The genetic etiology of childhood cancers still remains largely unknown. It is therefore essential to develop novel strategies to unravel the spectrum of pediatric cancer genes. Statistical network modeling techniques have emerged as powerful methodologies for enabling the inference of gene-disease relationship and have been performed on adult but not pediatric cancers. We performed a deep multi-layer understanding of pan-cancer transcriptome data selected from the Treehouse Childhood Cancer Initiative through a co-expression network analysis. We identified six modules strongly associated with pediatric tumor histotypes that were functionally linked to developmental processes. Topological analyses highlighted that pediatric cancer predisposition genes and potential therapeutic targets were central regulators of cancer-histotype specific modules. A module was related to multiple pediatric malignancies with functions involved in DNA repair and cell cycle regulation. This canonical oncogenic module gathered most of the childhood cancer predisposition genes and clinically actionable genes. In pediatric acute leukemias, the driver genes were co-expressed in a module related to epigenetic and post-transcriptional processes, suggesting a critical role of these pathways in the progression of hematologic malignancies. This integrative pan-cancer study provides a thorough characterization of pediatric tumor-associated modules and paves the way for investigating novel candidate genes involved in childhood tumorigenesis. Improving the management of pediatric cancer is essential and will benefit from more accurate diagnosis, new personalized treatment and development of specific and less damaging therapies. To face these challenges, it is necessary to unravel the complete genetic repertoire of pediatric malignancies. Recent studies have improved the understanding of the genetics of childhood cancer, but have mainly focused on depicting the germline and somatic mutational landscape of these diseases4.Cancer remains the leading cause of death by disease in children of less than fourteen years of age5. Childhood cancers have a 14-times lower mutation rate compared to adult tumors and mostly arise from mutations in few driver genes. Somatic alterations mostly target a handful of major genes such as CDKN2A, NOTCH1, NRAS, KRAS or TP53, and pathways disrupted by driver alterations are either common to cancer (e.g. cell cycle) or specific to pediatric cancer histotypes4. More than half of the driver genes are restricted to one cancer histotype and 83% of them are not shared between hematologic and solid tumors. This indicates that certain genes and pathways are exclusively dysregulated in a single type of childhood cancer.Several evidences demonstrated that the biology and genetics of pediatric cancers set them apart from adult tumors8. This proportion is likely underestimated considering that only cancer-related genes were analyzed for pathogenicity in these studies. To date, over 100 cancer predisposition genes have been described and most of the associated pathogenic germline variants were loss of function mutations in DNA or double-stranded break repair genes8. The total spectrum of cancer-predisposition genes involved in childhood tumorigenesis still remains to be uncovered.Regarding hereditary predisposition, genome-wide studies reported that pathogenic germline variants were identified in 8–10% of the affected children and adolescents10. Integration of transcriptome-based knowledge has emerged as a powerful method for prioritizing genomic alterations in cancers11. Statistical network modeling is essential for interpreting genotype-to-phenotype relationships or discerning transcriptional regulatory programs14. Studies reported that mature pediatric tumors mirror the conserved transcriptional programs of embryonic cell populations that have been subject to genomic changes15. A system-level understanding of how the genetic mutations affect transcriptional profile has been provided in adult pan-cancer data16. Such analyses revealed common functional gene clusters that are shared by multiple adult cancer types.Tumor initiation and progression result from complex interplay between germline and somatic events that shape the transcriptional landscape of tumors17. The published studies have only focused on deciphering co-expression networks of one particular histotype and, therefore, lack to provide a global view of both common and histotype-specific processes that drive childhood tumorigenesis. This requires a deep exploration of the co-expression network obtained by analyzing pan-cancer childhood data.In onco-pediatric research, construction of co-expression networks achieved interesting results in identifying predictive molecular biomarkers and in unraveling differential regulatory molecular programs by analyzing matched normal-tumor samplesHere, we carried out computational analyses of the transcriptome data of 820 pediatric cancer samples selected from the Treehouse Childhood Cancer Initiative (TCCI) dataset across six cancer histotypes. We constructed a co-expression network using weighted gene co-expression network analysis (WGCNA) to capture transcriptional relationships between genes in pediatric cancers. We associated the resulting modules with tumor types by examining their transcriptional profiles and by characterizing their biological functions. We determined the most connected genes within modules and highlighted their biological relevance to different tumor types. We investigated for the over-representation of pediatric cancer gene sets in these modules and mapped them into the co-expression network. Our integrative analysis provides a working frame for investigating candidate genes involved in pediatric tumorigenesis through the deep-level exploration of modules specifically associated with childhood cancers.We hypothesized that transcriptome data from pediatric cancer samples could provide a thorough understanding of the key genes and pathways implicated in childhood tumorigenesis. We thus developed an integrative study for which the general workflow is displayed in Fig. 18, Wilms Tumor 19, Medulloblastoma 20 and Acute Myeloid Leukemia 21. The median age was consistent with recent reports for Acute Lymphoblastic Leukemia 22, but lower for glioma with 8 vs 9 years old23. Consistent with the American Cancer Society statistics of 2014, we found a higher incidence of males in NBL (sex ratio = 1.42) and MBL (sex ratio = 1.59), along with a slight female preponderance in WT (sex ratio = 0.77)  Fig. .Figure 224. Considering the distinct embryonic origin of childhood tumors, our findings demonstrate that each pediatric cancer type has a specific transcriptome signature.We next applied a t-distributed stochastic neighbor embedding (t-SNE) algorithm on the RNA-Seq data to refine groups of childhood tumors by projecting the patient samples in a low-dimensional space based on their transcriptional features. Hierarchical clustering of the resulting coordinates revealed six clusters matching the pediatric tumor histotypes Fig. . We obseAfter demonstrating that pediatric tumors were characterized by specific gene expression profiles, we have undertaken a network-based approach to identify modules of genes particularly associated with childhood tumors. We constructed modules of genes sharing highly similar expression patterns across pediatric pan-cancer samples by performing WGCNA analysis on the transcriptome data of the study cohort. We identified 23 co-expression modules, labeled by color Fig. . For all2 = 0.93, p < 0.001), magenta/NBL , red/WT (R2 = 0.8), midnightblue/AML , green/AML  and lightgreen/MBL  . To further demonstrate the significance of these module/tumor associations, we found that the second highest levels of correlation decreased to levels less than 0.5 with the other cancer types: lightcyan/WT , magenta/AML , red/AML , green/MBL , midnightblue/glioma , lightgreen/AML   their gene expression levels in the tumor samples and 2) their gene significance levels towards the cancer histotype. We first assessed module-tumor relationships by using Linear Discriminant Analysis (LDA) approach that maximizes the separation between tumor types based on the expression profiles of the modules  Fig. . A low cConsidering the associations between the modules and the specific tumor types, we reasoned that exploring the biological functions of the genes within modules would shade light on the subtype-specific processes implicated in pediatric cancers. To investigate this, we performed functional enrichment analyses using GO and KEGG annotation terms for each module J recombination . Abnormal recombination during somatic rearrangements of surface immunoglobulin (Ig) and T cell antigen receptor (TCR) genes has been described in the transformation of lymphoid cells26. The lightgreen-MBL module was functionally related to visual  and sensory perception , along with forebrain development . This is in accordance with the aberrant differentiation of the most aggressive subgroup of MBL in the photoreceptor program27. The over-expressed genes in the green-AML module were linked to myeloid-mediated immunity processes . No enrichment was found in either GO or KEGG annotation terms for the midnightblue-AML module. Taken collectively, five out of the six cancer-histotype specific modules were enriched in biological functions consistent with the tissue origins and physiopathology of the related childhood cancer. The modules associated with pediatric tumors encompass genes implicated in developmental processes, supporting the close link between organogenesis and tumorigenesis in childhood cancers.We found that genes co-expressed in the magenta-NBL module were involved in the development of the autonomic (Fold Change (FC) = 22; FDR < 0.001) and sympathetic nervous system , in line with the physiopathology of NBL that derives from postganglionic