From April 2013 to July 2014, we enrolled 442 symptomatic children with malaria, 442 matched controls, and all household members of both groups. Here, we evaluate genetic similarity between infected individuals using three indices: sharing of parasite haplotypes on binary and proportional scales and the L1 norm. Symptomatic children more commonly share haplotypes with their own household members. Furthermore, we observe robust temporal structuring of parasite genetic similarity and identify the unique molecular signature of an outbreak. These findings of both micro- and macro-scale organization of parasite populations might be harnessed to inform next-generation malaria control measures.Novel interventions that leverage the heterogeneity of parasite transmission are needed to achieve malaria elimination. To better understand spatial and temporal dynamics of transmission, we applied amplicon next-generation sequencing of two polymorphic gene regions ( et al. use amplicon next-generation sequencing of two P. falciparum polymorphic gene regions to investigate the genetic similarity of parasite populations across time and space in a pediatric cohort in Kenya. They identify both micro- and macro-scale structuring of malaria parasites in this high-transmission setting, which could inform future intervention strategies.Here, Nelson  This improvement is broadly associated with the adoption of core control measures, principally the use of long-lasting insecticide-treated bednets, improved case management with rapid diagnostic tests (RDTs), and treatment with artemisinin-combination therapies (ACTs). Yet in some areas such as Bungoma county in western Kenya, Plasmodium falciparum transmission has failed to decline in proportion to control efforts, underscoring the need for new strategies3. Owing to this, there is renewed interest in developing, testing, and deploying supplemental strategies to accelerate malaria elimination4, including enhanced surveillance, mass drug administration, and active community-based case detection3. Some of these strategies have been deployed in low/moderate transmission settings with variable success6, but have been less frequently trialed in high-transmission settings of sub-Saharan Africa. Apart from the operational challenges of such settings, a major impediment to the application of these tools is a limited understanding of the fine-scale heterogeneity in malaria risk and transmission. Gaps in knowledge include: the extent to which time and geographic space structure parasite populations, the introduction or propagation of novel parasite strains, and the ability to identify hosts related through discrete parasite transmission chains. A greater understanding of the dynamics of natural infections and their impact on parasite transmissibility could enable rational implementation of control measures to reduce the malaria disease burden in high-transmission settings.The global burden of malaria has decreased considerably: from 2000 until 2015, cases declined by 41% and malaria deaths fell by 62%7. However, P. falciparum molecular surveillance efforts have been hindered by: (1) the size and complexity of the parasite genome8, (2) the high prevalence of polygenomic infections in high-transmission areas10, and (3) the complex life cycle involving sexual recombination11. Collectively, these challenges limit the ability to define sequence identity of parasites in vivo, which is essential for identifying transmission between hosts. One potential solution is NGS of PCR amplicons, which enables resolution of parasite variants within a population into an array of genetically distinct haplotypes14. Using control parasite DNA mixtures, we16 and others13 have previously demonstrated that amplicon sequencing results in high-fidelity haplotype output with identity and frequency directly proportional to the input genetic material. The high sensitivity of this technique coupled with the ability to parse out multiple genotypes in polyclonal infection is highly appealing for the use of amplicon NGS data for malaria molecular epidemiology7.Next-generation sequencing (NGS) technologies may enable both local- and population-level tracking of parasite transmission2. Though entomology in this study was quite limited, we also identified clustering of larval sites and bloodfed anopheline mosquitoes in case households; however, these relatively weak associations suggest that vector proximity is not a primary driver of disease risk in this context.We previously conducted a case-control study of malaria in western Kenya in 2013–2014. Over a 15-month period, infected (case) and uninfected (control) index children along with all household members of both groups were enrolled and tested with malaria RDTs  and apical membrane antigen-1 (ama1). Subsequently, we apply three metrics to assess interhost parasite genetic similarity: binary haplotype sharing (any haplotypes in common), proportional haplotype sharing (percentage of haplotypes in common), and the L1 norm (sequence-based distance). We hypothesized that this genotyping approach coupled with metrics of genetic similarity would yield information regarding spatial and temporal scales of malaria transmission that might be harnessed to target malaria control measures. Specifically, in this study we predicted that parasite populations in case children (CC) would be more genetically similar to asymptomatically infected members of their own households than to infected members of other households, and that the overall likelihood of genetically similar haplotypes between any two sampled individuals would be inversely related to geographic and temporal distance.Here we employ parasites collected during this case-control study in western Kenya to interrogate malaria transmission across temporal and spatial scales. For this investigation, we utilize amplicon NGS of highly polymorphic P. falciparum. A total of 966 RDT+ infections were submitted for amplicon NGS of csp and ama1 loci  or ama1 haplotypes  , and 17.9% control household members  and ama1 . Across all successfully genotyped infections, the median read coverage was 13,369 for csp and 11,392 for ama1  and 33.9%  of genotyped infections  and 14 (ama1). Overall intrahost nucleotide diversity was high, dominated by nonsynonymous sequence polymorphisms  . However, there was no consistent difference between the MOI detected in CC vs. CHM within a single household .Most study participants had polygenomic infections: single haplotypes were detected in only 34.7%  appeared in only a single month  of each haplotype (PDH) between young children (≤5 years) and older children/adults (>5 years). However, we observed no consistent difference in haplotype prevalence between the ≤5 years and >5 years populations  often depends upon host age in areas of endemic transmissioncsp , which was statistically significant for binary sharing , proportional sharing , and the L1 norm   though a heterogenous L1 norm . Collectively, these findings indicate temporal structuring  of parasite populations as defined by all metrics of genetic similarity at both csp and ama1 loci.We subsequently employed binary sharing, proportional sharing, and the L1 norm, novel metrics which describe population-level genetic similarity, to examine t) Table . Analogoes Table . Howeveres Table . Overallcsp  and ama1 haplotypes . Similarly, proportional share scores were higher for CC with their origin CHM for both csp and ama1 . Finally, the L1 norm, which measures genetic distance between parasite populations, was statistically reduced for CC to origin CHM compared to URCHM at the csp  but not ama1 locus  compared to csp (120) and the resulting lower probability of observing exact matches.To investigate whether index cases and household members have similar parasite populations, and thus whether asymptomatic parasitemia among household members may be a risk factor for new clinical cases, we assessed genetic similarity between CC and asymptomatic CHMs from their household of origin. As a comparison, we also measured parasite genetic similarity between CC and members of an unrelated case household (URCHM), which was selected based on time of sampling . We evaluated CC:CHM and CC:URCHM genetic similarity using binary sharing, proportional sharing, and the L1 norm. Binary sharing was enhanced between CC and origin household CHM compared to binary sharing between CC and URCHM when computed using both cus Fig. . These rn = 38 households). To do so, we calculated aggregated csp and ama1 binary sharing, proportional sharing, and the L1 norm between each pairwise combination of CC and CHM, yielding 1444 genetic relatedness values. We surmised that if binary sharing, proportional sharing, and the L1 norm are highly predictive indicies, the calculated CC:CHM value should be greatest for the comparison of CC with their own household members thus accurately identifying the CC household of origin. Overall, binary sharing was the most predictive of the origin household of a CC, with maximal binary share score correctly predicting the CC origin household 18% of the time (7/38) compared with 16% (6/38) for proportional sharing and 11% (4/38) for the L1 norm . We also evaluated the ability of each sharing index to predict the time and geographic location of CC infections by comparing CC:CHM scores across time and space. All three genetic similarity metrics were highly predictive of CC temporal malaria acquisition, identifying the correct position in time of the CC ± 30 days  for approximately 50% of CCs , then calculated the temporal  is highly dependent upon temporal distance between CC   were significantly more common in CC than CHM during June 2013 (each p < 0.0001 by Fisher Exact test)   Fig.  during est) Fig. . We exam H1 Fig. . Likewis H8 Fig. . In totaH13 Fig. . Intrigu13) Fig. . In comp13) Fig. . Thus, bcsp and ama1 haplotypes largely occurred in CC during a 3-week period from 6/17/2013 to 7/8/2013, peaking during the week of June 24th , comprising more than 98% of the reads detected in an individual. Employing this definition, we identified a total of 29 outbreak cases and 48 non-outbreak cases among the 77 total CC between 5/6/2013 and 7/29/2013. We see a clear peak of genetically homogenous outbreak cases that accounts for nearly all cases during this 3-week period and is book-ended with endemic transmission of non-outbreak cases —a null result.Interestingly, we observe that this unique combination of 4th Fig. . We defises Fig. . Intriguses Fig. . The seeP. falciparum polymorphic gene targets in parasites collected from households in western Kenya to investigate the geographic and temporal structuring of parasite populations. Our analyses indicate that temporally proximate infections have enhanced genetic similarity, suggesting that parasite populations are at least partially structured by time. Furthermore, we report that children with malaria are more likely to share parasite haplotypes with asymptomatically infected members of their own household compared with members of time-matched households. Finally, we utilized temporal structuring of parasite populations to identify the genetic signature of an outbreak of parasite genotypes manifest as enrichment of specific parasite haplotype combinations during a single month in 2013. Collectively, our analysis identifies both micro- and macro-level organization of parasite populations, enhancing our understanding of malaria transmission heterogeneity.In this study, we utilized amplicon deep sequencing of two 24, we previously noted that CC in this cohort were 2.5 times more likely to have RDT+ household members than were control children2 indicating household-level hotspots of high-risk individuals. Herein, we extended this association using our parasite genotyping approach to quantify the degree of