Whole Exome Sequencing (WES) is the application of the next-generation technology to determine the variations in the exome and is becoming a standard approach in studying genetic variants in diseases. Understanding the exomes of individuals at single base resolution allows the identification of actionable mutations for disease treatment and management. WES technologies have shifted the bottleneck in experimental data production to computationally intensive informatics-based data analysis. Novel computational tools and methods have been developed to analyze and interpret WES data. Here, we review some of the current tools that are being used to analyze WES data. These tools range from the alignment of raw sequencing reads all the way to linking variants to actionable therapeutics. Strengths and weaknesses of each tool are discussed for the purpose of helping researchers make more informative decisions on selecting the best tools to analyze their WES data.  Recent advances in next-generation sequencing technologies provide revolutionary opportunities to characterize the genomic landscapes of individuals at single base resolution for identifying actionable mutations for disease treatment and management , 2. WholGenomic studies that employ WES have increased over the years, and new bioinformatics methods and computational tools have developed to assist the analysis and interpretation of this data . The majComputational tools developed to align raw sequencing data to an annotated VCF file have been well established. Most studies tend to follow workflows associated with GATK –6, SAMtoBioinformatics methods developed beyond the establishment of annotated VCF files are far less established. In cancer research, the most established types of beyond VCF tools are focused on the detection of somatic mutations. However, there are strides being made to develop other computational tools including pathway analysis, copy number alteration, INDEL identification, driver mutation predictions, and linking candidate genes to clinical data and actionable targets.Here, we will review recent computational tools in the analysis and interpretation of WES data, with special focus on the applications of these methods in cancer research. We have surveyed the current trends in next-generation sequencing analysis tools and compared their methodology so that researches can better determine which tools are the best for their WES study and the advancement of precision medicine. In addition, we include a list of publicly available bioinformatics and computational tools as a reference for WES studies .Alignment, removal of duplicates, variant calling, annotation, filtration, and prediction are all parts of the steps leading up to the generation of a filtered and annotated VCF file. Here we review each one of these steps, as shown in http://www.novocraft.com/), SOAP 1 [The first step in any analysis of next-generation sequencing is to align the sequencing reads to a reference genome. The two most common reference genomes for humans currently are hg18 and hg19. Several aligning algorithms have been developed including but not limited to BWA , GEM 1111, ELAND, SOAP 1  and 2 [1, SOAP 1 , SSAHA [, SOAP 1 , Stampy , SOAP 1 –22, and http://picard.sourceforge.net./), FastUniq [ de novo approach to quickly identify PCR duplicates. FastUniq imports all reads, sorts them according to their location, and then marks duplicates. This allows FastUniq not to require complete genome sequences as prerequisites. Due to the different algorithms each of these tools use, these tools can remove PCR duplicates individually or in combination.Some auxiliary tools have been developed to filter aligned reads to ensure higher quality data for downstream analyses. PCR amplification can introduce duplicate reads of paired-end reads in sequencing data. These duplicate reads can influence the depth of the mapped reads and downstream analyses. For example, if a variant is detected in duplicate reads, the proportion of reads containing a variant could pass the threshold needed for variant calling, thus calling a falsely positive variant. Therefore, removing duplicate reads is a crucial step in accurately representing the sequencing depth during downstream analyses. Several tools have been developed to detect PCR duplicates including Picard  such as insertions and deletions (INDELs) in high-throughput sequencing data are more challenging to identify than single nucleotide variants because they could involve an undefined number of nucleotides. The majority of WES studies follow SAMtools  or GATK  de novo assembly. When compared to SAMtools and GATK, Platypus had the lowest Fosmid false discovery rate for both SNVs and INDELs in whole genome sequencing of 15 samples. It also had the shortest runtime of these tools. However, GATK and SAMtools had lower Fosmid false discovery rates than Platypus when finding SNVs and INDELs in WES data [Platypus  was deveWES data . TherefoFreeBayes uses a unique approach to INDEL detection compared to other tools. The method utilizes haplotype-based variant detection under a Bayesian statistics framework . This mePindel was one of the first programs developed to address the issue of unidentified large INDELs due to the short length of WGS reads . In brieSplitread  was deve de novo assembly to determine INDELs from paired-end reads of WGS data [Recently developed indelMINER is a compilation of tools that takes the strengths of split-read andWGS data . CompariWGS data . SAMtoolWGS data . TherefoF-score, Sprites performed better than Pindel using real and simulated data [Most of the recent SV detection tools rely on realigning split-reads for detecting deletions. Instead of a more universal approach like indelMINER, Sprites  aims to ted data .All of these tools use different algorithms to address the problem of structural variants, which are common in human genomes. Each of these tools has strengths and weaknesses in detecting SVs. Therefore, it is suggested to use several of these tools in combination to detect SVs in WES.Once the variants are detected and called, the next step is to annotate these variants. The two most popular VCF annotation tools are ANNOVAR  and MuTeDuring the annotation process, many resources and databases could be used as filtering criteria for detecting novel variants from common polymorphisms. These databases score a variant by its minor allelic frequency (MAF) within a specific population or study. The need for filtration of variants based on this number is subject to the purpose of the study. For example, Mendelian studies would be interested in including common SNVs while cancer studies usually focus on rare variants found in less than 1% of the population. NCBI dbSNP database, established in 2001, is an evolving database containing both well-known and rare variants from many organisms . dbSNP aThe Leiden Open Variation Database (LOVD) developed in 2005 links its database to several other repositories so that the user can make comparisons and gain further information . One of Besides knowing if a particular variant has been previously identified, researchers may also want to determine the effect of a variant. Many functional prediction tools have been developed that all vary slightly in their algorithms. While individual prediction software can be used, ANNOVAR provides users with scores from several different functional predictors including SIFT, PolyPhen-2, LRT, FATHMM, MetaSVM, MetaLR, VEST, and CADD .SIFT determines if a variant is deleterious using PSI-BLAST to determine conservation of amino acids based on closely related sequence alignments . PolyPheMetaSVM and MetaLR represent two ensemble methods that combine 10 predictor scores  and the maximum frequency observed in the 1000 genomes populations for predicting the deleterious variants . MetaSVMThe Variant Effect Scoring Tool (VEST) is similar to MetaSVM and MetaLR in that it uses a training set and machine learning to predict functionality of mutations . The maiWhile all of these methods predict the functionality of a mutation, they all vary slightly in their methodological and biological assumptions. Dong et al. have recently tested the performance of these prediction algorithms on known datasets . They poAfter a VCF file has been generated, annotated, and filtered, there are several types of analyses that can be performed . Here weAfter VCF annotation, a WES sample can have thousands of SNVs identified; however, most of them will be silent (synonymous) mutations and will not be meaningful for follow-up study. Therefore, it is important to identify significant somatic mutations from these variants. Several tools have been developed to do this task for the analysis of cancer WES data, including SomaticSniper , MuTect SomaticSniper is a computational program that compares the normal and tumor samples to find out which mutations are unique to the tumor sample, hence predicted as somatic mutations . SomaticAnother popular somatic mutation identification tool is MuTect , developWhile SomaticSniper and MuTect require data from both paired cancer and normal samples, VarSim  and SomV2 of the ratio of tumor depth to normal depth for each region [One active research area in WES data analysis is the development of computational methods for estimating copy number alterations (CNAs). Many tools have been developed for estimating CNAs from WES data based on paired normal-tumor samples such as CNV-seq , SegSeq h region . Lastly,h region  is utilih region .Recently, ExomeAI was developed to detect Allelic Imbalance (AI) from WES data . UtiliziA systematic evaluation of somatic copy number estimation tools for WES data has been recently published . In thisCancer is a disease driven by genetic variations and copy number alterations. These genetic events can be classified into two classes, “driver” and “passenger” mutations. Driver mutations are the key mutation that drive the development of cancer and provide a survival advantage, whereas passenger mutations are “by-stander” alterations that happen to be altered in the primary cells but do not provide a survival advantage. As the cancer exomes tend to have high mutational burdens, identifying the “driver” mutations from the “passenger” mutations is one of the key analyses in cancer research. Several tools have been developed to find driver mutations including but not limited to CHASM , DendrixCHASM (Cancer-specific High-throughput Annotation of Somatic Mutations) uses random forest as the machine learning approach to distinguish the difference between driver and passenger mutations in cancer . CHASM w TTN) have been falsely reported as being related to cancer when in fact their large size just makes the probability they would be mutated by chance increase [Another common driver mutation tool is MutSigCV developed to resolve the problem of extensive false-positive findings that overshadow true driver mutations . As the increase . MutSigCDe novo Driver Exclusivity (Dendrix) is a novel computational tool to determine de novo driver pathways (gene sets) from somatic mutations in patient data [ent data . The maiAfter candidate somatic mutations have been identified; one common type of analysis is to determine which pathways are affected by these mutations. Common pathway resources and tools used for these types of analysis include KEGG , DAVID , STRING KEGG represents one of the most popular databases for pathway analysis. DAVID is a popular online tool for performing functional enrichment analysis based on user defined gene lists. STRING is the largest protein-protein interactions database for querying and searching for interactions between user defined gene lists. BEReX integrates STRING, KEGG, and other data sources to explore biomedical interactions between genes, drugs, pathways, and diseases. Both STRING and BEReX allow users to perform functional enrichment analysis and