sympathetic neuroblasts Fig. . The red8. The blue module was involved in lymphocyte differentiation  and proliferation  which is consistent with the pathogenesis of ALL. Likewise, the grey60 module was related to the B-cell ALL subtype with functional enrichment in B cell activation  and differentiation . Despite functions specific to the B-cell ALL subtype, the grey60 module showed higher levels of expression in both acute leukemias and glioma samples, thereby, not fulfilling the « histotype-specific » criteria. The brown module was over-represented in genes linked to post-transcriptional and epigenetic processes, such as mRNA processing  and histone modification , which are believed to contribute to relapse in acute leukemias28. Additional modules were associated with biological mechanisms having known implications in pediatric tumorigenesis, see Additional File Functional examination of the 17 non-cancer-histotype specific modules revealed significant associations with either canonical oncogenic or onco-hematologic processes Fig. . The tan29, we defined different lists of pediatric cancer genes (pedCGs) implicated in ALL (113 genes), AML (32 genes), WT (49 genes), MBL (113 genes), NBL (166 genes) and glioma (22 genes) , the lightcyan-ALL module in ALL pedCGs , the red-WT module in WT pedCGs , and the lightgreen-MBL module in MBL pedCGs   Fig. .Figure 6Overall, 4 out of the 6 cancer-histotype specific modules were significantly enriched in the pediatric cancer genes of the associated tumor type. These findings support that genes in these cancer-histotype specific modules should be given higher priority in variants prioritization methodologies applied to childhood cancers.2 depicted the germline mutational landscape of pediatric tumors through a comprehensive pan-cancer study in a large cohort of children and adolescents. The pediatric cancer predisposition genes (pedCPGs) were defined as cancer-related genes harboring pathogenic germline mutations in childhood cancer patients. We mapped the literature-based pedCPGs into the constructed childhood cancer co-expression network  and midnightblue-AML  modules encompassed most of the clinically actionable genes. We found the oncogenic (tan) module to be significantly enriched in pediatric cancer predisposition genes for 4 out of the 5 tested histotypes  . These results suggest that genes in the tan module, when mutated, are likely contributing to tumor initiation in multiple childhood cancers. We also identified a novel onco-hematologic module (pink) that gathers genes believed to be early genetic determinants in pediatric acute leukemia  Fig. . No data.65–5.75;. 2 Fig. . Most of4 identified pediatric cancer driver genes (pedCDGs) in a large cohort of childhood cancer patients. We recovered the published data of this study to build lists of cancer driver genes significantly mutated in ALL (106 genes), AML (33 genes), WT (12 genes) and NBL (8 genes)  and AML pedCDGs . The ALL driver genes were over-represented in the grey60  and lightcyan-ALL  modules. We demonstrated that genes frequently altered by somatic alterations in pediatric ALL were significantly enriched in the grey60 module related to B cell development and in the lightcyan-ALL module. The grey60 module could therefore, play a key role in the ALL tumorigenesis and was also associated with a favorable status of druggability .Ma and colleaguesd in ALL 06 genes,30, here the pathogenesis of childhood cancers. We defined hub genes as the most interconnected genes within a module. To provide novel insights into potential key regulators of childhood cancers, we performed an in-depth evaluation of their hub genes and focused on the top 15 hub genes  gene is one of the major predisposition gene for NBL31 and was identified as a key regulator in the magenta-NBL module. Other hub genes of this module were shown as essential for neural differentiation of the sympathoadrenal lineage  and comprised a novel candidate gene for NBL (ISL1)33. The paired box 2 (PAX2) was among the hub genes of the red-WT module and believed to be a tumor-inducing gene in WT with key role in kidney cell differentiation34. The major gene of predisposition to Wilms Tumor (WT1) was, however, ranked 483rd, as its expression was high in both WT and AML samples  gene, known as the major predisposition gene in B-cell ALL35, one of the direct target of PAX5 (CD19) and a key player in B-cell differentiation (TCL1A)36. In the lightgreen-MBL module, the hub genes were involved in neurogenesis, particularly in the forebrain  and cerebellar development  with predominant expression in MBL41. Two of these hub genes  were the conductors of key transcriptional programs in the Group 3 subtype of MBL, the most aggressive subtype of MBL37. The hub genes in the green-AML module encoded protein with roles in leukemogenesis and myeloid differentiation  or subject to recurrent alterations in infant AML (MYO1F)45. Statistical analyses supported that pediatric cancer genes were enriched among the hub genes of the six cancer-histotype specific modules . The pediatric cancer genes of one tumor type were enriched among the hub genes of the associated module .The paired like homeobox 2B  but most of them have not been associated with pediatric tumors   and RNA metabolic processes . Some of these hub genes were either considered essential to the survival of AML cells (JMJD1C) or identified as fusion partners (CHD1) of the major player in hematologic malignancies (RUNX1). The hub genes of the blue module take part in immune response mechanisms  and comprised one critical regulator of lymphoid differentiation (IKZF1) that is frequently deleted or mutated in B-cell precursor ALL47. The cancer driver genes of ALL were enriched among the hub genes of the blue module .Many of the key regulators in the canonical oncogenic (tan) module were involved in the processes leading to tumor cell proliferation and survival  Fig. . ConsideOur study integrated genomic knowledge in the network-based analysis of RNA-Seq data of six pediatric cancer types to provide a novel biological framework for investigating genes involved in childhood cancers. This comprehensive pan-cancer study relies on the robust definition of gene co-expression modules and their association with particular features of pediatric cancers. The observation of transcriptional profiles and biological functions connect modules to cancer-histotype specific, onco-hematologic and canonical oncogenic processes Fig. . Topolog16. The present study identifies modules associated with childhood cancers having biological implications in developmental processes. Our findings support the close tie between organogenesis and tumorigenesis in childhood malignancies. The pathogenesis of NBL is tightly related to disruption in noradrenergic neuronal development. The key regulators  of this developmental process are also the hub genes of the module found associated with NBL33. One of its key regulators, PHOX2B, is the major predisposition gene to NBL31. Therefore, the other central genes of the magenta-NBL module constitute interesting candidates that should be further investigated in the study of the nervous system development and NBL. In support of our findings, ISL1 has been recently defined as a novel candidate gene for NBL and is also one of the hub gene of the magenta-NBL module32. The ALL tumorigenesis is the result of aberrant V(D)J recombinations at the origin of recombinase-mediated deregulated expression of a variety of proto-oncogenes. In the lightcyan-ALL module, the genes are involved in V(D)J recombination processes which is consistent with the physiopathology of ALL and includes, as one of its key regulators, a major predisposition gene for B-cell ALL (PAX5). The red-WT module is associated with the ontogeny of the kidney and one of its hub genes PAX2, is also believed to be a strong candidate gene for WT and is known as a key player in kidney cell differentiation34. Numerous genes co-expressed in the lightgreen-MBL module are related to embryonic brain ontogeny and to key transcriptional programs implicated in MBL pathogenesis37. The central regulator OTX2 of the lightgreen-MBL module is a candidate driver gene for MBL pathogenesis and is responsible for the regulation of cerebellar development and forebrain segregation37. One of the two modules associated with the AML subtype is related to myeloid-mediated immunity processes. The cancer-histotype specific modules associated with NBL, ALL, WT and MBL are significantly enriched in pediatric cancer genes of the related histotype. Despite revealing modules with functional relevance for the majority of the tumor types, our analysis was not able to pinpoint a module specific to glioma. This is likely the result of the wide heterogeneity of this cancer type characterized by distinct subgroups, as shown in our t-SNE analysis. The hub genes of the cancer-histotype specific modules were enriched in known pediatric cancer genes. Many of these hub genes have still unknown functions or unrevealed implications in childhood cancers. Considering these converging levels of evidence, the hub genes of the cancer-histotype specific modules constitute interesting candidates that should be investigated to validate their role in pediatric cancers, developmental processes, or both.As demonstrated for adult cancers, our approach enables investigating cancer genes and shows that multiple cancer types have exclusive hub genes. Adult pan-cancer analyses achieved interesting results in identifying functional gene modules common to cancer, rather than modules specific to tumor types3. The genes co-expressed in this module are therefore likely early genetic determinants of childhood tumorigenesis. In acute leukemias, the cancer driver genes are over-represented in the brown module associated with common functions in epigenetic and post-transcriptional modifications. These processes are the most important