genetic similarity between infected individuals. For example, symptomatic children had a much higher median probability of sharing a csp haplotype with asymptomatic members of their own household (50%) than with those of a matched household , indicating that parasites infecting symptomatic children are more genetically similar to those in their own household than to the population at large  were exceedingly common throughout the study period and present in both CC and CHM during the majority of months, while other outbreak haplotypes (csp H48/H52 and ama1 H13) are rare and appear almost exclusively among CC from May–July 2013. One CC infected with all seven csp/ama1 outbreak haplotypes reported a travel history to another malaria-endemic region within the past 3 months, so we might hypothesize that rare haplotypes (csp H48/H54 and ama1 H13) were imported from outside the study area. The reason for the co-occurrence of this unique combination of haplotypes among symptomatic children is unclear, and two major questions remain unanswered by this investigation: (1) How did the outbreak spread nearly simultaneously across a relatively large geographic area and (2) why was this combination only detected among CC? The geographic co-occurrence of outbreak cases may be contingent upon unconventional and/or undescribed vector movement and biting behavior or cryptic human movements within the study area. Strain-specific immune response have been associated with protection from disease29—including those directed against a vaccine-elicited csp haplotype30—and therefore, it is possible that rare haplotypes represent new, antigenically diverse malaria strains to the study area (not previously encountered by CC) leading to enhanced clinical disease. Alternatively, these rare haplotypes could be genomically linked with novel virulence factors. Though we cannot discern the origin of vector transmission and/or cause of pathogenesis with any degree of certainty using our amplicon NGS data, the ability to detect and monitor genetically identical polyclonal infections over time demonstrates the power of this amplicon NGS sequencing approach for malaria molecular epidemiology.Notably, this is the first study to detect the genetic signature of an outbreak amidst endemic malaria transmission. While several investigations have recognized genetic homogeneity in small-scale outbreaks37, prior molecular epidemiology studies have generally failed to observe parasite population genetic structure  using a variety of genotyping methods, sampling schemes, and analytic tools43; one recent notable exception found a significant decay in genetic relatedness as a function of increasing distance using microsatellite genotyping of polygenomic infections, but reported from a low-transmission setting in Namibia44. In our high-transmission setting, we observed that parasite populations were structured (1) by household and (2) by time more than geography by the application of three novel metrics  to systematically interrogate the genetic similarity of polygenomic parasite populations present within individual hosts expressed as haplotypes of unlinked parasite genes csp and ama1. The concordant results from parallel analyses using these unlinked gene targets extends the credibility and utility of our novel metrics as tools. Two of these metrics, binary sharing (any shared haplotypes) and proportional sharing (percentage of shared haplotypes), express the genetic relationship between two infections as a function of the presence or absence of identical individual haplotypes within the pair. The premise of these metrics is that haplotypes that are identical by state represent parasites identical by descent; although this assumption would not be reasonable over large distances or time periods owing to a variety of biological and epidemiologic factors, the constrained temporal and spatial scales and dense sampling of our study render this a more plausible assumption. The third metric, the L1 norm, has been used in viral genomic epidemiology as a measure of the sequence-based genetic divergence of two pathogen populations45. The polygenomic nature of most infections in high-transmission settings has precluded the use of numerous traditional tools of population genetics, but the use of our metrics enabled us to exploit these polygenomic infections, by capturing and then linking diverse haplotypes in separate hosts. We suggest that binary and proportional sharing metrics produce highly similar results in our analyses, whereas the L1 norm results are somewhat distinct and heterogeneous  or ring-treatment  in an effort to mitigate foci of transmission. These programs are popular and literature supports their efficacy at identifying additional cases, though there are insufficient data regarding cost-effectiveness and the impact of transmission intensity or diagnostic test sensitivity for these methods to be fully embraced. This investigation provides direct evidence that clinically silent parasite transmission chains within households are an important risk factor (but not the exclusive source) of new infections, which supports the rationale for employing reactive strategies to interrupt household-level transmission. Yet, our data also suggest that parasite populations are structured more by time than space, and therefore that household-level interventions csp and ama1.The greatest limitation of this investigation is the lack of longitudinal sampling of cases/household members or longitudinal entomologic surveillance. We anticipate that the presence of multiple data points would enrich our understanding of parasite transmission dynamics, including the directionality and time scale of transmission, temporal fluctuations in haplotype frequency and parasite density, and the impact of parasite density upon the probability of onward transmission . Another limitation is the study protocol resulted in a dataset that is (1) biased towards a young age range (given the tendency for infected children to have higher parasite density) and (2) is dominated by CC+ direct household members (CC neighbors and community members were excluded apart from control households) and thus we could not test for fine-scale decay in sharing by distance or empirically define a distance threshold for the observed genetic sharing. Additionally, our analyses of haplotype sharing between polygenomic infections require acceptance of the assumption that parasites identical by descent can be inferred from haplotypes identical by state, and this can be undermined by convergent evolution on sequences by balancing selection and constrained haplotype frequencies across a dataset, which collectively produce identical matches by chance. These risks, however, were mitigated in our study owing to the fine temporal and spatial scales of sampling and the abundance of unique haplotypes at each locus; in addition, the concordant findings in analyses using haplotypes derived from unlinked gene targets further suggests that matching by chance was common. Finally, as with any study requiring PCR amplification and DNA sequencing, there is the potential for systematic error  to impact the experimental findings. We reduced the risk of systematic error by enforcing strict sequencing read quality criteria, using validated haplotype inference tools, and measuring effects using unlinked polymorphic targets in orthogonal analyses of Pf haplotypes are structured more clearly by time than space, but also that, at the household level, children with malaria share parasite genotypes with asymptomatically infected household members who may constitute a risk factor for childhood malaria. Subsequent longitudinal studies ought to employ repeated sampling of household members  as well as high-resolution entomologic surveillance. Such a study design would enable the discernment of how parasite populations change over time as well as the origin of symptomatic infections—are parasites associated with malaria disease transmitted from household members or is their origin external to the household? These definitive findings could have widespread implications for the next-generation of malaria control efforts to combat heterogeneous transmission and ultimately to eliminate malaria disease.In conclusion, this is the first study to develop and apply indices of genetic similarity between infected individuals to explore dynamics of parasite transmission at both household and population levels. Collectively, our data suggest that 2. Briefly, children admitted to the Webuye County Hospital with a diagnosis of malaria (confirmed by SD Bioline Pf HRP2 RDT) who resided within the six administrative sublocations immediately surrounding the hospital were eligible for enrollment as CC. Household members of CC were tested by RDT and provided a dried blood spot (DBS) within 1–7 days of enrolling the CC. At that time, CC were matched by age and village to an RDT-negative control child and all of the members of the control child’s household were similarly tested and provided a DBS. A household was defined in this study as all family members and individuals residing under a single shared roof. All household members of case and control children were RDT-tested and DBS obtained at a single point in time immediately following child enrollment in the study. While case households were matched to control based on geographic proximity, neighbors and community members residing in close proximity to the enrolled household were not necessarily tested or sampled. All DBS were stored at 4 °C with desiccant, then shipped to Duke University  for subsequent analysis. Consent was obtained from all participants prior to enrollment. The study protocol and consent procedures were reviewed and approved by the Moi University Institutional Research and Ethics Committee (IREC/2013/13) and the Duke University Institutional Review Board (Pro00044098).Participants were enrolled between 18-April-2013 and 5-June-2014 as part of a case-control studyGenomic DNA (gDNA) was extracted from each using a Chelex-100 protocol46 and reconstituted in 100 μL nuclease free water. We estimated parasite densities in all RDT-positive infections using a real-time PCR assay targeting the pfr364 motif in the parasite genome47. gDNA from each sample was tested in duplicate, and densities were estimated from standard curves on each reaction plate computed from amplification of a series of quantitative standards ranging from 1 to 2000 parasites/uL of whole blood.A single 6 mm diameter punch from each DBS was deposited into a unique well of a deep 96-well plates P. falciparum genes encoding circumsporozoite protein  and apical membrane antigen 1  were amplified in separate reactions from gDNA using primers that each included an identical overhang sequence , and nuclease-free water to a total reaction volume of 25 μL; cycling conditions were 95C x 3′ → (98C × 20s → 62C × 15s → 72C × 25s) × 35 → 72C × 1′. PCR1 products were used as the template for PCR2 reactions, which used forward and reverse primers that annealed to PCR1 overhang sequences and also contained a MiSeq adaptor and a unique 8-mer index sequence , and nuclease-free water to a total reaction volume of 25 μL; cycling conditions were 94C x 2′ → (94C × 15s → 72C × 10s → 68C × 30s) × 15 68C × 5′. PCR2 products were amplicons including requisite adaptors for MiSeq sequencing as well as unique combinations of two MiSeq indices to enable disaggregation of reads by sample after sequencing. Separate library pools were prepared for csp and ama1 targets by combining an equal volume of PCR2 products from each reaction. Libraries were purified and concentrated with ethanol, electrophoresed in a 2% agarose gel, gel-purified using QIAquick gel extraction kit (Qiagen), and subsequently cleaned using Ampure XP beads (Agencourt). Finally, csp