the flexibility to explore the interactions between user defined gene lists by expanding the networks.DAPPLE  uses literature reported protein-protein interactions to identify significant physical connectivity among the genes of interest . DAPPLE https://www.mycancergenome.org/). My Cancer Genome provides a bridge between genomic data and clinical therapeutic treatments. Similarly, ClinVar provides information on the relationship between variants and clinical therapy [The ability to link variants with actionable drug targets is an emerging research topic in precision medicine. Databases such as My Cancer Genome have provided the framework for these studies  and molecular analysis, this remains one of the challenges in translating WES data analysis into clinical practice. Projects such as cBioPortal provide a framework for incorporating sequencing data with available clinical data . New metWES data analysis pipelines integrate computational tools and methods described in the previous sections in a single analysis workflow. Here, we review three recent sequencing pipelines SeqMule , Fastq2vSeqMule stands out in part due to the use of five alignment tools  and five different variant calling algorithms  . SeqMuleBoth SeqMule and Fastq2vcf pipelines focus on taking raw sequencing data and converting it into a filtered VCF file. IMPACT (Integrating Molecular Profiles with ACtionable Therapeutics) WES data analysis pipeline was developed to take this analysis a step further by linking a filtered VCF to actionable therapeutics . The IMPTo compare the strengths and weaknesses between these three WES pipelines, SeqMule allows the use of different alignment algorithms in its pipeline whereas IMPACT and Fastq2vcf only utilize BWA as the sequencing alignment algorithm. SAMtools is the common tool used by IMPACT, Fastq2vcf, and SeqMule to call variants. In addition, Fastq2vcf and SeqMule employ GATK and other variant calling algorithms for variants detection. Fastq2vcf and IMPACT both annotate the variants with ANNOVAR. Fastq2vcf also utilizes VEP and IMPACT utilizes SIFT and PolyPhen-2 as the primary variants prediction methods. For Post-VCF analysis, IMPACT pipeline has more options as compared to SeqMule and Fastq2vcf. In particular, IMPACT performs copy number analysis, tumor heterogeneity, and linking of actionable therapeutics to the molecular profiles. However, IMPACT is only designed to be performed on tumor samples while SeqMule and Fastq2vcf are designed for any WES dataset. Therefore, it is advisable for the users to consider the analytic needs to select the appropriate WES data analysis pipeline for their research.As recently discussed by Altman et al., part of the U.S. Precision Medicine Initiative (PMI) includes being able to define a gold standard of pipelines and tools for specific sequencing studies to enable a new era of medicine . AutomatIn summary, we have reviewed several computational tools for the analysis and interpretation of WES data. These computational methods were developed to generate VCF files from raw sequencing data, as well as tools that perform downstream analyses in WES studies. Each tool has specific strengths and weaknesses, and it appears that using several of them in combination would lead to more accurate results. Currently, there are still challenges for bioinformaticians at every step in analyzing WES data. However, the greatest area of need is in the development of tools that can link the information found in a VCF file to clinical databases and therapeutics. Research in this area will help to advance precision medicine by providing user-friendly and informative knowledge to transcend the laboratory.


Many instances of large-scale coordination occur in real-life social situations without the explicit awareness of the individuals involved. While the majority of research to date has examined dyadic interactions – those between two individuals – during intentional or deliberate coordination, the present review surveys the handful of recent studies investigating behavioral and physiological synchrony across groups of more than two people when coordination was not an explicit goal. Both minimal  and naturalistic  group interactions appear to promote unintentional group synchrony although they have so far only been studied separately. State differences in unintentional group synchrony, or the relative presence of coordination in various conditions, have tended to be assessed differently, such as using correlation-type relationships, compared to its temporal dynamics, or changes over time in the degree of coordination, which appear to be best captured using phase differences. Simultaneously evaluating behavioral, physiological, and social responses as well systematically comparing different synchrony measures could further our understanding of the influences on and measures of group synchrony, allowing us to move away from studying individual persons responding to static laboratory stimuli and toward investigating collective experiences in natural, dynamic social interactions. Interpersonal synchrony, or the temporal coordination of actions and responses among individuals, is a common and central feature of human interactions and gatherings. Coordinated movement is theorized to have evolved to establish and maintain group cohesion, promoting enhanced coordination during survival-relevant activities such as hunting or warfare . PsycholThis has been in part due to difficulties in operationalizing the construct of interpersonal synchrony . Some reAlthough joint action, communication, and relationship formation rely on the coordination of behaviors between two individuals , such coEarly experimental work, which relied on behavior coding by trained observers at regular intervals, showed unintentional coordination of a group of listeners’ non-verbal behaviors like nodding and posture to a speaker’s speech and movements  and has been associated with increased movement synchrony. On the other hand, naturalistic group interaction reflects some form of organizational or social structure  and has been found to correspond with enhanced autonomic synchrony.Some studies of unintentional group synchrony have examined the influence of minimal group interaction. Such experiments are designed to involve the least possible reciprocal action between individuals following from incidental mutual sensory information. These studies investigated whether simply being in a room together with other participants or having visual information on other participants was enough to promote group coordination. These paradigms provide a constrained experimental setting to investigate variables that contribute to unintentional group synchrony, although they may not necessarily be representative of real-life group interactions.In a laboratory setting with minimal interaction, Similarly, Other studies of unintentional group synchrony, in contrast, have investigated the effects of naturalistic group interaction. These experiments evaluate individuals as they perform real-world tasks or participate in real-world events that involve a predetermined social or cultural structure. More ecologically valid contexts such as these, while limited in the types of causal inferences that can be drawn, enable researchers to study the dynamics of unintentional synchrony in real-life group interactions.In a laboratory study with naturalistic interaction, Outside of a laboratory setting, during a fire walking ceremony, In addition, The small number of studies conducted so far and the limited types of measurements collected, however, make it difficult to draw conclusions about how shared experiences influence unintentional group synchrony of behavioral and physiological responses. Does minimal group interaction promote physiological synchrony to the same degree as naturalistic group interaction? Does behavioral synchrony arising from shared experience facilitate physiological synchrony or vice versa? Do behavioral and physiological synchrony resulting from shared experience contribute differentially to social bonding? These questions could be more precisely addressed by simultaneously evaluating behavioral, physiological, and social responses.Prior studies on unintentional group synchrony have developed various ways of quantifying behavioral and physiological responses corresponding with shared experiences. We categorize these methods into two types based on the aspect of interpersonal coordination they evaluate: state differences and temporal dynamics. Our review indicates that state differences in group synchrony, reflecting comparisons between particular conditions, have been mostly described using correlation. In contrast, the temporal dynamics of group synchrony, which capture similarity or dissimilarity over time, seem to be better represented by phase difference.Several studies of unintentional group synchrony compared movement or autonomic coordination among individuals between different group or time conditions. These state differences in group synchrony, which are reduced to a single index of similarity for an entire time period, have been assessed for the most part using correlation- or coherence-type relationships between each pair of participant time courses. In addition, while some of these methods simply calculate an index of similarity among individuals’ behavioral or physiological responses, other methods extract additional indices that describe other aspects of the relationship among individuals.With generalized partial directed coherence , a partiMeanwhile, in cross-recurrence quantification analysis , a crossIn contrast, a few studies of unintentional group synchrony instead examined how movement or autonomic coordination across individuals developed over time. These measures of the temporal dynamics of group synchrony, which are derived from some index of similarity at each time point, have tended to be based on phase differences between participants’ time courses. Some of these methods calculate an index of similarity using pairwise statistics, while other methods derive an index of similarity from group information.To assess time-varying group synchrony, intersubject phase synchronization and sliding window intersubject correlation average across measures of similarity over time between each pair of participants in a group. With intersubject phase synchronization , the insIn contrast, other ways of assessing time-varying group synchrony, such as the cluster phase method and Kuramoto order parameter, calculate a measure of similarity over time based on information from the group as a whole. With the cluster phase method , the insIn general, there does not seem to be a standard method for quantifying unintentional group synchrony. Are similarities between individuals’ behavioral or physiological responses better captured by correlation or coherence, which is based on variance information or data spread, or by phase difference, which is based on temporal information or timing details? Are similarities amongst individuals’ behavioral or physiological responses better represented by correspondence between pairs of individuals or by the relationship of one individual to the rest of the group? Future studies would greatly benefit from a systematic comparison of the discriminability and reliability of these different methods of quantifying unintentional group synchrony.Unintentional group synchrony of behavioral and physiological responses is an essential feature of numerous social processes. Findings so far suggest that minimal  and naturalistic  group relationships both promote unintentional group synchrony of body movements and autonomic responses. In addition, state differences in unintentional group synchrony have been primarily evaluated using correlation- or coherence-type relationships between participants. On the other hand, the temporal dynamics of unintentional group synchrony have been mostly assessed using phase differences between participants as they capture moment-by-moment changes.However, the limited number of studies that have examined unintentional group synchrony and the lack of standardized methods for measuring it mean that the factors that influence it and the means by which it can be quantified remain unclear. Do minimal and naturalistic group relationships, as well as behavioral and physiological synchrony, contribute differentially to social processes? Which aspects of similarity between individuals’ responses are accurately and reliably captured by correlations or coherences compared to phase differences and by pairwise statistics compared to group-based calculations?Understanding how these influences on and measures of unintentional group synchrony reflect social processes is crucial as we move away from studying individual persons responding to static laboratory stimuli and toward investigating collective experiences in natural, dynamic social interactions. Future studies could address these outstanding questions by evaluating behavioral, physiological, and social responses concurrently. Finally, a systematic evaluation of the different measures available would provide valuable insights regarding how unintentional group synchrony could be quantified.ME, JB, and DSM contributed to the conception and writing of the content for the review.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.