somatically-altered pathways in childhood cancers and could be critical for tumor progression in hematologic malignancies4. The ALL driver genes are enriched in the lightcyan-ALL module related to V(D)J recombination and the grey60 module linked to B cell activation and differentiation. This suggests that co-expressed genes and pathways in these modules  could contribute to B-cell ALL tumorigenesis. We could not assess the genomic alterations for all the studied tumor types because of biases in documented literature. There was a lack of information regarding germline mutations in WT and the driver genes in glioma and MBL that prevented us to test them for enrichment analyses4.Our analysis further links modules to cancer-related pathways that are not specific of one pediatric tumor. Statistical analyses show enrichment of cancer genes frequently altered by pathogenic germline variants in the module related to cell cycle regulation and DNA repair, which is consistent with recent findings48. Our results thus enable identifying candidate targets in cell-cycle therapeutics in childhood cancer. The majority of the hub genes of the grey60 module have key roles in innate immune recognition and activation and comprise Toll-like receptors (TLR1 and TLR6) that are potential therapeutic targets in onco-hematology49. Hematopoietic malignancies promote unique immune evasion pathways and genes taking part in the innate immune system appear to be logical innate immune targets. The hub genes of the grey60 module constitute candidate targets that should be investigated for therapeutics in onco-hematology. Targetable genes involved in the VEGF pathway are enriched in the midnightblue module and include critical regulators such as VEGFR1 (known as FLT1) and VEGFR3 (known as FLT4) that are inhibited by VEGF-targeted approaches . Further investigations of the genes co-expressed in this module is however needed to clarify their potential in the management of hematologic malignancies.Regarding over-representation of clinically actionable genes in key modules, our analyses give relevant information about therapeutic targets. Across pediatric malignancies, the canonical oncogenic (tan) module shows a significant enrichment in drug-targetable genes. Most of the central regulators of the tan module are taking part in the regulation of the cell cycle. Currently, number of specific cell cycle inhibitors have emerged in the context of pediatric-focused drug development13. Another point is related to interpretability of the modules. As modules can be interconnected, some genes may interact with many others and participate in different functions50. This could be seen as a limit, considering that genes involved in various malignancies have lower biological significance than expected, towards a particular tumor. As an example, WT1 gene is not among the top hub genes of the WT-module because of its involvement in different cancer types. We also questioned the tissue effect in our study, hypothesizing that the modules associated with pediatric tumor histotypes could be more the reflect of the tissue origin of the tumor than independent molecular drivers. We performed additional analyses proving that pediatric cancer genes of one tumor type were over-represented in the tissue-specific genes matching the cell-of-origin of the tumor . We selected only cases with an age at diagnosis equal to or less than 18 years old to fit our problematic and cancer types that were represented by at least 50 cases for enough statistical power. The AML, ALL, NBL, and WT samples data were recovered from the TARGET project and supplemented by the MBL and glioma samples from TREEHOUSE. Expected counts were annotated using the human genome (GRCh38.p3) version 23 with Ensembl gene IDs. We focused on transcripts with consistent annotations, i.e. protein-coding genes, with more than 10 reads in overall samples. Read counts were normalized using the variance-stabilizing transformation of the DESeq. 2 R v.16.1 package52 based on tumor type and project variables . The resulting transcriptome dataset consisted of 14,748 gene expression measurements for 820 pediatric tumor samples, see Extended Experimental Procedures for data pre-processing  and downloaded from the UCSC Xena platform at nome GRCh8.p3 vers53. To apply t-SNE on more than thousand input objects, we used a variant of the Barnes-Hut algorithm. We ran 2,000 times the Barnes-Hut t-SNE and set the theta parameter to 0 to lower the Kullback-Leibler divergence (Rtsne R library v.0.13). The resulting coordinates were used for hierarchical clustering analysis (hclust R stats v.3.4.4) and clusters were defined using the cutree function of the R stats library.We employed the t-SNE technique to investigate and visualize the transcriptome dataset in a low-dimensional space (2D-map)55. We used the blockwiseModules function to construct a signed co-expression network with sized modules ranging from 30 to 8,000 genes and set the power adjacency function to 14 and the mergeCutHeight to 0.25, see Extended Experimental Procedures for details  was defined as the first principal component of a given module and considered as a representative of the module expression profile. The Module Membership (MM) of a gene was defined as the correlation between its expression profile and the ME of a module. We tested the difference in the mean expression levels of a gene between one tumor type vs all the other types by performing Wilcoxon Rank-Sum tests (wilcox.test R stats v.3.4.4). P-values were adjusted with a Bonferroni correction according to the number of genes and tumor types tested (p = 5.65 × 10−7). The gene significance (GS) was measured as minus log10 of the adjusted p-value and reflected the association of gene with a tumor type.We constructed a co-expression network using the WGCNA method developed by Langfelder and Horvath (WGCNA R library v.1.63)13. We randomly selected gene sets matching the size of the observed module and compared the sum of gene correlations in the null module with the one observed over 10,000 iterations. The statistical probabilities were defined as the rank of the observed module among null module out of the total iterations. Significance was considered after Bonferroni correction according to the number of modules tested (p < 2.17 × 10−3). We used a bootstrap-based method to evaluate the module structure vulnerability to perturbations. Networks were reconstructed 100 times with the same parameters on a random selection of the initial samples. The robustness was measured as the number of times a gene was assigned to the observed module over iterations.To evaluate the stability and reliability of the co-expression network, we assessed if the modules were composed of genes more strongly correlated than by chanceWe performed a LDA approach (lda function MASS R package v.7.3–50) to segregate pediatric cancer types based on the average expression profiles of each module. The module contribution to the in-between-class variability of a pediatric tumor was defined as the mean expression of the module across samples of one tumor. We obtained a matrix with tumor types in rows and modules in columns with each cell corresponding to this mean expression. To identify strong module-tumor relationships, we defined an empirical threshold of 0.06 of the absolute value of the mean expression. Mean values were visualized through clustered heatmaps (pheatmap R library v.1.0.10).56.We performed a functional enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) with enrichGO and enrichKEGG functions (clusterProfiler R library v.3.4.4). For each module, the 100 genes with the highest MM were used as input and the initial 14,748 genes as the background set. For the sake of accuracy, we used entrez ids as input and set up the significance thresholds to 0.01 for FDR adjustment method29. Based on the published study of Zhang and colleagues2, we selected all the germline variants reported in autosomal dominant and recessive cancer genes to establish pediatric predisposition genes (pedCPGs) for each pediatric tumor. All the alterations in autosomal dominant cancer genes were displayed across modules by using the oncoPrint function (ComplexHeatmap R library v.1.14.0). We used the list of pediatric cancer driver genes (pedCDGs) identified by Ma and colleagues4 and selected only the significantly mutated ones for each pediatric tumor type . Potentially druggable genes (PDGs) consisted of the ones known to have a direct or indirect targeted treatment available or under development57. The detailed methodology is available in the Extended Experimental Procedures  of normal tissue samples. The teGeneRetrieval function (TissueEnrich R library v1.5.1)58 was used to identify tissue-specific genes based on the median gene-level TPM by tissue.The pediatric cancer genes (pedCGs) were collated from the PediCan database58. All the enrichments with OR >1 passing FDR < 0.05 were considered as significant in the analysis. To visualize relevant genes in the network, we selected the top 15 hub genes and pediatric cancer genes within a module (igraph R library v.1.2). The edges between pairs of the input genes were calculated based on the TOM and represent the strength of their shared connections. The over-representation of pediatric cancer genes in tissue-specific gene sets was displayed using the corrplot function (corrplot R library v0.84).Gene set enrichment analyses were performed using a two-sided Fisher’s Exact test with an alpha level of 0.05 to assess the relationship between the genes of a list and a module. This analysis determines whether the fraction of genes of interest in the module is higher compared to the fraction of genes outside the module . The statistical probabilities were reported as the FDR adjusted p-values to reduce the likelihood of false positivesSupplemental Tables.Supplemental Figures.Extended Experimental Procedures.