and ama1 pools were combined in equimolar fashion into a single sequencing pool. The resulting single pool was divided between two MiSeq (v3 300-cycle PE) runs.Parasites were genotyped from each DBS obtained from RDT+ participants. Dual-indexed libraries for each target were prepared using a nested PCR strategy and then pooled for NGS on an Illumina MiSeq platform. Polymorphic segments of csp and ama1 haplotypes were inferred from Illumina sequence reads. First, using Trimmomatic48, CutAdapt49, and BBmap50, reads were mapped to the P. falciparum strain 3D7 reference sequences for csp or ama1, then primer sequences (and nucleotides directly adjacent) were trimmed from the sequences. For quality filtering of mapped reads, we used a sliding window to remove reads if the average Phred quality score over four adjacent nucleotides was <15. These quality-filtered reads were input into DADA2 (version 1.8) in order to join paired-end reads, call haplotypes, and remove chimeras17. Within DADA2, read quality was further enforced prior to haplotype inference using the Phred quality score for each read to model error frequency, removing reads with greater than the predicted errors. To ensure only high-quality haplotypes were included in the analysis dataset, we censored haplotypes supported by fewer than 100 reads or present in less than 1% of the data.Hn), which is a unitless measure of variability calculated asn is the number of possible nucleotides at each position , and p the frequency of the variant nucleotides being compared. The sum of Shannon entropy at each nucleotide position represents the composite entropy score for the full amplicon. Nucleotide diversity (π) was computed as the average distance between each possible pair of sequences using51L is the sequence length in nucleotides for π, N is the total number of reads per participant, dij is the number of nucleotide differences between haplotype i and j, and fi/fj is the number of reads belonging to haplotype i. πS and πN were calculated as the average ds and dN between pairs of haplotypes weighted by the haplotype’s abundance:dsij is dS between haplotype i and j sequences and dNij is dN between haplotype i and j sequences. π, πS, and πN were compared by Friedman test + posthoc Wilcoxon Signed-Rank test (two-tailed). Of note, csp amplicon sequence variability  are consistent with previous reports12.Variation across the amplicon was assessed by Shannon Entropy  was defined as the presence in any two sampled individuals of at least one identical haplotype. Proportional sharing  was defined as the proportion of identical haplotypes shared between any two sampled individuals (A and B):45N is the sequence length in nucleotides, n the number of possible nucleotides at each position , and pi/qi the frequencies of the variant nucleotides being compared. To calculate genetic similarity metrics between two groups, bootstrap iterations of each metric were performed upon a random sampling of pairs of individuals selected from each group (with replacement) and the average computed . L2 was not utilized for analysis in this study because L1 and L2 are directly proportional and nearly perfectly correlated at both csp  and ama1 . Binary/proportional share scores and the L1 norm were validated by assessing the mean values for each household (3+ members), sub-location, location, and the overall population for both csp and ama1 . These metrics were computed by iterative random sampling (with replacement) of individuals between months as described above. Likewise, we assessed spatial structure by calculating each genetic similarity metrics between each pairing of administrative locations  for a restricted time window (April–June 2013 or April–June 2014) by repetitive random sampling of individuals from each administrative location. A restricted time window was utilized to limit the impact of time as a confounding factor that might obscure spatial structuring of haplotypes. We compared metrics computed between pairs of months or pairs of administrative locations using a nonparametric Mann–Whitney U test, with a p-value <0.05 considered to be significant.To assess overall temporal structure of genetic similarity, we computed mean binary sharing, proportional sharing, and the L1 norm metrics between each pairing of study months  vector behavior in western Kenya, which occurs after 9 pm52 and thus renders the household as epicenter of parasite transmission; and (3) the absence in the very circumscribed study site (~100 km2) of other candidate features .We limited assessment of spatial structure to inter-household distance owing to: (1) prior mapping of larval sites around a subset of these households, which indicate that larval sites are numerous, small, and transientcsp and 45 households for ama1. We matched each of these households to one other household based on sampling date using a weighted scale . In the event of a tie matching score, we randomly selected a single household. All households were successfully matched to at least one other household ±30 days. Furthermore, alternative matching algorithms employing case child age and household geographic location were also trialed and produced similar results.We limited the assessment of genetic similarity between case child (CC) and CHM to the subset of households with parasite sequence data from three or more infected members such that CC sharing could be assessed against at least 2 CHM for each household. We enforced this constraint to make the analysis more conservative by mitigating the risk of sampling error resulting in spurious findings regarding genetic similarity between CC and a single household member. This subset consisted of 41 households for n = 41 for csp and n = 45 for ama1) we computed the genetic similarity between CC and CHM from their household of origin as well as CHM from the matched household. Each comparison relied upon random, iterative sampling of CHM as previously described. The outputs of this process were pairwise (between CC and CHM) estimates of the likelihood of CC parasite genetic similarity with members of their own household in comparison to members of a matched household. Finally, we compared haplotype genetic similarity metrics of CC with origin CHM to those of CC with matched household members by Wilcoxon Signed-Rank test (two-tailed).Next, for each CC  were calculated for the pairing of every case with household members from each case household. Values for csp and ama1 were normalized and combined into binary sharing, proportional sharing, and L1 norm composite scores. On the basis of these composite scores, we rank-ordered households for each case child. Subsequently, we computed the geographic and temporal distance between each case child and each rank-ordered household. Of note, the Fisher’s Exact test comparison of observed vs. expected frequency in Table We estimated the value of these genetic similarity metrics to predict temporal and geographic order in the dataset within the 38 households with n = 283 for csp yielding 40,044 pairwise comparisons; n = 288 for ama1 yielding 41,472 pairwise comparisons). We analyzed each pairwise estimate as a function of temporal distance  or geographic distance, (expressed in kilometers), and modeled the relationships between these metrics using locally estimated scatterplot smoothing (LOESS).We analyzed the genetic relatedness between infections in all passively detected CC by computing pairwise genetic similarity metrics for each unique pairing of infections (csp (n = 120) and ama1 (n = 180) haplotype by month between individuals ≤5 years vs. >5 years old, defined as age PDH:csp haplotype i during a given month in individuals ≤5 years and csp haplotype i during a given month in >5 years . Furthermore, we assessed haplotype prevalence in CC and in asymptomatic CHMs, and used these to calculate the PD of each haplotype as CC:CHM PDH:csp haplotype i during a given month in CC and csp haplotype i during a given month in CHM . We compared within each month the PDH for each haplotype using a Fisher’s Exact test (two-tailed); for these comparisons, we applied a Bonferroni correction for the alpha as p = 0.05/505 observations ~1e-4. We used these PDH values to identify haplotypes that were significantly over-represented in specific age groups or in CC compared to CHM. This investigation served as a basis to empirically define outbreak cases of parasites bearing either csp or ama1 haplotypes. We assessed spatial structure of outbreak cases vs. non-outbreak children using a purely spatial Bernoulli model in SaTScan (version 9.4.4)53.We computed the prevalence of each unique All statistical tests were two-tailed. Details of individual statistical tests are described in relevant sections throughout the methods.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Liquid biopsies of blood plasma cell free DNA can be used to monitor treatment response and potentially detect mutations that are present in resistant clones in metastatic cancer patients.APC/TP53/KRAS major clone responded quickly after 3 cycles of FOLFIRI + Bevacizumab. Retrospective exome sequencing of pre-chemotherapy and post-chemotherapy tissue samples including metastases confirmed that the APC/TP53/KRAS and other major clonal mutations  were present in all samples. After the last chemotherapy cycle, CT imaging, CEA and CA19–9 markers validated the cfDNA findings of treatment response. However, 5 weeks later, the tumour had rapidly progressed.In our non-interventional liquid biopsy study, a male patient in his fifties diagnosed with stage IV colorectal cancer and polytope liver metastases rapidly progressed after completing chemotherapy and deceased 8 months after diagnosis. Retrospective cell free DNA testing showed that the APC/TP53/KRAS triple-mutated patient, these driver genes should be tested and monitored in a more in-depth manner in future patients. Patients with metastatic disease should be monitored more closely during and after chemotherapy, ideally using cfDNA.As FOLFIRI+Bevacizumab has recently also been associated with sustained complete remission in a  Blood plasma “liquid biopsy” from a cancer patient and the analysis of circulating tumour DNA (ctDNA) enables the diversity of the mutational patterns to be monitored over the course of disease at serial timepoints, giving new clinically actionable insights into the therapeutic effectivity. We here report on a case that illustrates how treatment response could be detected early from two blood samples. This case comes from a large ongoing exploratory study whose results were not used for treatment intervention.In colorectal cancer patients diagnosed with organ metastases, systemic therapy with chemotherapy and targeted antibodies or inhibitors is regularly based on the molecular characterization of the tumour . UsuallyWe report on a male patient in his fifties of North European ancestry with stage IV colorectal cancer and no known familial history of cancer. The patient had not participated in colon cancer screening tests. Clinical symptoms were unexplained weight loss for a period of six months before diagnosis. Initial diagnosis then presented a primarily metastasized adenocarcinoma of the cecum and bilobular hepatic metastases. Due to polytope bilobular liver metastasis surgical resection was not indicated, and he was admitted to a palliative chemotherapy (CTX). Figure The patient gave informed written consent for serial blood sample collection for biomarker analysis and the study was approved by Kiel University’s medical faculty ethics board (#A110/99). For analysis of ctDNA, we obtained a pre-treatment blood sample in week 2 and a mid-treatment blood sample in week 10, after three cycles of chemotherapy. The patient opted out of further blood sampling when he progressed. The blood samples