein . Some ofed PrLZ 46–49. C2 gene ; and excisoform . We also domain ; and ind domain .RLN1 andRLN2 genes which encode peptide hormones that may be important in prostate cancer55. Androgens drive a promoter switch in bothRLN1 andRLN2 to produce predicted non-coding or untranslated mRNA isoforms, reducing expression of protein-codingRLN1 andRLN2 mRNA isoforms. To test whether prostate cancer cells turn off gene expression by switching between utilisation of promoters that generate coding and noncoding mRNAs, we analysed RLN2 protein levels. Consistent with our hypothesis and a previous study55, RLN2 protein production was negatively regulated by androgens in parallel to the switch to the non-coding mRNA isoform  . These iisoform .LIG4 gene  with no impact on the coding sequence. These include a promoter switch in theG4 gene .104. The remaining isoform pairs identified within our dataset have not been previously annotated by UCSC, therefore it was not possible to include them in our comparison. A description of the cohort used is summarised inTo investigate potential links between androgen-dependent mRNA isoforms and tumourigenesis, we analysed the expression of 41 androgen-regulated mRNA isoform pairs in clinical prostate adenocarcinoma and normal prostate tissues. This analysis utilised transcriptomic data from 497 tumour samples and 52 normal samples in the PRAD TCGA cohortTACC2,TPD52,NUP93,PIK3R1,RDH13,ZFAND6,CDIP1,YIF1B,LIMK2, andFDFT1; an alternative 3´ end inCNNM2; and alternative exons inNDUFV3 andSS18 (ZFAND6 andCDIP1) are predicted to introduce a change in the 5′UTR, whereas all the others are predicted to alter the resulting protein isoform. A number of mRNA isoforms that were androgen responsive in LNCaP cells showed tumour specific alterations opposite to the effect of androgen stimulation. These wereLIG4,MAPRE2,OSBPL1A,SEPT5,NR4A1, andRCAN1 . For the remaining 14 mRNA isoform pairs, the data was inconclusive according to the consistency conditions listed in the methods section , the expression changed significantly with Gleason score and showed specific alterations consistent with the effect of androgen stimulation. Conversely, 9 alternative isoforms (which were androgen responsive in LNCaP cells) showed tumour specific alterations opposite to the effect of androgen stimulation . 3 androgen regulated mRNA isoforms  change significantly with both Gleason grade and tumour stage.We next investigated whether the identified androgen-dependent mRNA isoforms are differentially expressed during prostate cancer progression by correlating the expression levels of each isoform with Gleason scores and prostate tumour grades within the PRAD TCGA cohort . For 6 oReal-time PCR raw Ct valuesClick here for additional data file.Copyright: © 2018 Munkley J et al.2018Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Raw unedited western blot imagesClick here for additional data file.Copyright: © 2018 Munkley J et al.2018Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).106. Alternative promoter use can also generate mRNA isoforms with distinct functional activities from the same gene, sometimes having opposing functions11.The main function of the androgen receptor (AR) is as a DNA binding transcription factor that regulates gene expression. Here we show the AR can couple hormone induced gene transcription to alternative mRNA isoform expression in prostate cancer. In response to androgens, the AR can induce the use of alternative promoters, induce the expression of alternatively spliced mRNA isoforms, regulate the expression of non-coding mRNA transcripts, and promote the transcription of mRNA isoforms encoding different protein isoforms. Importantly, we also find that some of these alternative mRNA isoforms are differentially regulated in prostate cancer versus normal tissue and also significantly change expression during tumour progression. Our data suggest that most androgen regulated alternative mRNA isoforms are generated through alternative promoter selection rather than through internal alternative exon splicing mechanisms. This suggests expression of alternative isoforms of specific genes can be a consequence of RNA polymerase being recruited to different promoters in response to androgen stimulation. Alternative promoter usage has been observed for many genes and is believed to play a significant role in the control of gene expression7. We also identified 4 AR regulated alternative mRNA 3′ end isoform switches. This is the first time that regulation of 3′ mRNA end processing has been shown to be controlled by androgens. The selection of alternative 3′ ends can produce mRNA isoforms differing in the length of their 3′ UTRs (which can lead to the inclusion or exclusion of regulatory elements and influence gene expression), or in their C-terminal coding region (which can contribute to proteome diversity)114. Defective 3′ mRNA processing of numerous genes has been linked to an oncogenic phenotype119, and the 3′ mRNA end profiles of samples from multiple cancer types significantly differ from those of healthy tissue samples121.Androgen exposure further drives a smaller number of alternative splicing events suggesting that the AR could contribute to altered patterns of splicing in prostate cancer cells. Tumour progression is believed