British Journal of Cancer1 and contributing to the evaluation of the potential risk of HPV-related second cancer in patients with a history of a primary HPV-related cancer in the same or an independent mucosal site.We read with a great interest the meta-analysis of Gilbert D.C. et al. recently published in 2 and it is now well established that HPV infection is involved in the oncogenesis of genital  or oropharyngeal carcinomas. Patients with primary HPV squamous cell carcinoma may have increased risk to develop HPV-related second malignancies, in the same or in another mucosal site.8 However, the vast majority of available data regarding the risk of developing secondary cancers or high-grade intraepithelial lesions for a patient who has presented an index HPV-induced cancer originate from retrospective studies essentially focused on two mucosal sites1 and as it was discussed by Gilbert et al., all of these retrospective studies are very heterogenous.Oncogenic high-risk HPV (HR-HPV) are involved in about 5% of overall cancersEven if the tendency towards an increased risk was observed in all studies and across each of the mucosal sites, it is still difficult to clearly recommend a systematic follow-up for these patients and to define how this monitoring should be carried out. Indeed, the studies are very heterogenous and present clear limitation due to their retrospective design, especially for the ones which reported rates of a second independent cancer after an initial diagnosis of primary HPV-associated cancer.The European Georges Pompidou Hospital  takes advantage of a large recruitment of patients suffering from HPV-related cancer , as well as HPV biological and clinical experts. Diagnosing and treating HPV-induced cancers represents a unique opportunity to prevent subsequent HPV-associated cancers with a multidisciplinary follow-up. We have created in 2014, for the first time to our knowledge, the so-called CoMPap (Consultation Multidisciplinaire Papillomavirus) HPV multidisciplinary consultation based on an annual prospective monitoring of patients with a history of HPV-related primary tumour. To be enroled, the patients have to sign a dedicated informed consent approved by the ethical committee.Included patients are annually examined by clinicians  to detect macroscopic mucosal lesion in all HPV-concerned mucosal site. In parallel, pap-smears are collected for cytological and virological evaluation. Different infection sexually transmitted serological status are checked and a psychologist can also be requested for patient care if indicated. Finally, the CoMPap consultation allows an innovative medical care of the HPV cancers allying supervision, prevention, psychological support and biobanking.n = 41; 69.5%) essentially enroled after in situ or infiltrative genital carcinoma diagnosis. Eighteen men were also included and monitored.Until now, 59 patients (median age 55 years) have been enroled in the CoMPap. The cohort includes a majority of women  to closely monitor HPV-related cancer patients in prospective cohorts to prevent or diagnose at an earlier curable stage of these potential secondary malignancies.


Unlike chemotherapy treatments that target the tumor itself , immune-based therapies attempt to harness the power of an individual patient’s immune system to combat cancer. Similar to chemotherapeutic agents, the dosage and Administration section of labeling for all five currently approved PD-1/PD-L1 inhibitors (immunotherapy) recommends duration of treatment until disease progression or unacceptable toxicity. Overactivation or constitutive activation of the immune system with immune based therapies can lead to T-cell exhaustion and activation induced cell death (AICD) in T- and B-cells. Examples of immune exhaustion and T-cell depletion is noted in preclinical and clinical studies. Overactivation or constitutive activation leading to Immune exhaustion is a real phenomenon and of profound concern as immune cells are the true arsenal for control of the tumor growth. Designing trials rigorously to address the optimum treatment duration with immune based therapies is critical. By addressing this concern now, not only we may improve patient outcomes, but also gather a deeper understanding of the role and mechanisms of the immune system in the control of tumor growth.Chemotherapy and immune-based therapies provide antitumor effects through completely different mechanisms. Chemotherapeutic agents are cytotoxic in that they directly inhibit basic cellular mechanisms, killing both malignant and nonmalignant cells , while immune based therapies wake-up the host immune system to recognize malignant cells and eliminate them.overactivation or constitutive activation of the immune system may lead to immune exhaustion and depletion of effector T-cells thereby causing decreased anti-tumor effects and possible allowing for tumor progression.While there is a burgeoning excitement surrounding development of immune based therapies for the treatment of cancer, the optimal duration for these therapies need to be explored with equal fervor. Dosing for chemotherapy has been determined over years through large-scale prospective randomized trials to pinpoint the dose which maximizes therapeutic effect while minimizing side effects. Also, due to the mechanism of chemotherapeutic action, the duration of treatment with these agents is generally until disease progression or patient intolerance. However, experience with immune based therapies is limited, with current dosing and duration guidelines based primarily on initial trials required for approval of the agents. Since immune based therapies work by activating the body’s own immune system, there is concern that Similar to chemotherapeutic agents, the Dosage and Administration section of labeling for all five currently approved PD-1/PD-L1 inhibitors recommends duration of treatment until disease progression or unacceptable toxicity. However, since immune based therapies work with a completely different mechanism compared to chemotherapy, using the same therapy duration may not be the optimal approach.In exploring treatment duration with immune based therapies, we need to answer the following: (1) does indefinite treatment with immune based therapies exhaust the immune system counteracting its own mechanism of action leading to tumor progression and (2) how can clinical trials be designed to identify the optimal duration of immune-based therapy that prevents immune cell exhaustion but supports anti-tumor immunity. Overactivation or constitutive activation of the immune system can lead to T-cell exhaustion and activation induced cell death (AICD) in T- and B-cells. Clinical examples of the detrimental effects of immune exhaustion have been studied in a number of other diseases including sepsis and chronic viral infections, where constitutive activation of the immune system eventually leads to immunosuppression through similar mechanisms to those described below –3.AICD is programmed cell death in activated T cells caused by the interaction of Fas receptors  and Fas ligands  . Both acExhausted T-cells in cancer express high levels of inhibitory receptors, including PD-1, CTLA-4, TIM-3, LAG-3, BTLA and TIGIT, as well as show impaired effector cytokine production such as IL-2, TNF-α, IFN-γ and GzmB and are essentially ineffective in eliminating malignant cells. Deeply exhausted and terminally differentiated T-cells can also more frequently undergo AICD and apoptosis. A few examples of immune exhaustion in preclinical and clinical studies are listed below.Some of the combination studies performed with checkpoint modulators have demonstrated T-cell exhaustion and attenuation of the efficacy in preclinical models. Agonist antibodies specific to OX40 (anti-OX40) can induce significant antitumor effects in preclinical models. Combination studies performed with anti-PD1 and anti-OX40 demonstrate that concurrent combination treatment induces a strong, but short-lived burst of intratumoral T-cell proliferation, which coincides with acute cytokine secretion, increased TIM-3 + CD8+ exhausted cells, and attenuated antitumor effect. However, administering anti-OX40 and anti-PD-1 in a sequential fashion avoids the T-cell exhaustion induced by concurrent combination treatment . A similA perfect example of overactivation or constitutive activation leading to immune exhaustion can be observed in purine nucleoside phosphorylase (PNP) deficient patients. These patients present with lymphopenia and all along it was thought that inhibition of PNP would lead to immune-suppression and hence PNP inhibitors were developed for autoimmune diseases and hematologic malignancies . ContrarPNP deficiency or inhibition causing hyperactivation of the immune system is noted in genetic, preclinical and clinical studies. Examples of this phenomenon include the following (a) autoimmune manifestations, such as lupus, hemolytic anemia and idiopathic thrombocytopenic purpura, are relatively common in PNP deficient patients , (b) PNPAnother possible clinical example comes from clinical trials of Indoleamine 2,3-dioxygenase − 1 (IDO-1) inhibitor, epacadostat in combination with anti-PD1 immunotherapy. Epacadostat (given daily) in combination with anti-PD1 failed to reach primary endpoint that is Progression free survival (PFS) and overall survival (OS) in the phase III trials of unresectable metastatic melanoma (data presented April 2018) in spite of a number of positive phase I/II trials in melanoma and other solid tumors . While tChronic/constitutive activation of immune system releasing proinflammatory factors promotes tumor development, progression, and metastatic dissemination. Although, signals that trigger acute inflammatory reactions often stimulate dendritic cell maturation and antigen