were collected in Streck cfDNA BCT tubes from which plasma was centrifuged according to the manufacturer’s recommendations and stored at − 20 °C until DNA isolation. The plasma was thawed at room temperature. DNA was isolated from the plasma using the PerkinElmer NEXTprep-Mag cfDNA kit according to the manufacturer’s protocol. Ultra-deep sequencing of the ctDNA was performed on Illumina NextSeq 500 with 2x150bp reads using the PANCeq pan-cancer panel [For analysis of tumour tissue mutations, we obtained DNA from a pre-treatment colonoscopy biopsy from the primary tumour in the cecum in week 0 and four tissue samples from an emergency hemicolectomy in week 19 . The tissue samples were obtained from formalin-fixed paraffin embedded samples after HE staining and histological identification and marking of cancer cell regions by the pathological laboratory. Tumour DNA was isolated from the tissue using the RecoverAll™ Total Nucleic Acid Isolation Kit for FFPE (ThermoFisher Scientific Inc) according to the manufacturer’s protocol. Exome sequencing of the tissue DNA samples, and a patient-matched blood buffy coat DNA sample was performed on Illumina NovaSeq 6000 with 2x150bp reads using the IDT xGen Exome Research Panel  according to the manufacturers’ protocols. Sequence data analysis was performed with GenSearchNGS (Phenosystems S.A.), Alamut Visual (Interactive Biosoftware), IGV and pibase –5.KRAS mutation p.G12D; no mutation in exon 15 of BRAF; MSI stable. Our blood sample from week 2 revealed the KRAS mutation and four more major tumour mutations in the following genes (see Table APC (in 49% of sequences), TP53 (39%), KRAS (32%), THSD7B (20%), and a copy number amplification of a chromosomal segment on chromosome 4 containing FBXW7. The copy number amplification was deduced from a genomic stretch of germline polymorphisms with near-identical allele frequencies of around 0.25  commenced shortly after initial diagnosis. After three cycles of chemotherapy, the blood sample from week 10 revealed major changes in the tumour allele frequencies: TP53 (6%), APC (5.5%), KRAS (4%), and THSD7B (3%). The amplification of the chromosome segment with FBXW7 was not clearly detectable any longer in the cfDNA . After six cycles of chemotherapy, a re-staging by CT-scan was performed in week 15 and suggested a stable disease of the primary tumour and showed a regression of the liver metastasis  confirmed that the FBXW7 amplification was no longer clearly detectable in the cecum, lymph node, or peritoneal metastasis, but possibly in the pericolonic metastasis sample. The major clonal mutations detected in the pre-treatment biopsy remained conserved in all post-treatment samples, with no new major clonal mutations detected .Additional file 1 Table S1. Allele frequencies of germline polymorphims on chromosome 4, showing copy number amplifications in the pre-chemo plasma cfDNA.Additional file 2 Table S2. Somatic mutations detected using exome sequencing of cancer tissue samples versus blood bufy coat normal DNA.


This study finds 97 variants which alter the odds of developing non-specific mouth ulcers and replicate these in an independent cohort  ; P = 4.4e−483). Additional effect estimates from three independent cohorts with more specific phenotyping and specific study characteristics support many of these findings. In silico functional analyses provide evidence for a role of T cell regulation in the aetiology of mouth ulcers. These results provide novel insight into the pathogenesis of a common, important condition.Mouth ulcers are the most common ulcerative condition and encompass several clinical diagnoses, including recurrent aphthous stomatitis (RAS). Despite previous evidence for heritability, it is not clear which specific genetic loci are implicated in RAS. In this genome-wide association study ( H1 responses.Oral ulcerations are sores of the mucous membrane of the mouth and highly prevalent in the population. Here, in a genome-wide association study, the authors identify 97 loci associated with mouth ulcers highlighting genes involved in T cell-mediated immunity and T Depending on context, ulcers in the mouth are described as mouth ulcers or canker sores, both of which are descriptive terms rather than clinical diagnoses. In this article, mouth ulcers is used as an umbrella term describing the spectrum of clinical presentation.Oral ulceration is the most common ulcerative condition in humans, affecting up to 25% of young adults3, systemic lupus erythematosus5 and are considered a diagnostic feature of Behçet’s disease, an inflammatory disorder of blood vessels that causes ulceration of the mouth, eyes and genitals6.To date, many causes of mouth ulcers are recognised including mucosal trauma and a range of autoimmune and inflammatory conditions. For example, mouth ulcers are common in patients with ulcerative colitis and Crohn’s disease9. First line management strategies for RAS reduce the severity of ulceration without preventing recurrence10. Second line management strategies involve topical or systemic use of potent and non-specific immunomodulatory drugs including thalidomide and dapsone, exposing patients to a range of side effects12. Thus, there is unmet need for a wider range of therapeutic options in the management of RAS.Many people experience recurrent mouth ulcers which cannot be attributed to systemic disease or obvious oral trauma. Here, the clinical diagnosis of recurrent aphthous stomatitis (RAS) is used, referring to a group of closely related conditions of uncertain aetiology, whose defining feature is the presence of clinically characteristic oral ulcers, which are painful and associated with impaired quality of life13. Regardless of the initial trigger, it is believed that immune regulation plays a pivotal role in mediating tissue damage and the clinical presentation of RAS1. Susceptible individuals experience focal infiltration of the oral mucosa by monocytes and T lymphocytes deep to the basal membrane, followed by loss of superficial mucosa and a proliferative healing phase15.In part, the lack of satisfactory management for RAS reflects uncertainty in the exact aetiology. A number of predisposing factors have been reported including vitamin or haematinic deficiency, chemicals such as sodium laurel sulphate, mechanical trauma, stress and anxiety or infection with bacteria or viruses17 yet to date the genetic basis of this susceptibility remains poorly characterised. Previous candidate gene association studies have investigated variation in the region of genes encoding key cytokines , with varied results21. Genome-wide association studies (GWAS) have the potential to identify genetic variants associated with both susceptibility to initial triggers and the immune reaction that leads to the tissue damage and ulcer formation.Family-based studies support a role of genetic susceptibly in the aetiology of RAS22. The study found some evidence for an association between the vascular endothelial growth factor (VEGF) pathway and the oral ulcer sub-phenotype. It is not clear how these findings relate to the general population and there are no previous conventional genome-wide association studies for RAS or mouth ulcers.A recent study used genome-wide data to look for associations between gene pathways and specific sub-phenotypic features (including mouth ulcers) in a case series of patients with systemic lupus erythematosusThere is therefore a need to undertake genome-wide analysis for RAS, but specific measures are unavailable in large cohorts. The most appropriate strategy given available data, is to use large collections to identify and replicate associated genetic variants in a well-powered GWAS of self-reported non-specific mouth ulcers and then validate the effects of these variants in smaller collections with more clinically relevant RAS-specific measures. Given that RAS is nested within mouth ulcers, the inclusion of other causes of ulcers would introduce, at worst, noise into the analysis and, at best, enhance it by highlighting mechanisms which are relevant to oral mucosal breakdown irrespective of trigger.This genome-wide association study identifies 97 variants which alter the odds of developing non-specific mouth ulcers and replicates them in an independent cohort. In silico functional analyses provide evidence for a role of T cell regulation in the aetiology of mouth ulcers. These results provide novel insight into the pathogenesis of a common, important condition.In UK Biobank (UKBB) participants with data on mouth ulcers, the mean age at questionnaire completion was 56.7 years , 54.2% of participants were female and 10.2% of participants reported having mouth ulcers within the last year. In research participants from the personal genetics company 23andMe, Inc. with data on canker sores, 67.8% were over 45 years old, 59.2% of participants were female and 72.4% of participants reported ever having canker sores. In the QIMR Berghofer Medical Research Institute (QIMR) Over 50s (Aged) study (AG), which examined aging and age-related disease in twin pairs from the Australian Twin Registry, the mean age was 61.2 years . A large proportion of participants were female (74.1%) and the proportion reporting a history of mouth ulcers was 18.7%. Two other studies exclusively or partially included younger participants, the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK population-based birth cohort , and QIMR Melanocytic Naevi in Adolescent Twins (TW) study, which primarily examined melanotic naevae in twin pairs ; 53.8% females). In these studies, the proportion reporting ulcers was much higher  24, heritability was estimated at 8.7% . Genomic inflation factor (lambda GC) was estimated at 1.20. The intercept term from univariate LDSR was 1.03  suggesting that most inflation in lambda GC was attributable to polygenicity rather than bias.The primary genome-wide analysis was undertaken in UK Biobank. At an aggregate, genome-wide level there was evidence for a genetic contribution to mouth ulcers, with heritability estimated at 8.2%  under an infinitesimal model implemented in linkage disequilibrium score regression (LDSR)P < 5e−8) with mouth ulcers was seen at 7127 single variants  in PLINK, these formed 97 approximately independent lead variants.After final quality control (QC), 9,851,866 genetic variants were included in GWAS. Evidence for genome-wide association  were near the age of highest RAS prevalence. In the ALSPAC study all participants were in this high RAS prevalence age range. Twenty-four of the 97 lead variants showed consistent effect direction across all phenotypes from the three independent collections (Supplementary Data ILAS1-AS1 ~69 kb 5′ of IL12A on chromosome 3. This variant conferred large effects on the odds of mouth ulcers (odds ratio (OR) 0.72 per C allele, 95% CI: 0.71, 0.73; effect allele frequency (EAF) 0.89; χ2 test P = 4.4e−483). Complementary evidence for a protective effect of the C allele was seen in all three lookup cohorts  , rs11928736 within SCHIP1 , and rs55667203 within RP11-432B6.3   Table . After cIL10, showed the second strongest evidence for association after meta-analysis, conferring a large effect on mouth ulcers   Table .CCR3 showed strong evidence for a moderate effect on mouth ulcers  (Table CCR3 and rs34390431 near CCRL2) also showed strong evidence for an association with mouth ulcers after clumping  Table . AdditioIFNGR1 gene .Other associated common variants showed more modest effects on mouth ulcers. For example, rs7749390 an intronic variant in the ne Table , Fig. 2d5C Table , showed HLA-B) and the odds of developing mouth ulcers . The estimated effect size was moderate and showed directional consistency in all three lookup cohorts  there was very strong evidence for association between rs2523589  that have similar