to be associated with a coordinated change in splicing patterns which is regulated by several factors including signalling molecules124. Specifically, Sam68 and p68 have been shown to modulate AR dependent alternative splicing of specific genes and are significantly overexpressed in prostate cancer32. In future work it will be important to define the role of specific AR co-regulators in AR mediated isoform selection.Based on the findings presented in this study, we propose that activated AR has the ability to coordinate both transcriptional activity and mRNA isoform decisions through the recruitment of co-regulators to specific promoters. The genomic action of the AR is influenced by a large number of collaborating transcription factorsSome of the androgen dependent mRNA isoforms identified here are predicted to yield protein isoforms that may be clinically important, or to switch off protein production via generation of noncoding mRNA isoforms. Although the functional significance of the alternative mRNA isoforms identified in this study is yet largely unexplored, as is their role in the cellular response to androgens, the presented results emphasize the importance of analysing gene regulation and function at the mRNA isoform level.The data referenced by this article are under copyright with the following copyright statement: Copyright: © 2018 Munkley J et al.Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).https://doi.org/10.1016/j.ebiom.2016.04.01825The RNASeq data from LNCaP cells has been published previouslyThe RNAseq custom tracks are available inhttps://doi.org/10.7908/C11G0KM943Prostate adenocarcinoma cohort RNA-Seq data was downloaded from the Broad Institute TCGA Genome Analysis Center: Firehose 16/01/28 run10.5256/f1000research.15604.d21287341Dataset 1: Real-time PCR raw Ct values10.5256/f1000research.15604.d212874125Dataset 2: Raw unedited western blot images Prostate cancer is a common cancer in men that is driven in part through deregulated androgen signalling. The importance of androgen inhibitors in prostate cancer therapy and the clinical challenges posed by the development of androgen-resistant disease both justify the detailed description of the effects of androgen treatment on gene transcription and alternative splicing in prostate cancer cells. In this sense, the analyses reported by Munkley and colleagues represent valuable additions to the literature. However, further explanation of the results presented would increase the reader’s ability to understand these results and their significance, and to identify which results should be prioritised for further research. I have therefore provided some specific suggestions to increase the accessibility of the data as they are currently presented.The genes shown in Tables 1, 2, 3 and 5 are not shown in alphabetical order. It is unclear how these genes are ranked and why they are shown in the orders displayed. It would be helpful for any groupings of genes to be more clearly displayed in these tables where this is relevant.It would be helpful to more clearly indicate which findings are novel and which are supported by the literature in Tables and/or Figures.In Table 1, a number of genes are shown in bold, but this is not explained.In Table 1, it would be helpful to annotate the isoform ID’s shown (columns towards the right side of Table). What does “comparable” mean here?It is challenging to show data for a large number of genes, most of which the authors will not be familiar with. However, in Figure 2, incorrect information is shown for the TPD52 gene (panel A). The PrLZ isoform is actually longer than the TPD52 isoform , yet the sizes of these isoforms indicated in the diagram at the right have been switched (TPD52 is incorrectly shown to be the longer isoform). The authors should check whether this is an isolated error or whether other data for the TPD52 and PrLZ isoforms have been switched .It would be helpful for Table 4 to include percentages as well as sample numbers so that readers can compare the composition of the TCGA PRAD cohort with other published cohorts.Analyses compared differential isoform expression in prostate cancer and normal tissue. The cohort included 497 prostate cancer patients, for which 52 had matched normal tissue . I’ve assumed that these analyses compared transcript levels in the 497 prostate cancer cases with those in the 52 normal tissue cases. However, given that the 52 normal tissue cases had matched tumour samples available, were analyses conducted to compare the 52 matched cases? These analyses could be argued to be more robust through comparing matched samples, albeit in a smaller cohort.Table 5 should indicate the numbers of tumour and normal tissue samples compared.Some data in Tables 5, 6, and 6 are shown in bold, but this is not explained.I could not open Dataset 2. Could this be made available as a pdf file?All violin plots  should specify the sample numbers compared, either below the X axis or in the figure legend if the same sample numbers apply to every plot shown. Specific commentsI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This paper by Munkley and colleagues identifies in a comprehensive manner novel alternative mRNA isoforms regulated by androgens. Interestingly most isoforms result from a choice between alternative