This policy research aims to map patient access barriers to biologic treatments, to explore how increased uptake of biosimilars may lower these hurdles and to identify factors limiting the increased utilisation of biosimilars. A policy survey was developed to review these questions in 10 Central and Eastern European (CEE) and Commonwealth of Independent States (CIS) countries. Two experts (one public and one private sector representative) from each country completed the survey. Questions were related to patient access, purchasing, clinical practice, and real-world data collection on both original biologics and biosimilars. Restrictions on the number of patients that can be treated and related waiting lists were reported as key patient access barriers. According to respondents, for both clinicians and payers the primary benefit of switching patients to biosimilars would be to treat more patients. However, concerns with therapeutic equivalence and fear of immunogenicity may reduce utilisation of biosimilars. Similar limitations in patient access to both original biologics and biosimilars raise concerns about the appropriateness and success of current biosimilar policies in CEE and CIS countries. The conceptual framework for additional real-world data collection exists in all countries which may provide a basis for future risk-management activities including vigorous pharmacovigilance data collection. All European countries are facing the challenge of how to cover and reimburse high priced patent-protected medicines such as biologic treatments from their limited health care budget. Despite a greater unmet medical need due to shorter life expectancy and worse overall health status of the population compared to higher income Western European countries, financial constraints are greater in countries of Central and Eastern Europe (CEE) and Commonwealth of Independent States (CIS) . HoweverThe policy rationale for allowing biosimilar entry is analogous to that for generic entry following patent expiry for small molecules. Once patents expire, competing copies or near copies are permitted to enter the market. This competition will lead to lower prices, but generally as a function of the number of entrants. However, the costs of developing a biosimilar are much higher than those for a small molecule follow-on medicine. For the former, regulators require noninferiority head-to-head clinical trials versus the originator biologic medicine. For the latter, they require only bioequivalence assessed using laboratory methods. Generally, market prices decline with the number of entrants and in case of generic competition are often 10–20% of the originator's price. Markets for biologics with competing biosimilars may have fewer entrants and less price reduction, meaning that access will be more limited. Outcomes-based agreement that allow differential pricing in CEE and CIS countries could help to overcome this effect .A large number of original biologic medicines is about to lose patent protection in the near future . In factCompared to small molecule products, biologic therapies with large molecule weight and complex chemical structure cannot be reproduced in a completely identical form . TherefoA systematic literature review found that biosimilar immunogenicity differs among active compounds suggesting that immunogenicity of anti-drug antibodies should be an important consideration in the treatment decision-making process such as switching . Some opmap the barriers of patient access to biologic monoclonal antibodies in CEE and CIS countries,explore how increased uptake of biosimilar monoclonal antibodies may lower these hurdles,identify factors limiting the increased utilisation of biosimilar monoclonal antibodies.The objectives of our policy research were toA standardized English language survey was developed for collecting feedback from policy experts in 10 CEE and CIS countries, including Bulgaria, Czech Republic, Hungary, Kazakhstan, Latvia, Lithuania, Poland, Romania, Russia, and Slovakia. In each country, one expert from the public sector and one from the private sector completed the survey see . ExpertsSurvey questions are summarized in Iterative development of the survey tool covered preliminary interviews, field testing with local experts, and finalising it based on their comments before release. The final survey consisted of 4 spreadsheets . Answer options were given in the survey, but respondents had a chance to provide additional clarification by using an “other (please specify)” option after each question. In addition, respondents could provide additional explanation or references. Responses by the two experts from each country were reconciled and in case of inconsistency disagreements were solved via e-mail queries.Survey results are presented according to the study objectives, reflecting some key areas of improving patient access to biosimilar products.Generally, the cost-saving potential and the capacity to improve patient access to reimbursed biologic medicines were mentioned as key benefits of increased utilisation of biosimilars. According to respondents' opinions, both clinicians and payers expect that treating more patients with reimbursed biologic medicines  would be the primary benefit of switching patients to biosimilars . The cosAccording to this survey, in general, payers in CEE/CIS countries seem to have no concerns with extrapolation to indications. EMA registration is well accepted even if pivotal phase III data were to be extrapolated to other indications without additional phase III clinical trial evidence. In contrast, respondents in some countries—for example, in Bulgaria and in Slovakia—expected negligible or restricted use of biosimilars by clinicians in indications with only extrapolated data. This is especially true in the field of oncology, where respondents anticipated concerns from clinicians about extrapolating biosimilar access to other indications.According to respondents, immunogenicity after switching the patients to biosimilar medicines could be a major concern for clinicians, while limited information on therapeutic equivalence with their originator could be problematic for both clinicians and payers see . InteresIn majority of the countries, switching from original biologic medicine to its biosimilar alternative is allowed. However, patients on maintenance biologic treatment were expected to stay on the original product. While majority of the countries apply tenders for public procurement of biologic medicines, the tender-winning biosimilars were expected to be used for both treatment-naïve patients and patients already on maintenance biologics only in Lithuania, Poland, and Slovakia. In Hungary, there are two separate tenders, one for treatment-naïve patients and one for patients already on maintenance biologics. In all other countries, the tender winner product should be prescribed only for the nontreated patients, while treated patients may continue their previous biologic treatment: switching to a more affordable biosimilar alternative is not mandatory.Postmarketing data collection related to medicines in general was reported in all participant countries either in patient registries or based on payers' databases. Such activities are coordinated by the health insurance fund in Hungary, Poland, and Slovakia. Because of the abovementioned concerns related to biosimilars, all countries except Russia reported that pharmacovigilance data on biosimilars were regularly assessed or planned to be assessed in the near future according to EMA requirements. Additionally, national regulatory authorities in Hungary and Slovakia were reported to reassess data on effectiveness after registering a new biosimilar to ensure equal health gain with the originator product.While a few publications are available on biosimilar policies in higher income countries , 30–32, Given reported access restrictions, it is not surprising that increasing the number of patients receiving reimbursed biologic treatments was considered the primary benefit of switching patients to biosimilar products in almost all participant countries. Value-based purchasing strategies require strong government interventions: biosimilars will not become the preferred option in clinical practice if biosimilar policies are ineffective and central government administration plays a passive role . As withBecause infliximab, trastuzumab, and rituximab are mainly used as long-term maintenance therapies in chronic diseases, the societal benefits of access to more affordable biosimilars can be maximised by both starting all de novo patients on biosimilars and, as much as is clinically justifiable, switching patients to biosimilars . AlthougAs a general practice, our survey revealed that switching to biosimilar products is allowed in many countries and may be initiated at the discretion of the physician. This passive policy was confirmed in a recent policy survey in CEE countries . In contUncertainty related to switching may be handled by risk-management strategies . Ex postA recent paper summarizes potential government interventions to facilitate the utilisation of biosimilars in lower income countries see  9]..9].Generalizability of our findings is limited, as our policy research engaged only two experts (representing both public and private sectors) in each participant country. Although survey respondents had the opportunity to consult with additional local experts, their opinion may not be fully representative for the entire country. However, as our findings are in line with conclusions of other publications, the trends presented in this study may provide relevant input to reconsideration of current biosimilar policies in CEE/CIS countries.Our survey indicated a significant unmet need for more affordable biologic therapies in the CEE/CIS countries. The key concerns about the increased utilisation of biosimilars were limited evidence on therapeutic equivalence and expected adverse immunologic reactions. Nevertheless, the role of biosimilars in increasing patient access to reimbursed biologics was acknowledged by almost all experts. Policy-makers have to take a strategic approach to increase societal benefit from biosimilar medicines. Relying on free-market incentives may not be strong enough; hence, active government interventions instead of “passive disinvestment” policies are needed to correct for current access limitations.