presentation thus activating antigen specific T-cells and promoting anti-tumor effects. This antagonism between inflammation and immunity needs to be considered carefully.One could potentially envision immune based therapies to provide the necessary impetus at the beginning and, afterwards, discontinue the therapy further and let the activated immune system control the tumor growth. This concept has been nominally explored in a couple of retrospective or observational studies with the most notable being a follow-up to the KEYNOTE-001 trial for Pembrolizumab . They suThe fear of undertreatment can make clinicians and patients reluctant to pursue abbreviated dosing schedules with immune based therapies. However, with the understanding that over-treatment with immune based treatment could be counterproductive and may potentially promote tumor growth, the issue of treatment duration with immune based therapies need to be seriously addressed. Randomized trials are needed to explore not just stopping early, but also possible predictors of success with an earlier stop. Extensive correlative studies need to be performed with immune-based therapies to identify appropriate biomarkers with clinical response. Because of the complexity of the immune response and tumor biology, it is unlikely that a single biomarker will be sufficient to predict clinical outcomes in response to immune-based therapy. Rather, the integration of multiple tumor and immune response parameters, such as protein expression, genomics, cell subsets, and transcriptomics, may be necessary for accurate prediction of clinical benefit.In addition to these biomarkers, it may be important to explore immune-related adverse events as potential marker and a possible predictor of success with an earlier termination of therapy. A comparative detailed retrospective analysis of progression free survival and overall survival from patients who received immune based therapies and experienced immune related adverse effects leading to discontinuation of therapy versus patients who continued with therapy may provide valuable information.As the field for the immune-based therapies is expanding exponentially, the time is now to address the treatment duration for immune based therapies. Overactivation or constitutive activation leading to Immune exhaustion is a real phenomenon and of profound concern as immune cells are the true arsenal for control of the tumor growth. By addressing these concerns now, not only we may improve patient outcomes, but also gather a deeper understanding of the role and mechanisms of the immune system in the control of tumor growth.


To identify molecular differences between chondrocytes from osteophytic and articular cartilage tissue from OA patients.We investigated genes and pathways by combining genome-wide DNA methylation, RNA sequencing and quantitative proteomics in isolated primary chondrocytes from the cartilaginous layer of osteophytes and matched areas of low- and high-grade articular cartilage across nine patients with OA undergoing hip replacement surgery.ALDH1A2 and cartilage oligomeric matrix protein, which have functional genetic variants associated with OA from genome-wide association studies. An integrative gene ontology enrichment analysis showed that differences between osteophytic and low-grade articular cartilage chondrocytes are associated with extracellular matrix organization, skeletal system development, platelet aggregation and regulation of ERK1 and ERK2 cascade.Chondrocytes from osteophytic cartilage showed widespread differences to low-grade articular cartilage chondrocytes. These differences were similar to, but more pronounced than, differences between chondrocytes from osteophytic and high-grade articular cartilage, and more pronounced than differences between high- and low-grade articular cartilage. We identified 56 genes with significant differences between osteophytic chondrocytes and low-grade articular cartilage chondrocytes on all three omics levels. Several of these genes have known roles in OA, including We present a first comprehensive view of the molecular landscape of chondrocytes from osteophytic cartilage as compared with articular cartilage chondrocytes from the same joints in OA. We found robust changes at genes relevant to chondrocyte function, providing insight into biological processes involved in osteophyte development and thus OA progression. Significant cross-omics differences between osteophytic and low-grade articular chondrocytes identified for 56 genes.ALDH1A2 and COMP; both contain genetic variants associated with osteoarthritis.Genes with cross-omics differences include OA is a degenerative joint disease characterized clinically by pain and loss of physical function . It is vA key feature of OA is cartilage degeneration, and several genomics studies have investigated the molecular characteristics of this process (e.g. reviewed in ). HoweveA previous study of osteophytic cartilage in the knee investigated gene expression using a microarray and suggested large differences compared with ‘macroscopically intact’ articular cartilage . The autIn this proof-of-concept study, we report the first analysis of hip OA patient tissue using integrated multi-omics across genome-wide DNA methylation, RNA sequencing and quantitative proteomics to obtain a molecular portrait of chondrocytes from the cartilaginous layer of osteophytes. We identify key molecular players linked to this aspect of the disease and its pathogenesis.Rheumatology online.More details, including references, are given in the Rheumatology online). In each zone, a cartilage sample was removed, with subsequent extraction of DNA, RNA and protein. Cartilage from all structural layers was obtained, with care taken to avoid the removal of non-cartilage tissue. Details for histological examination, chondrocyte preparation, as well as extraction of DNA, RNA and protein are described in the Rheumatology online.The study complies with the Declaration of Helsinki. Tissue samples were collected under National Research Ethics approval reference 11/EE/0011, Cambridge Biomedical Research Centre Human Research Tissue Bank, Cambridge University Hospitals, UK. Three samples each were collected from nine patients  undergoing hip joint replacement surgery for OA. All patients provided written informed consent before participation. Cartilage tissue was classified macroscopically for each femoral head as: low-grade, with a smooth surface and no obvious evidence of damage or fibrillation; high-grade, with damaged and fibrillated cartilage; and osteophytic, from the cartilaginous layer of osteophytes located mainly around the margins of the articular surface (sample extraction section of the Rheumatology online.Liquid chromatography -mass spectrometry (LC-MS) analysis was performed on the Dionex Ultimate 3000 UHPLC system coupled with the Orbitrap Fusion Tribrid Mass Spectrometer . Abundance values were normalized by the sum of all protein abundances in a given sample, then log2-transformed and quantile normalized. No protein was detected in only osteophytic or only articular chondrocytes. Hence we restricted the analysis to 4653 proteins that were quantified in all individuals and tissues. Details for sample processing, LC-MS analysis, protein identification and quantification are described in the Rheumatology online.Multiplexed libraries were sequenced on Illumina HiSeq 2000  and a cram file was produced for each sample. We obtained transcript-level quantification using salmon 0.7.2 [Rheumatology online.Methylation was assayed using the Illumina 450k BeadChip . The resulting idat files were parsed and QCed using ChAMP  in R, yiThe proteomics, gene expression, and probe methylation differential analyses were carried out using limma  in R. WePrincipal component analyses were carried out using the prcomp function in R for each omics level, based on significant differences between osteophytic and low-grade cartilage at 0.1% FDR.To map Ensembl gene IDs to gene names and vice versa, we used the assignment in Ensembl . We only included instances where a unique Ensembl gene ID corresponded to a unique gene name.Rheumatology online.We applied MAGMA v1.06  to test P-values and the lowest SNP P-values, as recommended by MAGMA. Linkage disequilibrium (LD) was calculated from a subset of the UK Biobank samples (see Rheumatology online). We then tested whether the 56 genes are more associated with OA than expected by chance, correcting for the potentially confounding effects of sample size, gene size, gene density and the inverse of the mean minor allele count in the gene, as well as the log of these variables, as recommended.Each gene was assigned the single nucleotide polymorphisms (SNPs) located between the gene’s start and stop sites based on NCBI 37.3 gene definitions. For each gene, we used the combined statistic based on the sum of SNP log-Rheumatology online. Briefly, enrichment of each annotation for each of the three omics levels was assessed using a one-sided hypergeometric test, the P-values integrated across the three omics levels followed by randomizations. Significance was defined at 5% FDR. We excluded annotations that were enriched in only one of the omics levels, or where fewer than five genes contributed to the enrichment on at least two omics levels.Gene Ontology (GO)  biologicRheumatology online), in gene expression for 3601 of 14 029 genes (Rheumatology online), and 3161 DMRs that overlapped 3277 genes (Rheumatology online).First, we examined genome-wide methylation, gene expression and protein abundance differences between osteophytic and low-grade articular cartilage. At each molecular level, we found widespread differences at 0.1% FDR. In particular, we found significant differences in protein abundance for 942 of 