predicted functions more often than expected by chance. This analysis suggested genes encoding chemokines or chemokine receptors were plausible candidates at many associated loci, for example suggesting IL12A at the lead associated chr3:159483176-160796695 locus and IL12B at the chr5:158741791–158757895 locus. In loci containing multiple genes with related functions, several potential candidates were identified; for example, association at chr3:45864808–46621589 produced a number of candidates including CCR1, CCR2, CCRL2, CCR3, and CCR5 among others . Strong evidence for enrichment in several T cell regulatory gene sets was observed, for example, GO:0046632 , GO:0046634 , GO:200514 , GO:0002286 , GO;0042098  and GO:0045580  amongst others   1 cells and Th2 cells. These results suggest active gene expression occurs near associated variants in a tissue-specific manner 27 tissues and then uses full GWAS summary statistics to test for associations between these predicted expression levels and phenotype. Results were then assimilated using the S-MulTiXcan28 method, which integrates information from multiple tissue-specific predictions to improve statistical power. In total, 25,839 gene transcripts were tested for association, of which 244 transcripts passed a Bonferroni-corrected multiple testing threshold (P < 1.9e−06). The strongest evidence for association with mouth ulcers across all tissues was at IL12A mirroring the single variant results, with an increase in expression predicted to increase the odds of mouth ulcers (Z-test P = 2.23e−103). Other single variant results were mirrored in the results of this analysis with increased expression of SCHIP1 increasing the odds of mouth ulcers and IL10 decreasing the odds of mouth ulcers . The results for all genes are shown in Fig. Tests for association between ulcers and predicted gene expression were performed using S-PrediXcan29. Genetic correlation results were available for 222 traits, of which two passed a Bonferroni corrected P-value threshold of P < 2.3e−04. These were neuroticism  and depressive symptoms  . Additionally, it shows that genetic correlation between mouth ulcers and these traits is evenly distributed across the genome, without peaks in genetic correlation corresponding to peaks in local heritability of either mouth ulcers or these two traits . This identified 24 haplotypes which were associated with mouth ulcers at a Bonferroni-corrected P-value threshold of 0.05. The most robust finding was DRB1*0103, an uncommon haplotype  which was associated with markedly increased odds of mouth ulcers in a fully adjusted logistic regression model   in the predictions, up to 0.37% of variation in mouth ulcer severity (n = 2442) and 0.86% in mouth ulcer case status (n = 3562) could be accounted for using scores trained in UK Biobank. While scores trained only from loci meeting genome-wide significance in UKB accounted for 0.73% of variation in mouth ulcer severity and 0.21% in mouth ulcer case status. . Using Genome-wide Complex Trait Analysisrity n = 442 and 0To assess whether associated loci might represent targets for repurposed drug interventions we examined the Open Targets database for pharmacological interventions which might recapitulate the effects of naturally occurring genetic variation. Of the 244 gene transcripts that passed Bonferroni correction in S-PrediXcan, 27 were not recognised by the platform. As the platform limits the number of genes to 200, the 17 with the weakest evidence for association in S-PrediXcan were not included in the model. Fifty-two drugs were identified as potential targets . Effect estimates in samples with RAS-specific phenotypes provide further evidence that these variants are associated with this specific type of oral ulceration. These variants with large effects are closely related to biological mechanisms thought to be relevant to the formation of mouth ulcers. The remainder of the heritability is likely driven by indirect effects of large numbers of variants with modest effects on the odds of developing mouth ulcers. Given these genetic variants, which act indirectly, likely also contribute to a wide range of distal health-related phenotypes, it is perhaps unsurprising that neuroticism and depressive symptoms show detectable genetic correlation with mouth ulcers in both the LDSR and rho-HESS analyses. The view that this correlation is driven by non-specific associations across the genome, rather than effects at lead-associated variants, is supported by the location specific genetic correlation performed in rho-HESS.This large-scale genome-wide association study used a non-specific measure of mouth ulcers finding that, in common with studies of specific ulcer types, mouth ulcers are partly heritableThe variants with larger effects on the odds of mouth ulcers are likely to be clinically informative. Many are located in or near genes or are enriched in pathways relating to T cell immunity, and tend to impose a Th1-type immune response, a biologically plausible mechanism that supports previous literature and resonates with clinical practice. Complementary evidence of these variants having true causal associations with RAS is provided by the consistent effect directions seen for many of them in the three independent look-up cohorts with more specific phenotypes.PRSs, generated using variants selected with a range of significance thresholds, explained only a small amount of variance in the ulcer phenotypes. At present, these PRSs are unlikely to be a clinically useful tool in predicting which patients are at risk of developing mouth ulcers or in predicting severity of mouth ulcers.HLA-DRB1 which encodes the beta chain of the HLA-DR heterodimer, forming a ligand for the T cell receptor.Association in the HLA region at the single-SNP level was recapitulated by haplotype analysis, which identified multiple HLA haplotypes which are associated with odds of mouth ulcers. The most striking finding was an association between the DRB1*0103 haplotype and increased odds of mouth ulcers. DRB1*0103 is an uncommon haplotype of 31. Genetic variants at IL12A, IL10, STAT4, RIPK2, IRF8, and CEBPB-PTPN1 which have been reported in previous GWAS for Behçet’s disease34, are in high LD and have consistent effect direction with those reported here. This raises the possibility of a similar mechanism leading to clinical presentation with mouth ulcers in both conditions. Elsewhere in the literature there is striking overlap with the genetics of coeliac disease, where T cell-mediated responses are also believed to be important35. In particular, rs17810546  was reported as early as 2008 to have large effects on coeliac disease36, a finding which has since been validated37, whilst CCR3 has been reported more recently38. The HLA-DRB1*0103 haplotype has previously been reported as associated with ulcerative colitis40 and both Crohn’s and ulcerative colitis41 in candidate gene association studies. It is possible that the overlap in lead variants between these diseases is driven by specific tag variants flagging particular biological events affecting auto-immune traits of the digestive system.There are traits that commonly present with similar clinical symptoms to mouth ulcers, and the loci identified in this study show commonality with previous GWAS of these traits. Behçet’s disease is thought to relate to inappropriate T-cell-mediated inflammatory response and presents clinically with mouth ulcers among other features44, bacterial45 and other changes in the oral microbiome46 have been suggested as acquired risk factors for mouth ulcers. It is possible that the genetic loci identified in this study relate to mouth ulcers through regulation of the host microbiome; a similar view has recently been proposed for both Behçet’s and coeliac disease48. Hematinic status, especially deficiencies of folate, vitamin B12, ferritin or haemoglobin are thought to be risk factors for mouth ulcers49 and these deficiencies might occur secondary to genetically determined inflammatory or immune states, such as pernicious anaemia or coeliac disease.Immune regulatory loci identified here may influence the susceptibility of infective or non-infective risk factors for mouth ulcers. A range of viral+ T cell populations within the oral mucosa following minor triggers, leading to tissue damage and clinical manifestation as oral ulceration50. These upstream and downstream processes are not mutually exclusive and future research may wish to examine the effect of specific genetic loci across strata of potential risk factors to gain further insight into the aetiology.Loci identified in the present study may also have an effect at a tissue response level, where the cellular response to minor oral trauma is either proportionate and leads to resolution or disproportionate, leading to the clinical experience of mouth ulcers. One current view is that dysregulation of local cell-mediated response leads to an inappropriate focal accumulation of CD8A limitation of this study is that the presence or absence of mouth ulcers was inferred from questionnaire data rather than clinical examination. This is a necessary limitation as clinical oral examination data were not available and because the short duration and intermittent nature of mouth ulcers means they are often not visible on clinical examination even for affected individuals. In common with any questionnaire-derived data this may lead to some misclassification. For example, the phenotypes used in this study do not distinguish between RAS  and other causes of mouth ulcers. For the most part we believe non-RAS ulceration (such as traumatic ulceration) will be uncorrelated with genotype, and will therefore bias single SNP results and heritability estimates towards the null rather than generating false positive findings. Participants with Behçet’s disease or ulcerative colitis may report mouth ulcers which are secondary to their underlying diagnosis which could lead to false positive findings, but these conditions are uncommon compared to RAS. There may also be participants who have a genetic predisposition to over-report or under-report their symptoms which would mean misclassification is correlated with genotype. As discussed above these non-specific associations across the genome are anticipated to have small effects and are therefore considered unlikely to influence the lead-variant results but may bias the heritability estimates away from the null.Mouth ulcers become less common with age and there may be a genuine shift in aetiology with time. This was reflected in the different samples; the studies which include younger participants (ALSPAC and TW) had much higher mouth ulcer prevalence compared to the studies with older participants (UK Biobank and AG). A major motivation for the use of the look-up collections was to assess the effects of the lead genetic variants on more RAS-specific phenotypes and in studies with younger participants. However, a limitation of this strategy is the wide confidence intervals for effect estimates in each of the three smaller lookup cohorts, meaning that only variants with larger effects are expected to exclude the null and the heterogeneity across the three studies which precludes the use of meta-analysis.51) while others such as Ustekinumab target IL12A and are licenced for several diseases , and are being repurposed for other immune diseases such as systemic lupus erythematosus55. This vignette may help illustrate how the availability of GWAS results for mouth ulcers could facilitate repurposing of existing drug interventions or the development of novel, specific interventions for mouth ulcers.The Open Targets analysis identified 52 drugs which might recapitulate the effects of naturally occurring genetic variation. Some of these agents are tumour necrosis factor targets