promoters, suggesting that regulation takes place mostly at the transcriptional level, but they identified also a few alternative cassette exons and 3' ends. They show experimental validation for 17 isoforms. Beside increasing the number of identified genes in the context of androgen-treated prostate cancer LNCaP cells, the authors analysed the expression of those new isoforms in a large cohort of prostate tumours. They found the expression of some of the mRNA isoforms is positively correlated in the androgen-treated cell and in cancer versus normal samples, and find further correlation with the tumour grade and stage for 3 alternative isoforms. Overall this is an interesting work that clearly deserves to be published, as it reveals new potentially interesting target genes for prostate cancer. I have only a couple of comments/questions that may help to improve the strength of the manuscript.OSBPL1A,CLK3 andTSC22D3, which is correlated to tumour stage ? As these new isoforms are predicted to alter the protein sequence, is it possible to discuss or predict what could be the impact of these modifications for these proteins, with regards to what is known about their function and/or in the context of prostate cancer? Did the authors try to experimentally validate the regulation of alternative isoforms for the 3 most interesting genes, i.e. Looking at the RNA-seq profiles for the validated examples, it seems to me that in some cases, especially for RLN1 and RLN2, regulation of promoter choice correspond also to changes in the 3 end of the transcript (the peak seems to be shifted to the 3' end). Such examples may have escaped the in silico prediction, but can you make any comment on this ?I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Prostate cancer (PCa) is still a significant health problem in UK and across the world. Though a small minority of patients progress to aggressive forms, the absolute number is quite significant due to the high incidence of PCa among men. Therefore, investigation of molecular mechanisms of PCa progression is very important and will hopefully unravel novel therapeutic targets. Alternative splicing (AS) has been shown to occur in over 94% of genes in humans. It is therefore a crucial level of gene regulation and not surprisingly involved in virtually every physiological and pathological process. AS de-regulation has been implicated in many diseases, including cancer and in particular PCa, and interestingly, many times it has been shown to drive cancer pathology independently of transcription. Since androgens are main players in PCa, the idea of analysing global changes in AS in response to androgens is very welcome to the field. The authors found 10 times more AS isoforms regulated by androgens than previously reported in data from cell culture, most of them occurring through alternative promoter mechanism. They have confirmed and validated part of these changes. They have also analysed the isoforms changes between adenocarcinoma and normal tissues as well as during progression through the Gleason stages of PCa. one issue in global analysis of splice isoforms is which ones are causal (ie maintain and aggravate the phenotype) and which ones are just associated with the pathological progression; while a full answer to this would need experimental evidence on each individual splicing event, could the authors discuss 1-2 examples, if possible, where the changes at protein level (either sequence or expression level of a particular isoform) would hypothetically have a causal role This is a very well thought and executed study, with may informative results. I have a suggestion for the discussion part:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Tuberculous meningitis (TBM) is the most severe form of TB with high rates of mortality and morbidity. Here we conduct RNA-sequencing on whole blood as well as on ventricular and lumbar cerebrospinal fluid (CSF) of pediatric patients treated for TBM. Differential transcript expression of TBM cases are compared with healthy controls in whole blood and with non-TB cerebral infection controls in CSF. Whole blood RNA-Seq analysis demonstrates a distinct immune response pattern in TBM, with significant increase in both canonical and non-canonical inflammasome activation and decrease in T-cell activation. In ventricular CSF, a significant enrichment associated with neuronal excitotoxicity and cerebral damage is detected in TBM. Finally, compartmental comparison in TBM indicates that the ventricular profile represents brain injury whereas the lumbar profile represents protein translation and cytokine signaling. Together, transcriptomic analysis shows that disease processes differ between the periphery and the central nervous system, and within brain compartments. Tuberculosis meningitis (TBM) is a severe form of TB with limited treatment options. Here, the authors perform RNA sequencing on whole blood and on ventricular and lumbar cerebrospinal fluid (CSF) samples from pediatric patients treated for TBM to characterize the immune response and tissue damage. Dissemination to the brain results in tuberculous meningitis (TBM), which is the most severe form of TB and leads to high rates of mortality and morbidity in both adults and children4. An important determinant of poor