A unified analysis of DNA sequences from hundreds of tumors concluded that the driver mutations primarily occur in the earliest stages of cancer formation, with relatively few driver mutation events detected in the late-arising subclones. However, emerging evidence from the sequencing of multiple tumors and tumor regions per individual suggests that late-arising subclones with additional driver mutations are underestimated in single-sample analyses.To test whether driver mutations generally map to early tumor development, we examined multi-regional tumor sequencing data from 101 individuals reported in 11 published studies. Following previous studies, we annotated mutations as early-arising when all tumors/regions had those mutations (ubiquitous). We then inferred the fraction of mutations occurring early and compared it with late-arising mutations that were found in only single tumors/regions.While a large fraction of driver mutations in tumors occurred relatively early in cancers, later driver mutations occurred at least as frequently as the early drivers in a substantial number of patients. This result was robust to many different approaches to annotate driver mutations. The relative frequency of early and late driver mutations varied among patients of the same cancer type and in different cancer types. We found that previous reports of the preponderance of early driver mutations were primarily informed by analysis of single tumor variant allele profiles, with which it is challenging to clearly distinguish between early and late drivers.The origin and preponderance of new driver mutations are not limited to early stages of tumor evolution, with different tumors and regions showing distinct driver mutations and, consequently, distinct characteristics. Therefore, tumors with extensive intratumor heterogeneity appear to have many newly acquired drivers.The online version of this article (doi: 10.1186/s12885-017-3977-y) contains supplementary material, which is available to authorized users. Tumor cells accumulate numerous somatic mutations. Some of these mutations directly contribute to tumor growth and progression and are commonly referred to as driver mutations. Knowledge of the relative timing of driver mutations is essential for understanding cancer progression as a whole and for optimizing treatment for individual patients –4. For tMutations found in most cells of a tumor can be detected by estimating the fraction of cancer cells harboring a particular variant, or cancer cell fraction (CCF) . This apHowever, tumors consist of heterogeneous cell lineages, each of which may be driven by different driver mutations –3. For eNow that M-seq data from various cancers are available, it is possible to comprehensively explore the relative preponderance of driver mutations arising in early and late stages of tumor growth. Here we perform a meta-analysis using samples from 101 individuals representing various cancer types , 24–32, We obtained sequencing read counts of mutant and wild type alleles, and their chromosomal positions, for 101 tumor data sets from 11 published studies that contained at least three tumor samples per patient . The IntOGen pipeline examines genes that frequently have mutations with high functional impact and regions of the protein sequences where mutations frequently occur (Driver annotation type IV). To predict driver mutations with IntOGen, we input all mutations (point mutations and indels) with chromosomal positions, wild type and mutant nucleotides, and strand information obtained from Ensembl database.Furthermore, many computational methods have been created to determine variants with functional roles in cancer . In the Lastly, it has been observed that genes that are causal in one cancer type many not be causal in every cancer type. Therefore, in annotation type V, we obtained a list of cancer associated genes for each cancer type from IntoGen database  in orderBefore distinguishing between early vs. late-occurring mutations, we made use of the extra power conferred by the M-seq approach to detect potential sequencing errors. Using Treeomics , we estiTo distinguish early from late-arising mutations, we defined early-arising mutations as those that are found in all samples (ubiquitous mutations), and late-arising mutations as those that are private to only one sample (region-specific or private mutations). This definition is more stringent than the definition used in the previous TCGA analysis , becauseWe first pooled driver mutations from 101 patient data sets from 11 studies , 24–32 aInterestingly, we found relatively large numbers of driver mutations to be late-occurring (33–45%), with the number of late-driver mutations similar to or even greater than the number of early-occurring driver mutations (22–38%) Fig. . The remFalse-positive detections of driver mutations are expected to be high when mutations are located on genomic positions with higher mutation rate, e.g., CpG sites , 41. TheAlthough the total number of late driver mutations in our data was similar to or greater than that of early driver mutations, we found that the fraction of driver mutations occurring at early stages varied extensively among patients, studies, and cancer types. Very few early drivers were detected in esophageal adenocarcinoma data sets , but a large fraction of drivers were early in breast cancer data sets , and late drivers still occurred at a high frequency . Using In addition, very few patients had a greater fraction of late-drivers compared to early-drivers when single samples were used Fig. . This coWhile the above patterns consistently showed that the numbers of driver mutations occurring late are comparable to those that occurred early-on, it is important to assess their robustness to a number of factors that complicate analysis and interpretation of tumor genome variation.First, the observed variability in the timing of driver mutation occurrence among patients may be caused by technical issues, such as mutation calling methods, tumor purity, and sequencing depth. This was the reason for our use of Treeomics to exclude low quality SNVs due to low sequencing depth.Second, it is possible that the differences observed between studies (cancer types) were caused by the differences in mutation calling methods among the studies, as some studies may be able to detect mutations with lower SNV frequencies than others. However, we often observed that the fraction of early driver mutations as well as late driver mutations varied among patients from the same study analyzed with the same methodologies. Therefore, any systematic error based on methodology would appear to be minor, and such technical issues should not strongly affect our conclusion.Third, tumor purity could impact the annotation of early and late drivers. Generally, though not necessarily, the late-arising subclones will be in lower frequency when the tumor purity is low. Therefore, if purity were an issue, we would expect to experience a lesser power to detect late drivers as compared to early drivers, as early drivers are expected to manifest at higher frequencies. Thus, our estimates of the relative excess of late drivers are likely to be conservative.Fourth, the number of early driver mutations may be underestimated, because sequencing reads indicating true early driver mutations may not be observed in one of many samples by chance, despite high overall coverages. While this dropout can occur, we expect it to be far less common for early drivers than it would be for private mutations, because they will generally occur with lower frequency and in fewer multiregion samples than the early drivers. Once again, our observation of the relative excess of late drivers is conservative.Fifth, copy number alternations (CNAs) will likely cause difficulty in designating some early-arising drivers, because the drivers can be lost by the loss of genomic segments in some tumor samples. Ideally, a reanalysis of all the primary data will be desired to identify this effect fully. However, currently available methods are only modestly accurate , 45. FurOur results establish that the fraction of driver mutations occurring in the earliest stages of cancer varies among patients as well as cancer types. We have shown that, overall, the number of late driver mutations are equal to or greater than early drivers in 44% of the patients with metastatic tumors. This conclusion differs from some previous reports arguing that the majority of driver mutations happen early in cancer progression  or that Our observation that the number of late driver mutations is similar to early driver mutations does not inform us about the rate of driver mutation occurrence per cell or about the relative degrees of selective advantage conferred by early and late drivers. The number of cells that arise late in cancer progression  is expected to exceed the number of clonal cells, so the number of subclonal variants is expected to exceed the number of clonal variants, which would result in the increased preponderance of mutations. In fact, the number of driver mutations was linearly correlated with the number of passenger mutations for both early Fig.  and for We found that subclones with late drivers occur with significant frequencies; the average observed mutant frequency of late-arising mutations was 19% (with a standard deviation of 14%). In fact, 34% of late mutations were present at frequencies greater than 20% Fig. . HoweverIn a meta-analysis of genome variation data from multiple tumor in each patient, we find that the numbers of late driver mutations are substantial: they often exceed the number of early drivers. No previous study has conclusively demonstrated this pattern, even though they have indicated presence of driver mutations in tumors. These results implicate driver mutations in the continued development of aggressive tumor growth and in progression during later events such as recurrence, metastasis well beyond the initial founding of the tumor. Finally, these results highlight the importance of accounting for intratumor heterogeneity when evaluating the mutational histories of tumor cell populations.