4653 proteins (Rheumatology online), gene expression differences for 1512 of 14 029 genes (Rheumatology online) and 1113 DMRs overlapping 1113 genes (Rheumatology online). However, globally, the gene expression differences between osteophytic and high-grade cartilage were similar to the differences between osteophytic and low-grade articular cartilage , only one gene with differential RNA expression (Rheumatology online), and no differentially methylated regions.At any given level of significance, we identified far more significant differences in the comparison of osteophytic and low-grade articular cartilage than in the comparison of high- and low-grade articular cartilage. For example, at 0.1% FDR, we found no protein with differential abundance between high- and low-grade articular cartilage (P < 10−15). The direction of difference (increase or decrease) in osteophytic compared with low-grade articular chondrocytes agrees with the direction of difference in high-grade compared with low-grade articular chondrocytes for 74% of genes on mRNA level and for 66% of genes on protein level.The differences observed between osteophytic and low-grade cartilage are significantly correlated with the differences found between high- and low-grade articular cartilage . However, the magnitude of the log-fold-differences between the high- and low-grade articular chondrocytes is smaller .In agreement with this, the vast majority of genes with significant mRNA or protein level differences between osteophytic and low-grade articular cartilage also show the same direction of difference in high-grade compared with low-grade articular cartilage . We identified 309 genes with significant differences on both RNA and protein level at 0.1% FDR, 88.7% of these differences were directionally concordant .There was a significant positive correlation of the differences in gene expression and protein abundance identified between osteophytic and low-grade articular cartilage in the proteomics and the RNA sequencing data (P < 10−10). For all 56 genes, the direction of difference between osteophytic and high-grade articular chondrocytes is the same as the direction of difference between osteophytic and low-grade articular chondrocytes, and 42 genes also have evidence for difference between osteophytic and high-grade articular chondrocytes across all three molecular levels at 5% FDR or lower  missense variant in the gene has been found to significantly increase the risk of OA in a study of hip OA patients who underwent joint replacement surgery [ surgery .ALDH1A2 also displays significantly reduced gene and protein levels in osteophytic compared with low-grade articular cartilage, and is located in a hyper-methylated region. Several genetic variants in and close to ALDH1A2 have been associated with severe hand OA [ALDH1A2 gene expression, and another associated variant has also been associated with allelic imbalance in ALDH1A2 gene expression [ hand OA . The mospression .P = 0.2116 using MAGMA, see Methods section).We further tested the joint association of all 56 genes with susceptibility to OA using an unpublished dataset from UK Biobank . There was no significant excess of genetic association in the 56 genes together (Rheumatology online). The most significant annotations , we identified 36 GO annotations as significantly associated with the molecular changes at 5% FDR . This includes TF and ALDH1A2, which were identified as significantly different between osteophytic and low-grade articular cartilage on all three omics levels in this study.A previous study  examinedThis study provides a systematic molecular characterization of osteophytic chondrocytes in OA across genome-wide methylation, gene and protein expression levels. We have shown widespread molecular differences between chondrocytes from osteophytic and low-grade articular OA chondrocytes (as previously seen for gene expression in the knee), with similar but smaller differences between osteophytic and high-grade articular OA chondrocytes. By contrast, there were far fewer significant differences between chondrocytes from high- and low-grade articular cartilage for any given FDR. In a direct comparison, we have shown that the differences between osteophytic chondrocytes and those from low-grade cartilage are positively correlated with the differences between chondrocytes from high- and low-grade articular cartilage. The correlation between these differences is observed despite the morphological and histological dissimilarities between osteophytic and articular tissues. One interpretation would be that, although both tissues are subject to the disease process and the altered internal joint milieu , osteophTF), MMP13 and ALDH1A2 have also been previously identified in the knee [TF (reduced mRNA and protein levels in osteophytic chondrocytes) is known to be produced in hypertrophic cartilage as a pro-angiogenic molecule [MMP13 (increased mRNA and protein levels in osteophytic chondrocytes) is a marker of chondrocyte hypertrophy considered important in cartilage degeneration [ALDH1A2 is known to have genetic links to OA, discussed in detail below. The 56 genes with significant differences across omics levels also include CILP (reduced mRNA and protein levels in osteophytic chondrocytes), which was previously found to be down-regulated in mechanically induced OA in mice [IL6 (increased mRNA and protein levels in osteophytic chondrocytes), which has been localized to chondroblasts and preosteoblasts in human osteophytes during endochondral ossification [Of the 56 genes with differences between osteophytic and low-grade articular cartilage on all three molecular levels, gene expression changes in Transferrin  is an enzyme that catalyses the synthesis of retinoic acid, the active derivative of vitamin A (retinol). Vitamin A is involved in post-natal bone health and bone remodelling, with both high and low levels having negative effects [COMP is a constituent of the cartilage matrix, present in the interterritorial matrix and is involved in collagen fibrillogenesis [ALDH1A2 and a missense variant in COMP). We did not find such cross-omics significant changes of ALDH1A2 or COMP between low- and high-grade articular cartilage (with only protein-level changes ALDH1A2 significant at 5% FDR), which could suggest that their action is stronger in osteophytic cartilage, or could be due to the limited power in this discovery study. However, we have replicated gene expression changes of ALDH1A2 in osteophytic cartilage using independent data. Interestingly, the genetic association of ALDH1A2 was identified in severe hand OA [Notably, the 56 genes with differences between chondrocytes from osteophytic and low-grade articular cartilage on all three molecular levels also include two genes that harbor genetic variants robustly associated with OA identified from genome wide association studies,  effects . COMP isogenesis . Notably hand OA , which iUsing UK Biobank data, we did not find evidence for association of the joint set of the 56 genes with differences between chondrocytes from osteophytic and low-grade articular cartilage on all three molecular levels. It is possible that some of these genes exhibit molecular changes as a consequence rather than cause of the disease, or are involved in OA progression rather than incidence. The lack of association could also be due to still limited sample size of the genetic data. Larger cohorts will be required to determine the comprehensive set of genetic variants associated with OA, as well as which molecular changes are causal to disease processes.In the cross-omics GO analysis, several of the gene annotations with highly significant associations have known links to OA: changes in the extracellular matrix, collagen catabolism, inflammation and activity of the ERK cascade are all known interrelated processes taking place in the OA joint . The ERKTGFβ, PTH and IGF1 [We did not find cross-omics differences between osteophytic and articular cartilage in previously reported osteophyte development genes such as and IGF1 . This coThe major strengths of this study are the integration of DNA methylation, gene expression and proteomics data, for a comprehensive overview of the changes across molecular levels, and the precise matching of osteophytic, low- and high-grade articular cartilage samples from the same joint. The latter reduces the possibility of false-positives due to biological differences between individuals. This approach has helped illuminate the molecular basis of OA progression; tissue from healthy individuals and early OA stages would be required to characterize the onset of the disease. The main limitation of this study is the size of the cohort examined here. As such, this study is a proof-of-concept discovery study, and replication in larger independent datasets will be required. The deposition of all data in open repositories also allows the data to be combined with other datasets in the future.As noted above, the molecular associations identified may be a result of, rather than causal to the disease processes. Nonetheless, they can provide insights into characteristics of osteophytic chondrocytes and into disease progression, and suggest targets for further functional follow-up with translational potential.In summary, we present the first integrative methylation, gene expression and proteomics study across osteophytic and articular cartilage in hip OA. We have identified multiple genes with significant cross-omics changes between osteophytic and low-grade articular cartilage, including two genes associated with OA through genome-wide association studies.These findings offer evidence that the study of osteophytic cartilage can provide distinct insight into OA pathogenesis.Supplementary DataClick here for additional data file.