and are licenced for immune-related diseases, such as rheumatoid arthritis  were replicated in 23andMe. All variants which showed directional consistency across the two cohorts were further explored in three independent resources with more specific mouth ulcer phenotypes and genetic data; namely the ALSPAC, the QIMR Over 50s (Aged) study (AG) and the QIMR Melanocytic Naevi in Adolescent Twins (TW) study.A GWAS for mouth ulcers was performed within UK Biobank. Variants passing a conventional threshold for genome-wide significance 57. In the baseline questionnaire participants were asked to supply information about their oral health. The question stem was: Do you have any of the following? (You can select more than one answer). The possible answers included mouth ulcers and participants were prompted to answer this question thinking about the past year, if they pressed the help button. Participants who selected this answer were coded as cases. Participants who did not select this answer were coded as controls. Participants who chose the option for prefer not to answer, or did not complete the questionnaire session were coded as missing and not included in further analysis. UK Biobank received ethical approval from the North West Multi-centre Research Ethics Committee (REC reference for UK Biobank is 11/NW/0382).UK Biobank is a population-based health research resource consisting of ~500,000 people, aged between 38 and 73 years, who were recruited between the years 2006 and 2010 from across the UK58. Individuals included in the analyses provided informed consent and answered surveys online in accordance with the 23andMe human subjects protocol, which was reviewed and approved by Ethical and Independent Review Services, a private institutional review board (http://www.eandireview.com). Mouth ulcer cases were defined as those who answered yes to the question: Have you ever had a canker sore (an open sore on the soft tissue inside the mouth)? Those who answered no were considered controls, and those who responded don’t know were not included in the analysis.23andMe Inc. is a personal genomics company that provides genotype and health-related information to customers59. Follow up has included clinical assessment and questionnaires and is ongoing. Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. Study children were asked to complete questionnaires about oral health at age 23.9 years. Participants were asked if they had ever had mouth ulcers . An ever ulcers phenotype was generated with any answer of Yes,… used to define case status and No to define controls. Those who did not answer the question were set to missing. Study data were collected and managed using REDCap electronic data capture tools60.The ALSPAC longitudinal birth cohort recruited pregnant women living near Bristol, UK with an estimated delivery date between 1991 and 1992. There were 15,247 pregnancies resulting in 14,973 live birthsThe QIMR Over 50s (AG) study was initiated in 1992–1993 to understand the role of genetics in healthy aging and age-related disease. AG recruited twin pairs from the Australian Twin Register who were over 50 years of age. This study was approved by the QIMR Berghofer Human Research Ethics Committee (HREC reference number P1204). Participants were asked whether they had mouth ulcers now/previously/both or never. This was used to derive case control status for ever or never having ulcers.The QIMR melanocytic naevi in adolescent twins (TW) study was established in 1992 to investigate melanotic naevae. Twin pairs aged around 12 years of age were recruited from schools in Brisbane and the surrounding area in Queensland, Australia. This study was approved by the QIMR Berghofer Human Research Ethics Committee (HREC reference number P193). All twins and most parents donated blood for DNA extraction and completed a questionnaire. Children and mothers of nuclear families were asked independently about mouth ulcers. Mothers also answered a questionnaire on mouth ulcers on behalf of the entire family, with each family member given a score for ulcers which was scaled to allow comparison with the studies with binary ulcer phenotypes . Pictures of RAS were used to help participants identify whether they had this specific type of oral ulceration. These ulcer questions were treated as a severity score preserving all four possible responses. Severity scores from both parents and children were combined into a mixed cohort.n = 1120). Mouth ulcer data in the TW study were collected from 1992 until 2016 (n = 2442).Oral health phenotypic data collection in the ALSPAC study was conducted between November 2015 and September 2016, the AG study was conducted between 1992 and 1993  reference panels using IMPUTE2 algorithms64. A further QC protocol was then applied at the Wellcome Trust Centre for Human Genetics prior to release . The analyses presented here were restricted to autosomal variants using a graded filtering so that rarer genetic variants are required to have a higher imputation INFO score . Monomorphic and uncommon variants with MAF < 0.1% were removed. In addition, all variants not included in the HRC site list were removed. Individuals with sex-mismatch (derived by comparing genetic sex and reported sex) or individuals with sex-chromosome aneuploidy were excluded from the analysis (n = 814). A k-means cluster analysis was performed with four clusters using the first four principal components provided by UK Biobank in the statistical software environment R65. The current analysis includes the largest cluster from this analysis , of whom 461,106 individuals  had non-missing phenotype data and were included in GWAS66.The UK Biobank genotype data (July 2017 release) contains 488,377 successfully genotyped samples. 49,979 individuals were genotyped using the UK BiLEVE array and 438,398 using the UK Biobank axiom array. Pre-imputation QC, phasing and imputation were completedSensitivity analyses of the HLA region used imputed HLA haplotypes provided by UK Biobank. As this analysis was performed using logistic regression rather than mixed models, analysis was restricted to individuals of white British ancestry. In addition, one individual from each pair of closely related (third degree or closer) individuals was removed until no related pairs remained. Following these exclusion criteria, 337,115 individuals had genotype data available, of which 336,038 had non-missing phenotype data and were included in logistic regression models.67. Briefly, samples were genotyped on a 23andMe custom genotyping array platform  with a minimum call rate of 98.5%. Missing participant genotype date was imputed using the UK10K and 1000 genomes combined reference panel. Analysis was conducted on a maximal set of unrelated individuals.In 23andMe, DNA extraction and genotyping were performed on saliva samples by CLIA-certified and CAP-accredited clinical laboratories of Laboratory Corporation of America. QC, imputation and genome-wide analysis were performed by 23andMen > 10,000). Genotyping was conducted on the Illumina HumanHap550 quad chip for children and Illumina human660W quad array for mothers. Prior to imputation, samples with >3% missingness, indeterminate heterozygosity, extreme autosomal heterozygosity or which clustered outside the CEU HapMap2 population using multidimensional scaling were removed. In addition, variants with minor allele frequency of <1%, call rate of <95% or violations of Hardy–Weinberg equilibrium (P < 5e−07) were removed. Samples and variants passing these QC measures were carried forward to a joint phasing stage, prior to imputation to the HRC reference panel using the Michigan imputation server .Genetic data for the ALSPAC participants has been collected . Most participants were genotyped on the Illumina Human610-Quadv1_B (n = 1942) or HumanCoreExome-12v1-0_C (n = 1019) arrays; genotyping on a small number of additional individuals was conducted on the Illumina HumanOmni25M-8v1-1_B (n = 144), 317 K (n = 247), HumanCNV370 (n = 91), Human660W-Quad_v1_C (n = 8), HumanOmniExpress-12v1-1_A (n = 21) and PsychArray-B (n = 92) platforms. Genotype data from all assays was jointly imputed using HRC reference panel (r1.1)70.Genetic data for the participants of AG and TW with mouth ulcer data have been collected as part of a larger project by QIMR that comprises multiple waves of genotyping. In total, 3562 participants had phenotype and genotype data available  were transformed to log odds ratios and their corresponding 95% confidence intervals on the liability scale using a Taylor transformation expansion series75. Other methods for transforming betas to odds ratios that take into account allele frequency have been suggested. Both methods for deriving transformed OR showed excellent concordance 76 to identify approximately independent associated variants (options --clump-kb 10000 –clump-p1 5e-08 --clump-p2 1 --clump-r2 0.1), using an independent sample of HRC imputed genetic data to estimate LD. These variants were selected for replication in 23andMe.Associated loci that passed the genome-wide association threshold were clumped based on LD values using PLINK (version 1.9)77.In 23andMe, associations between the UK Biobank genome-wide associated variants and the mouth ulcer phenotype were assessed using linear regression assuming an additive model and using a 23andMe internally developed pipeline. Replication was considered successful if directionality was consistent across the two studies. Where replication was successful the estimates from UK Biobank and 23andMe were meta-analysed using the meta package in R72 as described above for UK Biobank. For AG and TW this was implemented in RareMetalWorker78. As with the GWAS association statistics, the results were transformed using a Taylor transformation expansion series to express log odds ratios on the liability scale. Variant lookups were in additional collections with better phenotypic data. These lookups aimed to provide conducted to provide complementary evidence for the effects sizes of the lead variants from independent sources of data. Insufficient power in individual studies and heterogeneity in phenotype across studies, which precludes meta-analysis, mean they were not used as further replication sets. Therefore, all variants identified in UK Biobank and followed up in 23andMe were taken through to in-silico functional analyses to avoid incorrectly disregarding potentially causal variants.Association between the replicated variants and the mouth ulcer phenotypes were assessed using a LMM with age and sex as covariates in the three lookup cohorts. In ALSPAC this was implemented in BOLT-LMM (v2.3)23 to quantify the relative contribution of polygenicity and bias to inflation in single variant test statistics from UKBB. We used LDSC to estimate heritability under the infinitesimal model assumption and obtained contrasting estimates from the HESS method which may be more robust under certain genetic architectures24.Univariate LD score regression was performed using the LDSC software package P-value threshold (P < 1.5e−04) and were examined further in a fully adjusted model which included adjustment for age, sex, 40 genetic principal components and genotyping array.Analysis of the HLA region in UK Biobank used 362 imputed haplotypes (provided by UK Biobank). Unadjusted analysis was performed using logistic regression and an additive model for each haplotype which used haplotype dosage to account for uncertainty in haplotype imputation. Seventeen haplotypes were not observed within the final sample, whilst 345 were present in at least one individual and produced test statistics. Of these 345 haplotypes, 24 passed a Bonferroni-corrected 26, which uses predictive models to impute transcript expression levels in specific tissues and then uses full GWAS summary