Measurement of laterality is of interest both to researchers investigating the neural organisation of the language system and to clinicians needing to establish an individual’s hemispheric dominance for language prior to surgery, as in patients with intractable epilepsy. Recently, there has been increasing awareness of the possibility that different language processes may develop hemispheric lateralisation independently, and to varying degrees. However, it is not always clear whether differences in laterality across language tasks with fMRI are reflective of meaningful variation in hemispheric lateralisation, or simply of trivial methodological differences between paradigms. This systematic review aims to assess different language tasks in terms of the strength, reliability and robustness of the laterality measurements they yield with fMRI, to look at variability that is both dependent and independent of aspects of study design, such as the baseline task, region of interest, and modality of the stimuli. Recommendations are made that can be used to guide task design; however, this review predominantly highlights that the current high level of methodological variability in language paradigms prevents conclusions as to how different language functions may lateralise independently. We conclude with suggestions for future research using tasks that engage distinct aspects of language functioning, whilst being closely matched on non-linguistic aspects of task design ; such research could produce more reliable and conclusive insights into language lateralisation. This systematic review was registered as a protocol on Open Science Framework:  It is well established that for most individuals, the left hemisphere is dominant in mediating language functions, as proposed in the 19th century by Paul Broca. Our understanding of such hemispheric specialisation for language in the centuries since still leaves many unanswered questions. Because both expressive and receptive aphasia are so reliably associated with left-hemisphere injury, there tends to be an assumption that left-sided lateralisation is a general feature of language processing, consistent across language domains. Nevertheless, there is evidence that lateralisation may differ within individuals for different language functions, as well as between individuals in side and extent.Early suggestions of within-individual variability can be found in Indeed, several contemporary models of language predict different patterns of lateralisation for different language processes . These pContemporary non-invasive techniques allow more extensive research on patterns of laterality than earlier clinical studies. Functional magnetic resonance imaging (fMRI) data can be used to calculate a laterality index (LI), a single value description of the predominance of activity in one hemisphere. The LI is calculated as the difference between activity in each hemisphere (L and R) divided by the total activity across the hemispheres. Multiple language tasks have been used with fMRI. At first glance, the literature appears to support the notion that language laterality is not unitary, because we can see differences between tasks in the strength of the laterality measurements they yield. However, the reasons for such variability in LI strength across language tasks can be debated; could it simply be an artefact of more trivial differences in task design, or does it reveal something fundamental about the hemispheric organisation of different components of language? Of course, trying to devise tasks so as to equate diverse language functions such as speech production and speech comprehension is an unrealistic and inappropriate goal. However, more can be done to optimize protocols for LI measurement, in order to try to reduce the possibility of differences in task sensitivity or measurement error being responsible for variability in LIs across tasks.This systematic review aims to assess evidence on the robustness of laterality measured using fMRI with different language tasks, from studies published between 2000 and 2016. This is done with a view to providing some guidance on optimizing variables such as region of interest and baseline task on a task-by-task basis. Such optimization will be important before tasks can be used to systematically probe patterns of co-lateralisation and independent lateralisation of different language functions. We hypothesise that (1) different language tasks will demonstrate different levels of lateralisation and (2) parameters such as the region of interest and baseline task used will have effects on laterality measurement that may be task-specific.https://osf.io/5vmpt/. We do not cover here generic issues such as how thresholding and other methodological issues affect laterality measurement, since these were the focus of a companion review based on the same source material  the paper reported LIs for language calculated using fMRI; (2) the paper studied healthy monolingual adults; and (3) if both patients and healthy controls were studied, the data for controls were reported separately. Papers were excluded if: (1) they exclusively studied structural asymmetries, children or bilingualism; or (2) they used language tasks with non-European languages. The search was restricted to studies of healthy, monolingual, adult participants to reduce heterogeneity within our study sample.The following search terms were used to search papers published between 2000 and 2016 in Web of Science: laterali* OR asymmetr* OR dominance; AND language OR reading; AND fMRI OR functional MRI OR functional magnetic resonance imaging OR functional MR OR function MRI; NOT schizophrenia; NOT development*; NOT child*; NOT bilingual*. This was last searched on 05/12/16. Titles and abstracts of the resulting 90 papers were then screened by two of the review authors (Abigail Bradshaw and Zoe Woodhead), followed by full-text scans to determine whether the inclusion criteria were met. Selected lists were compared between reviewers and any discrepancies discussed and a mutual decision made. This resulted in the selection of 34 papers. We next screened papers citing these 34 articles. A further 50 articles were identified as meeting our criteria, yielding a total of 84 papers. A final check of papers led to the discounting of 7 papers deemed to not sufficiently meet criteria, with a further paper being discounted during conductance of the review. The full search and selection process is illustrated in Information on variables of interest for each study were collected and managed using REDCap electronic data capture tools  hosted aThe variable nature of the methods and measures reported by different papers did not permit performance of a meta-analysis. Instead, to illustrate the strength of laterality measured across different language tahe sks, we produced forest plots showing the mean and 95% confidence intervals of LI values reported in the studies, as well as their associated methods of LI calculation, region(s) of interest, and language and baseline tasks –4. Howevhttps://osf.io/7s4hv/).To avoid the potential confound of heterogeneity in samples in terms of handedness, these forest plots only included mean LIs reported by our selected studies measured either from right handed participants, or from mixed handedness samples where the relative proportion of left and right handers was representative of the general population . We excluded LIs reported from studies that selected a participant group on the basis of their pre-known lateralisation. Where more than one frontal LI was reported from a study, inferior frontal gyrus (IFG) LIs were selected; where more than one temporoparietal LI was reported, the LI calculated from the largest area of temporoparietal cortex was selected. Forest plots were created using a script in R, which is available along with the data on open science framework  words that meet certain criteria, such as beginning with a particular letter (phonemic fluency), belonging to a particular semantic category (semantic fluency), verbs that are semantically associated with a particular noun (verb generation), or words that are antonyms/synonyms (antonym/synonym generation). Any lateralisation induced by this task may thus reflect a mixture of phonological, semantic, word retrieval and speech motor planning/articulation processes see . LateralAcross the papers reviewed here, verbal fluency tasks are consistently reported as yielding the strongest laterality when compared to other receptive and expressive tasks within studies . StudiesA number of studies have compared the use of different verbal fluency paradigms. When a frontal region of interest (ROI) is used, semantic fluency is reported as less strongly lateralising than verb generation or phonemic fluency . ConversVerbal fluency tasks tend to induce the strongest laterality in frontal ROIs . AlthougAlthough the majority of studies using verbal fluency tasks employed a passive baseline task such as fixation, a number used active baselines such as finger tapping or silent word repetition see . Dodoo-SSentence generation requires participants to generate sentences to describe presented pictures. These sentences may either be pre-defined and learnt prior to scanning, or generated during the scan itself. Relative to word generation, additional syntactic and semantic-integration processes are involved in the construction of a sentence see . These aMean LIs reported from sentence generation studies are illustrated in There is mixed evidence as to whether sentence generation yields differences in the laterality measured from frontal versus temporoparietal ROIs. While When sentences are learnt prior to scanning, studies generally employ simple cross-fixation or rest as a baseline . ConversPassive speech listening as a language paradigm appears to yield more variable laterality estimates, perhaps reflective of the wide variety of language processes that it can engage see . Lower-lThe majority of studies using passive listening tasks reported very weak average LIs see . A notabStudies are inconsistent as to whether stronger laterality is found for speech listening within a frontal or a temporoparietal ROI. The varying levels of asymmetry reported for speech listening tasks may in part be attributable to the baseline used by different studies. per se  text reading task with a baseline of covert reading of text composed of pronounceable non-words. Phonemic judgement tasks require a decision relating to phonological structure; most commonly, a rhyme judgement. This task relies on mapping of acoustic or visual input onto phonological units such as phonemes and syllables, a process known as decoding. The precise nature of these stored phonological codes remains a debate; according to theorists in the tradition of the motor theory of speech perception e.g., , these aPhonemic judgement tasks yield relatively strong laterality, with reported LI values ranging from 0.41 to 0.84 see . HoweverRhyming decision tasks yield particularly strong laterality