An imbalance of iron metabolism with consecutive aggregation of α-synuclein and axonal degeneration of neurons has been postulated as the main pathological feature in the development of Parkinson’s disease (PD). Quantitative susceptibility mapping (QSM) is a new imaging technique, which enables to measure structural changes caused by defective iron deposition in parkinsonian brains. Due to its novelty, its potential as a new imaging technique remains elusive for disease-specific characterization of motor and non-motor symptoms . Functional network changes associated with these symptoms are however frequently described for both magnetoencephalography (MEG) and resting state functional magnetic imaging (rs-fMRI). Here, we performed a systematic review of the current literature about QSM imaging, MEG and rs-fMRI in order to collect existing data about structural and functional changes caused by motor and non-motor symptoms in PD. Whereas all three techniques provide an effect in the motor domain, the understanding of network changes caused by non-motor symptoms is much more lacking for MEG and rs-fMRI, and does not yet really exist for QSM imaging. In order to better understand the influence of pathological iron distribution onto the functional outcome, whole-brain QSM analyses should be integrated in functional analyses , to enable a proper pathophysiological interpretation of MEG and rs-fMRI network changes in PD. Herewith, a better understanding of the relationship between neuropathological changes, functional network changes and clinical phenotype might become possible. Technically, QSM solves the deconvolution or inverse problem from a magnetic field to susceptibility source and thus maps the magnetic property of local tissue  is a new technique for quantifying magnetic susceptibility  and functional  imaging techniques may enable a more comprehensive view onto PD reflecting structural disease patterns and functional network changes characteristic for motor and non-motor subtypes in PD. These characteristic, disease-related changes measured by the proposed three imaging techniques might help to better classify individual patients with regards to their individual phenotypes. A better classification might then open the possibility for better prediction of disease course and more accurate treatment for the individual patient.Medical electronic search engine (PubMed) was used to collect studies using QSM, MEG and rs-fMRI in patients suffering from PD . Mainly articles published after the year 2000 were included in the reviewing process; a few important articles, however, date back to the 1990s. The keywords used were: (Parkinson’s disease OR Parkinson disease OR parkinsonian), (quantitative susceptibility mapping OR QSM), , . We listed all original studies in English that were retrieved with the search string.3 in the right and 164 mm3 in the left midbrain, whereas the total volume of significant R2* change was 62 mm3, distributed only in the left region of the SNc. In the ROI analysis, the QSM values and R2* values were significantly higher than in the control group with QSM being more sensitive. QSM values additionally correlated with disease duration, levodopa equivalent daily dosage (LEDD) and the UPDRS II. Acosta-Cabronero et al.  ROI- analyses of basal ganglia regions (mainly comprising the region of the SN) with correlations to the motor domain; (2) only two studies found correlations with non-motor symptoms ; (3) one whole-brain analysis of magnetic susceptibility perturbations in PD exists; (4) QSM is applied for the improved visualization of the STN and the GP.MEG is a non-invasive modality for assessing cortical oscillatory activities with high temporal and spatial resolution; it mirrors the summed up excitatory postsynaptic potentials of neuronal populations  during resting state modality as well as during isometric contraction compared to controls. The power of the contralateral hemisphere was significantly suppressed during isometric contraction in healthy controls. By contrast, in de novo patients both hemispheres were equally strongly activated. In medicated patients, the pattern was found to be reversed with a decrease of synchronized oscillatory activity within sensori-motor loops at beta-frequency. Contralateral beta power was significantly correlated with motor impairment during isometric contraction but not during rest. Pollok et al.  had lower parahippocampal and temporal delta band connectivity and higher temporal alpha1 band connectivity compared to controls. In a larger patient group (n = 43), longitudinal analyses over a 4-year period uncovered decreases in alpha1 and alpha2 band connectivity for multiple seed regions that were associated with motor or cognitive decline . Ponsen et al. . Heinrichs-Graham et al.  In PD, we can find a slowing of resting state oscillatory activity compared to controls, which increases during disease progression and is especially enhanced during cognitive decline. (2) The increased synchronization of beta-band activity is a pathognomic feature for the deficient motor action in PD and administration of dopamine decreases beta-band synchronicity with an improvement of motor output. (3) Methodologically, a combination of whole-head-MEG and basal ganglia LFP recordings are nowadays possible.via functional MRI  a primary somato-motor network; (2) default mode network (DMN); (3) the left and right parietal-frontal network; (4) the salience network; and (5) a primary visual area and extra-striate visual network.via FC analysis. They investigated the interplay between the rostral SMA  and the primary motor cortex  by choosing these two regions as seed regions. Without dopaminergic medication, connectivity within the pre-SMA in patients with PD compared to healthy controls was increased to the right M1 and decreased to the left PUT, right insula, right premotor cortex and left inferior parietal lobule. Stronger connectivity was only found in M1 within its own local region in PD patients compared to controls. In contrast to these findings, Yu et al. , Agosta et al.  comparedSharman et al.  found inBell et al.  also fouKwak et al.  applied n = 31) after dopaminergic withdrawal with healthy controls (n = 44). The analysis revealed increased FC between the STN and cortical motor areas  in line with electrophysiological studies. FC analysis in the M1 hand area elucidated that the FC increase was primarily found in the STN area within the BG, suggesting that increased STN-motor cortex synchronicity mediated via the so-called hyperdirect motor cortex-subthalamic pathway might play a crucial role in the pathophysiology of PD. Increased FC between these two areas was also found in early drug-naïve PD patients before application of dopamine  is involved in the “top down” control of executive control  and dorsal anterior cingulate cortex  abnormal signaling in the SMA; (2) decreased functional connectivity between the striatum and cortical regions; (3) abnormal STN-motor-cortex synchronicity resulting in motor impairment, like bradykinesia. Functional connectivity changes in the (4) DMN are frequently found not only in cognitively impaired PD patients, but also in patients with FOG and depression. Fatigue, but also cognitive decline, are caused by impaired functional connectivity of the; (5) salience network. In addition to connectivity changes within the DMN, significant decreases of connectivity; and (6) in the left and right inferior occipital gyrus appear in patients with PDD  in this review article. FC only refers to the statistical interdependence of the signal from different areas  should be seen as an idea/starting point but does still not tell the whole truth of the underlying network. The interpretation of combinations between the different connectome analyses are challenging and are part of actual discussions, see e.g., Uddin , Wang anIn this review article, we present three different imaging entities, which enable a view into structural and functional abnormalities in PD patients: (1) QSM imaging; (2) MEG; and (3) rs-fMRI. All described methods have a high potential to analyze pathophysiological changes in PD networks with a different accentuation of network changes in both the motor and non-motor domain, as summed up exemplary in As can be seen in the different tables, the most challenging factor for the prediction of the parkinsonian phenotype is a proper control of the heterogeneity of parkinsonian symptoms. In all studies we found: (i) PD-independent factors ; (ii) PD-characteristic factors like the existence of (a) non-motor symptoms  and non-motor symptoms  at cortical level. The whole-brain analyses of (3) rs-fMRI build a bridge between QSM imaging and MEG to combine subcortical and cortical network changes. Rs-fMRI, as already mentioned above, has a high impact to improve our understanding of global network changes, especially for the non-motor domain; but, it