outcome is the host inflammatory response, which results in the formation of a thick exudate at the base of the brain. The exudate precipitates cerebral ischemia and infarction by causing vasculitis and occlusion of the major cerebral arteries and by disrupting the flow of cerebrospinal fluid (CSF) causing hydrocephalus and raised intracranial pressure. Understanding the cerebral inflammatory response, therefore, has been a focus of research into the pathophysiology and treatment of TBM.Tuberculosis remains a major contributor to the global disease burden7. While adjunctive therapeutic intervention with corticosteroids improves short-term mortality, it does not reduce morbidity and has no significant influence on immune mediator concentrations8. Furthermore, research on serial concentrations of biomarkers of brain injury and inflammation in pediatric TBM demonstrated that the inflammatory mediator concentrations decreased in all patients within 3–4 weeks after starting treatment. However, markers of brain tissue injury continued to increase in patients who died9. In combination, this evidence suggests that a further characterization of the host immune response and the consequent secondary brain injury mechanisms set in motion is critical, and could illuminate novel avenues for host-directed TBM treatment. In this study we investigate the transcriptome of children with TBM at the site of disease to elucidate key immune and injury-related pathways. Our previous research has demonstrated the significant differences in biomarker profiles between the periphery and site of disease, and between compartments within the central nervous system9. Therefore, we conduct transcriptomic analysis on blood, lumbar CSF, and ventricular CSF.Hitherto, studies investigating the immune response in TBM have focussed largely on immune mediators like cytokines and chemokines, but poor outcome inconsistently associates with the concentrations of these mediatorsn = 8), and liver TB in 5% (n = 1) of TBM patients. No drug resistance was detected. All patients had associated hydrocephalus (100%) and features on contrasted brain CT in keeping with possible TBM: basal enhancement was presented in all patients, tuberculomas were identified on admission or follow-up scans in 30% (n = 6) of patients, and infarcts were present on 80% (n = 16). The 12-month mortality rate was 15% (n = 3). The three sample types were not available in all patients; whole blood RNA was available in 15 of the 20 TBM patients and ventricular and lumbar CSF RNA were available in 12 of the 20 TBM patients.The complete patient demographics and clinical characteristics are summarized in Table n = 19). Reasons for elective surgery included intracranial pressure monitoring , dorsal rhizotomy , fatty filum terminale , cranioplasty , surgery for hydrocephalus , cranio-cervical junction surgery , and craniosynotosis . These patients did not have an evidence of CNS inflammation at the time of sample collection.Twenty-seven healthy blood controls were enrolled, three were excluded due to a positive Quantiferon result (IGRA-positive). The remaining 24 patients had a median age of 5 years (min–max: 0.3–12.9); 79% were boys (n = 6). Two patients had bacterial meningitis . Four patients had bacterial meningitis secondary to shunt infections due to methicillin-resistant Staphylococcus aureus (n = 1), Escherichia coli (n = 1), Staphylococcus epidermidis (n = 1) and Pseudomonas (n = 1). One patient had neurocysticercosis.The seven other infection (OI) controls had a median age of 1.1 years (min–max: 0.1–12.1), 86% male   or TBM stage all had no influence in the whole blood transcriptomes. Age had a minor impact on the transcriptional responses in both TBM cases and healthy controls . The effect of age is also minute when considered as an interactive covariate in differential expression analysis comparing the two patient groups, as 2166/2230 genes remained significant and the functional pathways identified remained unchanged.To better understand the cellular response in the brain, we profiled the global transcriptomes of ventricular CSF by sequencing. This was compared to the transcriptome of the non-TB OI controls. We did not directly compare the transcriptomic data from the CSF to those of the blood as the different library preparation used to accommodate for the low quantity (picogram range) of CSF RNA would compromise the compatibility of differential gene expression analysis. We analyzed ventricular CSF RNA-Seq data from 12 TBM cases (five definite and seven probable) and seven OI controls , definite-like probable TBM (n = 3), and OI-like probable TBM (n = 4). There was no transcriptional difference between definite TBM and definite-like probable cases, but significant transcriptional differences were consistently detected in 1815 genes (padj < 0.05), when comparing OI-like probables to definite TBM, as well as to definite-like probable cases . Differential gene expression analysis identified 389 significantly different genes , where 45/46 associated genes were upregulated  in ventricular CSF, subgroup analysis was performed on the lumbar CSF transcriptomes. Surprisingly, no transcriptional difference was detected when comparing OI-like probable cases to definite TBM or to definite-like probable cases Fig. . Other