when a frontal or a combined frontal-temporoparietal ROI is used . For exaAll studies within our search using phonemic judgement were found to employ an active perceptual decision baseline task on either non-linguistic material  or nonsense words or characters . This subtracts out non-linguistic working memory processes see , as wellSemantic decision tasks require a judgement about a word’s semantic content or about the semantic relationship between a pair of words, such as whether two words belong to the same category. Such conceptual knowledge is proposed to rely on a distributed processing network, with different brain areas each contributing to different aspects of an item’s representation . In addiThe strength of laterality reported for semantic decision tasks is quite variable, ranging from near-zero to around 0.8 see . In contThe majority of studies report no significant differences in the magnitude of laterality found within temporoparietal and frontal ROIs for semantic decision tasks . Some stSemantic decision laterality is also strongly influenced by the baseline task used. per se  or verbal description. According to Studies using naming as a language activation task report a wide variety of LI values, ranging from 0.08 to 0.96 see . This caPicture naming also shows poor reliability in laterality measurement. Naming tasks do not appear to favour one ROI over another in laterality measurement. We found two studies which reported differences in the laterality measured from frontal and temporoparietal ROIs, however this difference was in opposite directions . The majThe baselines used with picture naming provide interesting evidence on the processes underlying its laterality. Combined task analysis (CTA) involves the calculation of LIs from contrast images generated by combining scans across multiple language tasks. This method identifies commonalities between tasks’ activity patterns in order to isolate the ‘core’ language network, and exclude task-specific, non-linguistic activity caused by differences in task design that may influence the LI value. In this way, CTA can represent a theoretical alternative to baseline tasks to subtract domain-general activity, assuming that different tasks involve different patterns of non-linguistically relevant activity. Indeed, there is evidence that CTA results in higher and more reliable and robust estimates of laterality for language .Nevertheless, the theoretical assumptions which motivate CTA can be questioned. CTA assumes that variability in laterality for different tasks should be ascribed to non-linguistic processes or viewed simply as measurement error, rather than reflecting the underlying nature of hemispheric organisation for language e.g., . Such a Such differences in dominance between language tasks would be lost in a CTA, since combining scans across paradigms would result in few areas of common activation and thus a loss of these tasks differences. Instead, it will be necessary to design fMRI protocols that probe the within-subject variation in language lateralisation across a range of tasks, while controlling for non-linguistic confounds. Conversely, the strong, reliable and robust LI values provided by CTA would be more useful in cases where a clear categorical decision on an individual’s language lateralisation is required.This review has highlighted the high level of variation and inconsistency in the strength and reliability of laterality measured using different language tasks. As per our hypotheses, some of this variability in laterality is related to parameters such as the region of interest and baseline task, which can have task-specific effects. In general, however, the current state of the literature is such that it is difficult to draw clear conclusions that can be used to guide task selection. This review highlights the need for more research that systematically compares laterality across different tasks in within-subject designs, with rigorous matching of non-linguistic aspects of task design.The current review of the literature does suggest however some practical recommendations that can be used to guide task design. Extensive use of verbal fluency is clearly warranted given the robustness of its lateralisation; however, the common employment of passive baselines should be replaced with active baselines carefully chosen so as to isolate the language process of interest whilst controlling for all other processes. Comparison of word generation with sentence generation offers the opportunity to test predictions of models that assume stronger laterality for processing of connected sentences over single words . Future Evidence on semantic decision paradigms suggests that stronger laterality can be observed when tasks require integration across the semantic content of different concepts , rather than simple category membership decision on single words. Similarly, where naming tasks are used, evidence suggests that naming from description yields more robust laterality measurement than naming from pictures; that is, an additional sentence comprehension component appears to improve the lateralising power of this task. Sentence comprehension tasks themselves appear to yield strong laterality; however, more work is needed to develop such tasks in order to attempt to disentangle semantic and syntactic components. Indeed, this review highlights a distinct lack of tasks in language laterality research that aim to primarily engage syntactic processing, reflected in the lack of consensus over the strength of its hemispheric specialisation. Further work is needed to design and validate tasks that isolate syntactic processing for laterality measurement. One possibility might be offered by tasks involving judgements on ‘jabberwocky’ sentences e.g.,  in whichMore work is needed to investigate the potential significance of variability in laterality across different language functions, both within individuals and at a group level. Growing appreciation of the potential significance of cases of dissociated dominance, both in clinical and healthy samples, should encourage the field to move away from the use of single tasks and single ROIs. Instead, research should focus on developing batteries of closely matched tasks that tap a variety of language functions to allow systematic comparisons in within-subject studies. This will ultimately allow for more quantitative meta-analyses of such literature, to draw stronger conclusions as to patterns of laterality across different components of the language network.One way to approach this would be to develop a generic task format in which the participant is always performing the same form of task with the same type of stimuli but with regards to different linguistic parameters. For example, one such format could be a decision task in which one must decide if pairs of word stimuli are ‘matching’ or ‘non-matching’. The parameters that define matching and non-matching pairs can then be varied according to the language process of interest; for example, rhyming versus non-rhyming (phonology), same semantic category or different semantic category (semantics) or same syntactic category or different syntactic category (syntax). These could be interleaved, with a visual cue indicating which decision should be made on the current trial. In this way, a generic task format would remove non-linguistic differences in task design that can confound interpretation of differences in laterality. This type of approach has been embraced by Price and colleagues in the development of a battery of tasks devised for a within-subject, fully balanced factorial design, with tasks corresponding to all possible combinations of levels of factors relating to experimental design aspects . This has been used to test contrasts that allow fractionation of different levels of linguistic processing for localization of brain activity e.g., ; future CTA can provide an efficient method of isolating language activity shared across multiple different aspects of language functioning, to allow robust and reliable measurement of laterality of the core language network. However, this methodology appears to be motivated by an implicit assumption pervasive across much laterality research that there is a single core language network which displays a unitary and perfect lateralisation; thus the ability of an fMRI protocol to provide a good measure of language laterality depends on its ability to uniquely engage this language network and to yield a laterality index of close to 1 at the group level. Tasks which yield LIs further from 1 therefore are viewed as inadequate measures of language lateralisation.We argue that defining the sensitivity of a task to capture the ‘true’ lateralisation of a language function in terms of the strength of its laterality can be challenged. Such an approach would lead one to reject tasks that yield lower LIs, which may in fact reflect meaningful variation in hemispheric organisation within the language network. For example, naming is a complex linguistic function that requires both receptive and expressive components, with access to both semantics and phonology; however the evidence reviewed here shows it yields low LI values e.g., . Is it aThis raises the question of how one should judge laterality paradigms; what metric should one use for judging ‘success’ in accurately measuring an individual’s laterality? This review has highlighted how different methods of laterality measurement can result in variable LI values for an individual across different regions, active language tasks and baseline tasks. For example, in the case of verbal fluency tasks, an individual may show a stronger LI when an active rather than a passive baseline is used . How sho10.7717/peerj.3929/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.3929/supp-2Supplemental Information 2Flow diagram illustrating the search and selection process for obtaining articles for inclusion in the review. Adapted from Click here for additional data file.10.7717/peerj.3929/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.3929/supp-4Supplemental Information 4Spreadsheet drawn from REDCap (Research Electronic Data Capture) database used to input data on each of the 76 selected studies.Click here for additional data file.10.7717/peerj.3929/supp-5Supplemental Information 5Summary of key outcomes of interest recorded for each study drawn from the full database S2.Click here for additional data file.10.7717/peerj.3929/supp-6Supplemental Information 6Spreadsheet with laterality data from studies meeting our criteria for inclusion in forest plots . Spreadsheet gives details on methods of LI calculation, LI means, standard deviations and/or standard errors, and 95% confidence intervals, with separate worksheets for each forest plot –4.Click here for additional data file.10.7717/peerj.3929/supp-7Supplemental Information 7A statement of purpose outlining the rationale for the systematic review and its contribution in light of previously published reports.Click here for additional data file.