has also obvious limitations, as it is neither able to give information about the underlying structure nor the direction, how different areas are influenced by each other. Due to its importance, we point to the section “Limitations of Functional rsMRI” (see above), which should not be neglected in the context of rs-fMRI network analyses.Despite these challenges, a combination of structural (QSM) and functional data  might allow a further classification and a possible prediction of the parkinsonian phenotypes as exemplarily proposed for the impaired motor network in One additional fact has to be mentioned: whereas MEG has the ability to measure activity changes in different networks with a high temporal resolution , rs-fMRI shows changes in different brain networks with a high spatial resolution . However, despite this very high spatial resolution, it measures BOLD changes, which are the hemodynamic, indirect, answers to brain activity; hence the temporal resolution of rs-fMRI is low. Herewith the combination of rs-fMRI and MEG analyses seems reasonable to close the gap between temporal and spatial resolution in order to capture the full disease pathology. QSM imaging might herewith give an additional answer of the structural disease pathology underlying these functional changes , the role of QSM at the cortical level for non-motor symptom studies is still questionable. In most of the QSM imaging studies using the regularization method [except for via a combination of structure and function. The further classification of parkinsonian phenotypes could provide insights into different disease patterns and courses, which might open the way to new therapeutic strategies to reduce parkinsonian symptoms.QSM imaging is a new method that is applicable to detect pathophysiological changes in the parkinsonian brain, which rely on iron accumulation indirectly interfering with the α-synuclein pathology. Together with functional measurements (like rsMRI and MEG) QSM imaging might open the possibility to better classify different parkinsonian phenotypes (including both the motor and non-motor domain) EP: substantial contributions to the conception and design of the work. Additionally, substantial contribution to the acquisition, analysis and interpretation of data for the work. EF: critically revising the manuscript and providing additional information in the MEG section. AS: drafting the work and revising it critically for important intellectual content.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Phoca hispida saimensis). Photo-ID data based on the unique pelage patterns of individuals were collected by means of camera traps and boat-based surveys during the molting season in two of the species’ main breeding areas, over a period of five years in the Pihlajavesi basin and eight years in the Haukivesi basin. An open model approach provided minimum population estimates for these two basins. The results indicated high survival rates and site fidelity among the adult seals. More accurate estimates can be obtained in the future by increasing the surveying effort both spatially and temporally. The method presented here proved effective for evaluating population size objectively, whereas the results of the current snow lair censuses are dependent on varying winter conditions, for instance. We therefore suggest that a photo-ID-based non-invasive mark-recapture method should be used for estimating Saimaa ringed seal abundances in order to ensure reliable, transparent population monitoring under changing climatic conditions.Reliable population estimates are fundamental to the conservation of endangered species. We evaluate here the use of photo-identification (photo-ID) and mark-recapture techniques for estimating the population size of the endangered Saimaa ringed seal ( Phoca hispida) is one of the most dependent of all the pinnipeds on ice and snow for its breeding, resting, and molting. In addition, estimates of its abundance are dependent on the ice platform, because censuses are typically carried out during the ice-cover season, in early spring. This means that the mild winters brought about by climate change may not only hamper the ringed seals’ breeding success ) success , but may success .P.h. saimensis) in Lake Saimaa, are the negative impacts of climate change, e.g. reduction of the number of available breeding habitats due to a decline in the ice cover and protective snow cover )2) )The photo-ID method is systematic and reliable, but statistical mark-recapture models entail assumptions that need to be taken into account when interpreting the results. When considering the assumption behind the POPAN model, photo-ID, as a “mark-recapture method”, enables sampling to be instantaneous, after which “the animals are released immediately”. The observation season is relatively short  compared with a one-year occasion interval and observation can therefore be regarded as instantaneous. Moreover, there is no possibility that photo-ID could affect the behavior or survival rate of the seals, since our sampling method is non-invasive. As the pups are not included in the data, the older individuals should have the same survival probability, because the survival rate is known to stabilize after the first critical year of life , 61, or The data collection methods used here differ in their re-capture probabilities, as camera trapping is a passive method that captures the same local individuals many times, while the boat-based survey method as an active method may capture different individuals better also on the edge areas. For these reasons, individual seals in our study areas have unequal capture probabilities to some an unknown extent, and this will introduce individual heterogeneity into the data, which is known to affect both population parameter estimates and their uncertainty estimates . A simulAs a statistically significant transient effect was observed, this should be taken into account in future population models when surveying the whole Lake Saimaa basin since it has been pointed out that sub-adult seals may move to the edges of the breeding areas, which would support this transient effect . This efOur recommendation is to use of both camera traps and boat-based surveys when collecting image data for mark-recapture analyses. Combining these datasets provides more identified individuals and re-sightings than either dataset alone, and will increase re-capture probability and thereby improve the accuracy of the population estimates. Both data collection methods entail both benefits and weaknesses. Camera traps provide a large amount of data to handle and are more labor-intensive than boat-based surveys, since the memory card needs to be changed regularly. Camera trapping also requires more knowledge of both seals and game cameras, whereas boat-based surveys can also be carried out by casual members of the public. Our results obtained for Haukivesi show that even a low annual boat-based survey effort with long enough study period (sufficient amount of time points) can be useful for photo-ID purposes and can provide a mark-recapture estimate. Camera trapping may favor individuals with high site fidelity, whereas boat-based surveys will also allow us to capture those seals that do not return to the same haul-out site after the first occurrence. Camera traps provide high quality close-up images that are ideal for identification purposes and they also record other valuable data such as sex, age class, social interaction and behavioral information , whereasAlthough our mark-recapture population estimates are quite the same magnitude as the official seal numbers for the Pihlajavesi and Haukivesi basins produced by the currently used lair census method , they arIt is significant that the current lair census method  requiresClimate change constitutes a major challenge for the monitoring and conservation of the Saimaa ringed seal e.g., –7). Our . Our 7])S1 TableIndividuals re-sighted using the camera traps, boat-based survey and their combination in A) Pihlajavesi basin (2013–2017), B) Haukivesi basin (2010–2012) and C) based only on the boat-based survey in Haukivesi basin (2010–2017).(DOCX)Click here for additional data file.S2 TableIndividuals over one-year-old photo-identified in the Pihlajavesi basin  and the Haukivesi basin . Separate results are presented for the various models and for the two observation methods and their combination.(DOCX)Click here for additional data file.S3 TableSeparate results are presented for 1A) the camera traps, 2A) the boat-based surveys and 3A) their combined data in Pihlajavesi (2013–2017) and for the 1B) camera traps (2010–2013) and 2B) boat-based surveys (2010–2017) in Haukivesi. Parameters marked with grey are reliably estimated. Standard errors  and 95% confidence intervals are presented (derived from Beta parameter estimates with inverse logit link).(DOCX)Click here for additional data file.