cIn this study we characterized the transcriptome of the whole blood, as well as two different sites of CSF , to investigate disease processes occurring due to TBM. The main findings indicated a compartmentalized immune response in the peripheral blood and a gene signature overrepresented by neural injury in the CSF, particularly in the ventricular CSF. This is the first comprehensive analysis to date of both blood and CSF transcriptomes in TBM.14 and in children15 have identified transcriptional signatures that could differentiate pulmonary TB from other infections, but whether these signatures can be applied for TBM remains to be investigated. A recent study used microarray to profile the whole blood transcriptomes of children with TBM at multiple time points and delineated reduced T-cell proliferation and immune responses over the course of disease progression16. While our study did not examine transcriptional changes over time, we identified a similar transcriptional reduction in peripheral T-cell responses when TBM cases were compared to IGRA-negative healthy controls. Intriguingly, a strongly upregulated innate immune response driven by inflammasome activation in whole blood was also identified. Our previous study on TBM-associated immune reconstitution inflammatory syndrome in adults also identified increased inflammasome signaling in the whole blood transcriptome and at protein level in the CSF17, further suggesting that inflammasome activation is a feature of TBM at the systemic level. In vitro and murine models of TB demonstrate that IFNγ and IFNγ-induced nitric oxide production are required to inhibit the NLRP3 inflammasome and the subsequent production of IL-1β to prevent neutrophil recruitment and tissue damage18. Thus, the lack of an effective T-cell response in pediatric TBM might be a contributing factor to the upregulated inflammasome activation.The pathogenesis of TBM is poorly understood due to the lack of an appropriate experimental model and the challenge of sampling the site of disease. Cohort studies in adults9. This is in keeping with the cerebral site of production of these markers and the known rostro-caudal decrement in brain-derived proteins as CSF flows from the brain to the spinal canal20. Consistent with these data the transcriptomic signature in ventricular CSF revealed significant enrichment associated with neural excitotoxicity predominantly driven by glutamate release, NMDA receptor binding and uptake, features also associated with other forms of brain injury. Glutamate is a key excitatory neurotransmitter with important roles in learning and memory21. TBM-induced infarction and ischemia could potentially stimulate glutamate release22, leading to the excessive binding of glutamate to NMDA receptors on neurons. This precipitates an influx of Ca2+ and other positive ions into the neuron, resulting in the production of calpains, caspases, endonucleases, and phospholipases, and begins a cascade of injury mechanisms23. These include mitochondrial damage, breakdown of the cell membrane and cytoskeleton, production of nitric oxide and free radicals, acidosis, and DNA fragmentation, subsequently leading to apoptotic or necrotic cell death25. Our transcriptomic data also showed upregulation of genes associated with nitric oxide, cytochrome c, brain injury proteins like myelin basic protein, and proteins associated with neurodegenerative disease, including tau, amyloid-beta and apolipoprotein E. Excessive glutamate and neuro-excitotoxicity are thought to contribute to brain injury and cell death in both acute brain injury conditions, such as traumatic brain injury (TBI) and epilepsy, and chronic brain disorders such as Alzheimer’s and Huntington’s disease28. Excitotoxicity may increase the risk of clinical and subclinical seizures in TBM, and of cortical spreading depression, a secondary injury mechanism known to occur in stroke. These electrophysiological events increase metabolic demand on the already ischemic brain. Our data reflect the clinical picture of brain infarction in our TBM cases demonstrated on imaging, and begin to illuminate various brain injury mechanisms set up by the inflammatory response and the consequent ischemia.Given the lack of access to brain tissues, we therefore utilized ventricular CSF as a proxy to study the cellular and immunological events in the brain. We have previously reported that ventricular CSF has significantly higher concentrations of cerebral injury markers relative to lumbar CSF24. The considerable damage and inflammation precipitated by TBM may initiate similar long-term processes and poor outcomes. The differences between cases and controls indicate that it is unlikely that these findings simply reflect hydrocephalus or the method of ventricular puncture.Validation and targeted investigation of these excitotoxic mechanisms could identify novel therapeutic agents to ameliorate the ongoing brain injury. Recent data in mild TBI suggest chronic low-level neuroinflammatory processes may be initiated by minor insults like concussion and lead eventually to neurodegenerative disease if they occur repeatedlyIntriguingly, on further examination of the excitotoxicity signature probable TBM cases differentiated into two subgroups: those with transcriptomic profiles more similar