Generally, ventral portions of the posterior cingulate cortex were more likely to be activated by spatial encoding, i.e., passive viewing of scenes or active navigation without a demand to respond, perform a spatial computation, or localize oneself in the environment. Conversely, dorsal portions of the posterior cingulate cortex were more likely to be activated by cognitive demands to recall spatial information or to produce judgments of distance or direction to non-visible locations or landmarks. The greatly varying resting-state functional connectivity profiles of the ventral  and dorsal  posterior cingulate regions identified in the meta-analysis supported the conclusion that these regions, which would commonly be labeled as “retrosplenial cortex,” should be more appropriately referred to as distinct subregions of the posterior cingulate cortex. We suggest that future studies investigating the role of the retrosplenial and posterior cingulate cortex in spatial tasks carefully localize activity in the context of these identifiable subregions.The retrosplenial cortex has long been implicated in human spatial orientation and navigation. However, neural activity peaks labeled “retrosplenial cortex” in human neuroimaging studies investigating spatial orientation often lie significantly outside of the retrosplenial cortex proper. This has led to a large and anatomically heterogenous region being ascribed numerous roles in spatial orientation and navigation. Here, we performed a meta-analysis of functional Magnetic Resonance Imaging (fMRI) investigations of spatial orientation and navigation and have identified a ventral-dorsal functional specialization within the posterior cingulate for  Over a century ago, Korbinian Brodmann published an exhaustive cytological parcellation of the human cerebral cortex  studies investigating the role of this region in spatial cognition do not report results in the retrosplenial cortex proper. This is partially due to the fact that the retrosplenial cortex, as delineated by Vogt et al.  is effecAlthough the mislocalization of the retrosplenial cortex is somewhat egregious, it is not without precedence. Brodmann himself, in fact, intentionally overrepresented the size of the retrosplenial cortex in his original figures Figure , and he While it is possible that a slight misrepresentation of an anatomically-defined region provides a more accurate representation of a functionally-defined region involved in spatial orientation and navigation, the “retrosplenial cortex” label has been used in human spatial cognition research far too liberally, including many areas of the posterior medial cortex far beyond the anatomical border of the retrosplenial cortex proper  OR precuneus OR  OR  OR  and (sulcus OR fissure)) OR ((Brodmann Area OR BA) and (23 OR 26 OR 29 OR 30 OR 31))AND and (navigation OR memory OR recognition OR learning OR integration OR construction OR imagination OR orientation)) OR path integration OR dead reckoning OR cognitive map OR mental representation OR spatial configuration OR perspective takingANDfMRI OR functional magnetic resonance imaging OR functional neuroimaging OR BOLD OR blood oxygen level dependentThis conjunctive search produced 297 articles, which were subsequently filtered to only include the 61 research articles with healthy, adult subjects performing a spatial task while fMRI data were collected, with coordinates reported in the manuscript or supplementary materials. The references in five relevant review articles included in search results were mined, resulting in an additional 23 articles meeting these criteria included from 497 references. Finally, an additional seven articles known to the authors through personal communications with other researchers were included. The total sample of articles passing filtering was comprised of 91 articles. This search strategy was not intended to be exhaustive, but rather generate a sample that is adequately representative of the state of the cognitive neuroscience literature investigating human spatial orientation and navigation.spatial encoding or spatial recall. Contrasts classified as spatial encoding were principally characterized by relatively more bottom-up or stimulus-driven BOLD activity. These included cases where participants were viewing or imagining visual stimuli, such as landmarks or scenes, or performing active navigation in a novel environment, without an explicit demand to perform a spatial computation or localize unseen landmarks in the environment. For instance, a functional localizer, contrasting BOLD activity while participants viewed scenes over BOLD activity while participants viewed faces or objects  over a control task, would be classified as spatial recall. However, this category also included contrasts weighted more heavily towards spatial representations or judgments in addition to recall per se. For instance, Rosenbaum et al. . We did not classify multiple non-orthogonal contrasts from a single study, and instead selected the contrast most representative of either spatial encoding or spatial recall, leaving non-orthogonal contrasts unclassified. We then passed all coordinates from classified contrasts to a Multilevel Kernel Density Analysis .To characterize the differences between subregions identified in the MKDA, we contrasted the resting-state functional connectivity profile of regions more likely to be activated by From all 91 articles passing initial filtering, we identified 143 coordinates from 32 articles with a label including “retrosplenial cortex.” Figure spatial encoding and spatial recall. Contrasts classified as spatial encoding were generally characterized by stimulus-driven activity in which participants viewed scenes or explored virtual environments, with no explicit demand to localize themselves or unseen landmarks. Contrasts classified as spatial recall included those with demands to recall the location of, or route to, landmarks in familiar environments, as well as contrasts that track environmental properties or knowledge . As shown in Figure p < 0.001 revealed that spatial encoding was more likely to activate ventrolateral portions of the posterior cingulate , whereas spatial recall was more likely to activate dorsomedial portions of the posterior cingulate . These findings closely parallel the results reported in our previous study  = 3.719, p = 0.002062 at MNI −14, −60, 14).We performed an MKDA to identify regions within the retrosplenial cortex and posterior cingulate Figure  that arespatial encoding, and a dorso-medial cluster of 408 voxels more likely to be activated by spatial recall as seeds for a resting state functional connectivity analysis. Contrasting the functional connectivity profiles of these regions revealed significant differences across the brain, detailed in Supplementary Table S2. Across the 132 brain regions tested, the ventro-lateral and dorso-medial posterior cingulate seeds displayed significantly (pfdr < 0.001) different connectivity patterns with 69 regions . The ventro-lateral spatial encoding seed displayed significantly more positive functional connectivity with numerous occipital, lateral parietal, and ventral temporal regions. The dorso-medial spatial recall seed, on the other hand, was more positively functionally connected to the posterior cingulate, as well as the frontal pole and dorsomedial prefrontal cortex , ventral portions were more likely to be activated by spatial encoding, and dorsal portions more likely to be activated by spatial recall.While this could be shrugged off as a simple case of difference in nomenclature, we would argue that the lack of specificity in the use of the “retrosplenial cortex” label actively impedes generating a clear and precise understanding of how this region supports the cognitive processes involved in spatial orientation and navigation in humans. In the present study, we provided evidence that the large region that is commonly labeled “retrosplenial cortex” displays a relevant subregion specialization. We classified 114 contrasts from 91 articles as either spatial encoding, cluster and the dorso-medial, spatial recall, cluster. The spatial encoding seeds were centered upon lateral portions of the anterior bank of the common trunk of the parietal-occipital fissure and calcarine sulcus, immediately ventral to where they join. This region displayed more positive functional connectivity coefficients with many ventral-stream, “spatial context” regions, such as the fusiform and lingual gyri (Milner and Goodale, spatial recall seed was centered 2 cm dorsal to the spatial encoding seeds, and displayed relatively greater resting state functional connectivity with regions commonly implicated in spatial manipulation, as well as spatial and episodic memory, such as the posterior cingulate, precuneus and frontal pole (Maddock et al., These findings are supported by a wide variety of previous research that have identified differences in cytology, as well as differences in functional and structural connectivity within this area (Vogt et al., spatial encoding clusters occupied a relatively more lateral position, deeply tucked within the parietal-occipital fissure. This localization is consistent with previous work by Silson et al. (spatial encoding clusters were sequestered to the lateral portions of the parieto-occipital fissure, as more medial and anterior regions may be involved in processes that are poorly characterized by the spatial encoding and spatial recall paradigm we adopted.While the ventral—dorsal distinction between these subregions was distinct, the n et al. , who locn et al.  also desn et al. . In thisthe relevant subregions of this brain area, we do feel that some simple considerations can be taken into account to reduce the ambiguity of the retrosplenial cortex’s position and role in cognition. First, we would suggest reserving the label “retrosplenial cortex” for peaks which reside within the callosal sulcus, or at least are closer to the callosal sulcus than the parietal-occipital fissure, especially at MNI z positions above +10 mm. Further, for the peaks in the posterior cingulate but in the vicinity of the retrosplenial cortex proper, it may be valuable to begin making the distinction between more ventral and dorsal regions; using the point at which the calcarine sulcus joins with the parietal-occipital fissure as an easily-identifiable landmark for differentiation, or at least reference, as our findings would indicate that regions ventral and significantly dorsal to this point may not be functionally homogenous.In conclusion, we believe that the identification of detectable subregions within the posterior cingulate warrants a more precise and nuanced manner in which we discuss and report the results of neuroimaging findings in this region. While the number and location of the particular clusters identified in this meta-analysis likely do not represent FB and GI conceived and designed the study. FB, AU, LHM and KP collected and analyzed the data. All authors contributed to and have approved the final manuscript.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.