statistics to test for associations between these predicted expression levels and phenotype. A threshold was applied on predicted performance (a measure of ability to accurately infer transcription levels at a given locus in a specific tissue) with a cut-off at 0.1. After filtering, all remaining predicted expression levels were tested for association with mouth ulcers. S-MulTiXcan28 was then applied to these tissue-specific estimates, which harnesses information from the sharing of expression quantitative trait loci (eQTL) across multiple tissues, to increase power to detect the effects of gene expression on the phenotype of interest. A Bonferroni correction for multiple testing was applied which takes into the correlation between tissues and equates to 0.05 divided by the number of genes with a prediction model in at least one tissue (P < 1.9e−06). Analysis was performed using the MetaXcan standalone package, which includes the S-PrediXcan and S-MulTiXcan methods.Tests for association between ulcers and predicted gene expression were performed using S-PrediXcan79. DEPICT anticipates that association signals for a biologically causal gene will co-localise with genes encoding other members of a co-regulated gene network elsewhere in the genome. By using publicly available gene expression data to define co-regulated gene networks, DEPICT can take account of association patterns elsewhere in the genome to nominate biologically plausible candidate genes at a statistically associated locus. DEPICT analysis was performed using variants that passed the genome-wide threshold (P < 5e−8) after clumping and default settings in the standalone java software.Gene prioritisation, gene set enrichment and tissue enrichment analysis were performed using the DEPICT software package25. Garfield performs greedy LD pruning of GWAS summary statistics using reference data from the UK10K project. LD-tagged genomic regions are then annotated with information on 1005 regulatory features identified in ENCODE, GENCODE and Roadmap epigenomics projects. These features include genic annotations, chromatin states, histone modifications, DNase1 hypersensitive sites and transcription factor-binding sites in a range of cell lines. GARFIELD tests whether regulatory information is present in trait-associated loci more frequency than expected by chance by performing adaptive permutations at various significance thresholds  but requires full GWAS data. Trait-associated loci are matched with appropriate null loci using many features, such as LD information from the ENCODE and ROADMAP projects. Analysis was conducted using default settings in standalone GARFIELD (v1.0) software.Enrichment in regulatory motifs was assessed using the non-parametric enrichment analysis package GARFIELD29. For traits that passed the Bonferroni-corrected threshold for association the rho-HESS approach80 was applied to estimate local correlation between mouth ulcers and these phenotypes.Genetic correlation between mouth ulcers and other traits was assessed using bivariate LD score regression implemented in the LD Hub platformP-value thresholds between 1 and 5e−08. PRS were standardised using a mean of 0 and a standard deviation of 1. Association between PRS and mouth ulcers was assessed using LMMs in GCTA30, including a genetic relatedness matrix to account for family structure and relatedness in QIMR.PRSs were generated in plink v1.9 at a range of https://www.targetvalidation.org) was used to assess whether any of the ulcer-associated genes might represent targets for drug repurposing. The S-PrediXcan analysis was used to select gene transcripts.The Open Targets database for pharmacological interventions (Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7


Rhabdomyosarcomas (RMS) are rare and often lethal diseases. It is assumed that the tumor microenvironment (TME) of RMS exerts an immunosuppressive function, but there is currently no systematic analysis of the immune cells infiltrating sarcoma tissue. Focusing on two common types of RMS , we performed a comprehensive immunohistochemical analysis of tumor-infiltrating immune cells in the TME. We performed a qualitative estimation of infiltrating immune cells in the tumor microenvironment by an experienced pathologist as well as a quantitative digital pathology analysis. We found that (1) manual and automatic quantification of tumor-infiltrating immune cells were consistent; (2) RME tumors showed a higher degree of immune cell infiltration than RMA tumors but (3) the number of tumor infiltrating lymphocytes was low compared to other solid tumor types; (4) microvascular density correlated with immune cell infiltration and (5) CD163 positive macrophages as well as CD54 positive microvessels were more often detected in RME than in RMA and correlated with patient overall and event free survival. Our systematic analysis provides a comprehensive view of the immune landscape of RMS which needs to be taken into account for developing immunotherapies for this rare type of cancer. Whereas a specific genomic translocation between a PAX3 or PAX7 gene and FOXO1  is the tumor initiating event of RMA, the prognostic more favorable RME is characterized by numerous genetic alterations1. The poor prognosis of RMA tumors is mainly traced back to early metastasis formation with five year survival rates below 30%2.Rhabdomyosarcoma (RMS) is a soft tissue malignancy that mainly affects children and young adults. Its main subtypes are alveolar RMS (RMA) and embryonal RMS (RME), both of which express skeletal muscle differentiation genes as defined by markers like myogenin (MYOG) and desmin but differ in terms of genetic and prognostic characteristics4. In particular, blocking antibodies against immune checkpoint molecules such as PD-1, PDL-1 and CTLA-4 show clinically meaningful activity in a number of cancer types and have been clinically approved (for review see5). It is known that the best response rates are attained in tumors with high immunogenicity such as microsatellite-instable tumors6, melanoma7, non-small cell lung cancer9 and others. Other immunotherapy options include adoptive cell transfer of immune effector cells such as chimeric-antigen receptor T-cells (CAR-T) or tumor-infiltrating lymphocytes (TILs). A CAR-T treatment has been clinically approved for lymphatic tumors but not yet for solid tumors13. For RMS tumors we developed an adoptive immune therapy using chimeric T cells, that recognize the fetal acetylcholine receptor as tumor specific antigen14.In general, immunotherapeutic options are considered as promising approaches to treat advanced cancer diseases. During the last decade it was shown that increasing the immune activity against tumor cells increases survival time in multiple solid tumor types16. The TME is a complex system, consisting not only of tumor but also of endothelial cells, cancer associated fibroblasts (CAF), cytotoxic and immune suppressive immune cell infiltrates, antigen presenting cells and the extracellular matrix17 - a conglomerate that is generally called “tumor stroma”. The interactions between these constituents determine the clinical course of a solid tumor disease and the susceptibility to tumor immunotherapy19. Furthermore, enumerating the different cell types in histopathology yields powerful biomarkers that can be used to stratify patients and to predict treatment response20. Tumors with pronounced immune infiltrates are usually termed “hot”, while the absence of immune cells within the tumor tissue is referred to as a “cold” microenvironment. However, these quantitative categories alone do not adequately reflect the deeply complex immune infiltrate that is typically observed in human tumor tissue: besides infiltration with CD4+ T helper and CD8+ cytotoxic T cells, many tumors contain abundant immune suppressive cell types, like myeloid derived suppressor cells (MDSC), M2 macrophages and regulatory T cells (TREG cells)17. The efficiency of immune therapeutic approaches based on cytotoxic immune cell infiltrates strongly depends on the composition of the TME. Especially, the interaction between tumor stroma together with tumor infiltrating lymphocytes strongly influences the outcome19. In summary, knowledge of the baseline (pre-therapy) immune infiltrate is helpful to predict for clinical course and treatment response and a detailed analysis of different cell types is necessary to derive clinical predictions21.Various studies have shown that a successful cancer immunotherapy depends on a pre-existing anti-tumor immune response in the highly dynamic tumor microenvironment (TME)22, the immune landscape of sarcoma, especially RMA and RME is still understudied. Here, we employed serial stainings on a comparatively large patient cohort, using a large variety of specific markers for cytotoxic T cells, macrophages and immune suppressive regulatory T cells. To the best of our knowledge, this is the first study that systematically addresses the immune contexture of RMA and RME: In particular, the stainings were independently analyzed by a previously validated computer based approach23 and an experienced pathologist, thereby combining the assessment of the pathologist with the quantitative analysis of the computer based approach.In the present study, we strived to deeply characterize the complex immune microenvironment of human RMS tumors. While the mutational landscape of sarcomas has been analyzed in large studies2, which gave comparable and highly correlated results  and compared to the computer-based cell densities of CD3, CD68 and CD168 cells per mmlts Fig. . Hereina23  were detectable  as well as the subsets   Fig. . As was 00) Fig. . The num00) Fig. . This wa00) Fig. . In addiCD20 positive B-lymphocytes were but exceptionally observed and occurred only in follicles in the peritumorous region, but not in the tumor itself  and tumors with a low densitiy of CD54 positive capilary structures  showed that there is a strong association between the grade of CD54 positivity of capillary sructures and the number of infiltrating CD3, CD8, CD68 and CD163 immune cells .Quantitative assessment of microvessel density based on automatic analysis of CD34 stained sections Fig. . In addiWe analyzed a cohort of 39 tumor patients of which 11 RMA and 9 RME patients died from cancer during the follow-up period. Analysis of the event free survival (EFS) for both subgroups revealed a better EFS for RME patients and for patients with a more favorable localization, but both without reaching significance  , neighter the amount CD3 nor CD8 or CD68 infiltrating cells was associated with OS or EFS. Nevertheless, a logistic regression analysis for the RMA and RME subtypes revealed an influence of CD163 on the mortality (dead yes or no) of RME tumor patients (p = 0.04) with an ODDS ratio of 0.634 and the event free survival time (p = 0.004) with an ODDS ratio of 0.497 Fig. . This me88) Fig. .Figure 6Separation of the available RME set in patients with low, intermediate or high risk tumors revealed more numerous tumor infiltrating CD3 and CD8 positive cells in patients with low risk tumors  and EFS (p = 0.05) survival of patients with CD54HIGH RME  and 1 (only few CD54 positive capillaries detectable). Contrary to what we had expected from the findings in RME, this revealed a better OS (p = 0.05) and EFS (p = 0.02) of patients with RMA tumors that lacked CD54-positive endothelial cells (grade 0)  Fig. .Figure 724. Therefore, we performed a comprehensive quantitative digital pathology-based analysis of various intratumoral immune cells, PD1 and PD-L1 expression and microvascular features in RMA and RME with reference to the clinical outcome, from which we gained the following new findings: (1) RMA exhibited a low number of intratumoral CD3+ and CD8+ T cells, while the abundance of these cells was