to the definite cases and those who were more closely related to OI controls. Posthoc analysis of their CT scans demonstrated differences between these two groups as well . Parents/guardians provided informed consent for all study patients. Children older than 7 years provided assent as per South African national guidelines.29. One patient was recruited from Tygerberg Hospital, Cape Town. All patients received standard antimycobacterial and adjunctive corticosteroid treatment30. Associated hydrocephalus was treated according to our institutional protocol31. Clinical data included admission signs and symptoms, demographics, death within 12 months, and radiological outcome, collected from admission and follow-up computed tomographic and/or magnetic resonance imaging brain scans. TBM disease severity was categorized using the refined British Medical Research Council criteria: Stage I: Glasgow Coma Score (GCS) 15 with no neurological deficit, Stage IIa: GCS 15 with neurological deficit/GCS13-14 with/without neurological deficit, Stage IIb: GCS 10–12 with/without neurological deficit, Stage III: GCS < 10 with/without neurological deficits33. Two control groups were enrolled; first, a group of patients undergoing elective neurosurgery for noninfectious conditions  from whom we collected blood; second, patients with meningitis or ventriculitis of non-TB origin (non-TB infection controls) from whom we collected blood and ventricular CSF. Since these patients required neurosurgical intervention with ventricular rather than lumbar puncture, lumbar CSF was not sampled. In those patients in whom lumbar punctures were performed, the small volumes of CSF obtained were prioritized for clinical imperatives.Patients treated for probable or definite TBM admitted to the Red Cross War Memorial Children’s Hospital, Cape Town, between July 2014 and February 2016 were recruited. TBM diagnosis was based on the consensus research definition, which combines clinical, radiological, and laboratory characteristicsBlood samples from cases and controls were collected during clinically indicated venepuncture on admission or at surgery. Lumbar CSF was collected from cases during routine lumbar punctures for diagnostic or treatment purposes. Ventricular CSF was collected from cases and non-TB infection controls during clinically indicated neurosurgical interventions including insertion of an external ventricular drain or ventriculoperitoneal shunt. All samples were collected before or within 48 h of the first dose of anti-tuberculous treatment. Where possible samples across compartments were time-linked. Healthy controls also had blood collected for QuantiFERON TB Gold (QIAGEN) analysis to ascertain TB exposure.Whole blood (1–2.7 ml) was collected in PAXgene Blood RNA Tubes (QIAGEN/BD Biosciences), and total RNA was extracted using the PAXgene Blood RNA Kit . QuantiFERON testing was conducted using the QuantiFERON ELISA kit (QIAGEN). CSF was prioritised for clinical investigations and the remaining fluid was stored in RNA preservation buffer at −80 °C until RNA extraction was performed. RNA was extracted using the Direct-zol™ RNA MiniPrep kit . The quantity and quality of the extracted RNA were measured by the Qubit fluorometer and the Caliper LabChip system, respectively. RNA libraries for whole blood RNA were constructed using the Ovation Human Blood RNA-Seq Library Systems  where ribosomal and globin RNA were removed according to the manufacturer’s protocol. RNA libraries for CSF RNA were constructed using the Ovation SoLo RNA-Seq system (NuGEN Technologies) due to the low quantity available. The final libraries were assessed using TapeStation 2200 System . All libraries were sequenced on Illumina Hi-Seq 4000 instrument with paired-end 100 cycle reactions.p-value (padj) for each gene per comparison. Gene Set Enrichment Analysis on the whole transcriptome was performed by preranking all genes to the Wald statistic and referenced to the Reactome biological pathway database. Functional canonical pathways and disease networks associated with the differentially expressed genes were identified using Ingenuity IPA (QIAGEN).The quality of the sequencing fastq files was analyzed using FastQC (v0.11.5) and poor quality samples were excluded from further differential gene expression analysis. Sequence reads were adapter- and quality-trimmed using Trimmomatic (v0.36) before aligning to the human genome (Ensembl GRCh38 build 88) using STAR aligner (v2.5.2a). Gene expression was quantified using RSEM (v.1.2.29), and differential gene expression analysis was performed using DESeq2 (v1.20.0) with default parameters for whole blood and with gene filtering (threshold ≥1) for CSF. DESeq2 uses a generalized linear model to estimate log2 fold change (log2FC) between comparison groups and the Benjamini-Hochberg false discovery rate was applied for multiple testing corrections, resulting in an adjusted The funders of this study were not involved in the study design, data collection, data analyses, data interpretation, or report preparation. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.Further information on research design is available in the Supplementary InformationReporting SummaryDescription of additional supplementary filesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10