We identify 68 SNPs as the major drivers of this change in correlation. As long as ancestry-related bias when using these clinical databases is minimally recognized, the genetics community will face challenges with implementation, interpretation and cost-effectiveness when treating minority populations.To characterize the extent and impact of ancestry-related biases in precision genomic medicine, we use 642 whole-genome sequences from the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) project to evaluate typical filters and databases. We find significant correlations between estimated African ancestry proportions and the number of variants per individual in all variant classification sets but one. The source of these correlations is highlighted in more detail by looking at the interaction between filtering criteria and the ClinVar and Human Gene Mutation databases. ClinVar's correlation, representing African ancestry-related bias, has changed over time amidst monthly updates, with the most extreme switch happening between March and April of 2014  evaluates typical variant filters and existing genome databases against newly sequenced African-ancestry populations. The idiom ‘searching for a needle in a haystack' is frequently used in genomics, and is especially apt for describing the search for causal alleles in patients with non-canonical diseases of likely genetic origin. As a field, we tend to be singularly focused on the needle and forget that the complexity of the haystack is actually a highly rate-limiting step of this search. The motivation of this project is to characterize the complex interaction between variant prioritization and ancestry, often believed to be largely affected by the predominance of European-based data within clinical databases12To explore the role ancestry plays in variant prioritization approaches often implemented in genomic medicine, we utilize whole-genome sequencing data from 642 study individuals in the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA). The CAAPA project represents a diverse group of admixed individuals of African descent with no suspected Mendelian conditions. It has been shown that there is a strong correlation between the overall number of variants found per individual and African ancestry456We initially classified variants into two main groups, with pathogenic annotated variants (PAVs) comprising those identified as disease-causing in the Online Mendelian Inheritance in Man (OMIM)111213141516171819121r=0.992, P=6.12 × 10−14) and a modest positive correlation between African ancestry and variants in ClinVar . The correlation becomes less positive or even negative (in silico predictions (deleterious filter).We find significant correlations between estimated African ancestry  and the negative  as we rein silico predictors to filter out putatively non-deleterious variants, we remove more functionally neutral variants from Africans than from Europeans. For instance, the number of non-deleterious PAVs per individual increases with African ancestry, whereas the number of deleterious PAVs per individual does not. Furthermore, because the number of deleterious mutations in African individuals is not greater than in European individuals252627One possible explanation for this general reduction of the positive correlation with ancestry is that these filters effectively remove functionally neutral variants, of which there are more in persons of African ancestry. Assuming this, one would predict a reduction in the positive correlation with African ancestry, as long as the filters remove a higher number of functionally neutral variants, relative to causative variants, from African populations compared with European populations. Given recent studies showing that African populations have more genetic variation than European populations52325262728P≤10−15, χ2-test of independence), with predictors that use clinical databases to train their algorithms being over represented in deleterious PAV calls, and algorithms that are agnostic to clinical databases making up a larger percentage of the deleterious NAV calls  switches to a significant negative correlation . An analysis of differences between the March and April 2014 releases identifies 68 single-nucleotide polymorphisms (SNPs) that drive this marked change, and more details are presented in the supplement  are not enriched for those genes associated with known Mendelian diseases or those found in the GWAS catalogueTo explore ancestral biases at a gene level, we evaluated the correlation between the number of PAVs per gene and African ancestry using the March 2015 release of ClinVar. After correcting for multiple testing, we found a significant negative correlation with African ancestry for 10 genes . These gThe ability to accurately report whether a genetic variant is responsible for a given disease or phenotypic trait depends in part on the confidence in labelling a variant as pathogenic. Such determination can often be more difficult in persons of predominantly non-European ancestry, as there is less known about the pathogenicity of variants that are absent from or less frequent in European populations. A key part of this are the differences between pathogenic variants, deleterious variants and prioritized variants, which are merely members of the proverbial haystack with differing levels of evidence for potential disease causality. It is important to note that a deleterious variant will only be labelled as pathogenic if its effect size is large enough to directly cause disease and this effect has been seen and annotated, and that a pathogenic variant will only be deleterious if it negatively impacts reproductive fitness. These terms are not the same, nor synonyms of true causality, but the use of deleteriousness as evidence for true disease causality is predicated on the fact that deleteriousness and pathogenicity should be correlated. While we cannot be sure which of these variants are truly disease-causing  without additional functional or association-based evidence, we believe that discrepancies between true pathogenicity and annotated pathogenicity are a major source of the biases we report. A likely contributor to this incongruity is that databases are missing population-specific pathogenicity information, and with regard to the results we report here, African-specific pathogenicity data. Therefore, true causal variants for predominantly non-European patients are likely to fall into the NAV categories. Since NAVs have the highest degree of positive correlation with African ancestry , causal variants falling into this group are more difficult to distinguish, as they exist amongst a larger number of high-priority background variants . This problem is compounded in individuals of substantial African ancestry, as their larger amount of overall genetic variation523As a consequence, review of genomic test results for persons of predominantly non-European ancestry could be both more challenging and costly. Positive correlations of African-ancestry proportion with non-deleterious PAVs and deleterious NAVs result in more variants to evaluate for African-ancestry individuals , which leads to higher costs and longer turnaround times. Assuming a cost of $500 per variant for Sanger confirmation in a CLIA-certified laboratory , as well as those entirely absent from clinically annotated databases, and are also subdivided into deleterious and non-deleterious subgroups. Using customized ANNOVAR index tables, we annotate variants with 11 in silico predictors of function111213141516171819http://exac.broadinstitute.org) and the Exome Sequencing ProjectWe annotate all variation using ANNOVARin silico prediction, and require that at least 2 of 11 in silico prediction methods identify variants as deleterious  and provides a catalogue of genetic diversity from multiple populations of African descent. These populations include individuals from North America, South America, the Caribbean and continental Africaet al.N=∼950) containing additional currently unpublished CAAPA data, our results were unchanged.CAAPA consists of high coverage (∼30 × ) whole-genome sequence data . This results in 167,987 SNPs for admixture analysis.To estimate ancestry we combine the CAAPA data with phase 1 of the 1000 Genomes Project, and data from previously published studies, which genotyped Hispanic and Native American samples on an Affymetric 6.0 chip36We estimate ancestry proportions using the software package ADMIXTUREP values are from the ‘weights'Owing to our population sampling approach, the full cohort does not represent an unstructured selection of individuals of African ancestry. To account for this when performing correlation analysis, we use the approach implemented in the R package ‘psych'The whole-genome sequence data referenced in this study were generated by the CAAPAHow to cite this article: Kessler, M. D. et al. Challenges and disparities in the application of personalized genomic medicine to populations with African ancestry. Nat. Commun. 7:12521 doi: 10.1038/ncomms12521 (2016).Supplementary Figure 1, Supplementary Tables 1-6, Supplementary Note 1, Supplementary Discussion, Supplementary Methods and Supplementary References