slightly higher among RME cases; (2) FOXP3 positive regulatory T cells (and B cells) were generally absent from RMA and RME; (3) CD54+ microvessels were significantly more frequent in RME than RMA and their presence was associated with higher numbers of intratumoral immune cells; (4) high numbers of intratumoral CD163+ macrophages and CD54+ microvessels were more common in RME and associated with improved survival in RME. Furthermore, we confirm the overall low abundance or even absence of CD8+ T cells in RMS as compared to other pediatric sarcomas27, and can now add that intratumorous lymphoid cells are also less abundant in RMS than in a broad spectrum of common cancers of adulthood23.Immune therapies are the new hopes for otherwise refractory cancers, and better knowledge of the tumor microenvironment is considered key to predict treatment outcome28, revealed an overall paucity of intratumoral CD3+ and CD8+ lymphoid infiltrates in RMS. However, different neoplasms  differ in terms of immune cell activation and infiltration29. Even studies of soft tissue sarcomas revealed that the immune cell microenvironment differentally affected outcome and prognosis in different sarcoma subtypes32. A comprehensive analysis of 249 soft tissue sarcoma (including 16 RMS tumors) showed that – across a broad spectrum of different ifiltrating immune cells analyzed - only an infiltration with CD20 positive B cells was an independent favorable prognostic marker31, i.e. a lymphocyte subset that was completely missing in our tumor samples. This shows that immunobiological findings obtained in a tumor entity should not be generalized without further ado, and special caution is to be taken when comparing childhood and adult cancers.The present systematic analysis of RMS and comparison with the 10 most prevalent non-sarcoma neoplams that are currently the focus of immunotherapies33, the total number of infiltrating immune cells in these tumor subtypes, as far as comparable quantitative studies are available, is low36 compared to the adult tumor entities analyzed in our study  typically show high TMB43. This hypothesis is also compatible with the observed higher number of immune cells in RME that show more genetic instability than RMA37. Futhermore, the virtual absence of FOXP3+ cells in the microenvironment of both RMA and RME; the lacking expression of the immunoregulatory PD-L1 and PD1 in any of our RMS cases (contrary to some mouse models44) and the preferential reduction of CD8+ cells over intratumoral macrophages in RMS as compared to a broad spectrum of other cancers , reflects the heterogeneity of TMB. Such studies should preferably include cases of an apparently small RMS subset (that we failed to identify in our cohort) with expression of PD-L1 on RMS tumor cells and excellent clinical outcome46. Although it appears likely that the strong focal endothelial expression of CD54 accompanied by focal lymphoid cell infiltrates in a subset of RMS are interrelated by a local immune reaction, it is unknown whether the deficient or low (grade 0–1) endothelial expression of CD54 in many RMS (and most RMA) reflects lack of endothelial activation or suppression of CD54 expression  that is often encountered in tumor microenvironments 47.An additional reason for the paucity of intratumoral lymphocytes in RMS could be the paucity of activated, CD54+ microvessels that we observed in a subset of RME and almost all RMA. The positive correlation between intratumoral lymphocyte numbers and endothelial CD54 expression in our cohort of RMS is in line with studies showing improved, CD54-dependent recruitment of leukocytes to the microenvironment of various non-rhabdomyomatous tumors48) awaiting elucidation. The positive correlation between the number of macrophage and CD54+ microvessels in RMS might mirror the well known production of angiogenic factors by tumor associated macrophages49. By contrast, the observation that higher numbers of CD163 positive macrophages and CD54+ microvessels were both associated with better survival of RME patients was unexpected: In almost all cancers high numbers of macrophages herald a poor prognosis that is thought to reflect the role of macrophages as suppressors of antitumor immunity and promoters of invasion and metastasis48. The opposite association encountered here in RME is, however, not without precedent: Cunha et al. found high numbers of macrophages in thyroid cancers linked with favorable outcome, making the authors propose two possible explanations for their exceptional finding: First, macrophages might activate rather than suppress the numerous CD8+ T cells that consistently accompany macrophages in thyroid carcinomas; second, a direct antitumor phagocytic effect of macrophages could be operative50. As to the first option, there is evidence of macrophage functional placity, i.e. their potential to switch from an immunoregulatory to an immunostimulatory function due to environmental cues or pharmacological intervention52. However, in light of the paucity of intratumoral T cells compared to the relative abundance of macrophages in the TME of RMS, the second mechanism may also be operative considering the high susceptible of RMS to macrophage-mediated cytotoxicity in vitro53. This is supportet by our finding, that especially low risk and a group of patients with intermediate risk tumors showed higher infiltration with CD163 positive macrophages.Like lymphoid cells, CD68+ and CD163+ macrophages were less abundant in RMS than in the most common cancers of adulthood Figs  and 3. H59.In any case, more investigations of the TME of RMS are necessary to learn whether the better prognosis of CD54+ microvessel-rich compared to microvessel-poor RME is due to better recruitment and activation of cytotoxic lymphocytes, immunostimulatory myeloid cells, their synergy or non-immunological mechanisms. Analogous immunological considerations may not apply to treatment-naïve RMA, in which intratumoral CD54+ microvessels and immune cells were consistently sparse and in which the occurrence of even a few CD54+ microvessels was associated with a significantly poorer prognosis than their complete absence. The opposite prognostic association of CD54+ microvessel density in RME and RMA is a new difference among many others between RME and RMA and a new example of the paradigm that the prognostic impact of intratumoral microvessel density depends on tumor type60), makes the random targeting of this immune checkpoint unlikely to be successful, while specific targeting may eventually be effective in the small, previously reported RMS subset with a PD-L1high immunophenotype61. (2) Novel immunotherapeutic strategies aim to target the immunosuppressive and tumor-promoting function of tumor-infiltrating myeloid cells by blocking the recruitment of monocytes or other precursors62. Whether such a strategy can be beneficial in RMS is an open question in light of our finding that higher numbers of macrophages in the TME were associated with better survival, at least in RME patients. By contrast, the latter findings may be a rational for strategies that try to activate the phagocytic capacity of intratumoral macrophages53 or skew their polarization towards an immunostimulatory function62. (3) Taking the overall paucity of intratumoral CD3+ in RMS into account, it appears likely that the adoptive transfer of RMS-directed cytotoxic lymphocytes alone may be insufficient to eradicate established RMS. Indeed, we previously showed that human RMS xenografts are only transiently susceptible to RMS-specific chimeric T cells14. Therefore, combination strategies that improve the recruitment of lymphoid cells (including chimeric effector cells) to the TME of RMS and prevent their inactivation there may be necessary to improve cell based immunotherapies. To rationally design such complementary interventions, be they directed towards ‘anergic endothelial cells”47 or intratumoral myeloid cells will require a more in depth analysis of intratumoral as well as peritumoral microvessels and a more comprehensive characterization - beyond the simplistic CD68+ and CD163+ dichotomy - of the many different myeloid cell subsets that can now be distinguished by multiparameter analysis48.The current findings migh have therapeutic implications: (1) the consistent lack of PD-L1 on tumor cells and tumor infiltrating immune cells, and the paucity of PD1+ cells in the TME of all our RMS cases (n = 39) . All patients were treated according to CWS protocols. Written informed consent according to the Declaration of Helsinki was obtained from all patients or their legal guardians, depending on the patients’ age. All studies were approved by the appropriate ethics and review committees  and 2012-257N-MA ). Composition of the available dataset is summarized in Supplemental Table et al.23.A set of human solid tumors for comparision of the tumor microenvironment in RMS and other tumor types were recently published by Kather 63 using the chemicals and reagents listed below: antigen retrieval in Novocastra antigen retrieval solution ph6 or pH 9.0 ; blocking of endogenous peroxidase ; detection of bound antibodies by the immunoperoxidase/DAB-based DAKO REAL detection system (DAKO).Immunohistochemistry staining was done on 0.5–1 µm slides of paraffin embedded RMS biopsies by using routine immune peroxidase techniques. For quality control reasons, tonsils were included as positive controls. Pretreatment of the sections is depending on the used antibody. Antibodies, pretreatment conditions and antibody dilutions are listed in Supplemental Table All 39 paraffin embedded RMS tissues were assessed for the infiltration with T-lymphocytes by using CD3 and CD8 staining, tumor-associated monocytes/macrophages by using CD68, CD163 and CD11b or B cells by using CD20 staining. Regulatory T cells (Tregs) were analyzed by using FOXP3 staining. For manual, semi-quantitative cell enumeration, an expert pathologist (A.M.) reviewed all slides independently of the original pathology report and estimated each immune cell infiltrate as the percentage of a given immune cell subset (e.g. CD3+ cells) per one hundred tumor cells. All analyses were performed in a blinded way with respect to clinical outcome. The validity of this approach was ensured by correlating the percentages based on manual enumeration with the results of computer-based image analysis (see below). Immune cells were also subdivided in terms of their localization as “intra-tumoral” (i.e. in between tumor cells), “septal” . In addition, follicular arrangement of lymphoid cells was assessed.64 to automatically detect all positively stained cells as described before19. The thresholds for cell detection were manually optimized for each type of staining and the same settings were used for all cases. For all further analyses, we used the mean cell density (cells per mm²) in the tumor tissue. For vessel detection a previously developed morphometric algorithm was used, that could safely recognized and distinguished endothelial cells from other CD31 und CD34 positive stromal cells65. Since CD34 expression is not restricted to endothelial cells, CD31 was applied as second endothelial marker compared to the CD34 pattern . Then a board certified pathologist (C.H.) manually delineated the tumor region in the digital slides. Next, we used QuPath v0.1.2Logistic regression analysis and survival analysis were done using SAS statistical software, release 9.4 . Non-parametric tests were used to compare groups; the Mann-Whitney test to compare two and the Kruskal-Wallis-Test to compare more than two groups. For survival analysis a logrank test (Mantel-Cox test) was used. Graphics were done by using the GraphPad Prism Software tool. Significant results were defined as p < 0.05; trends with p < 0.1.Supplemental Figures and Tables