ERF was also found to show significant association with bladder cancer risk in an independent population, was present in other prostate cancer-affected members in the pedigree, and showed evidence for altering the function of the associated protein. This evidence supports ERF (ETS2 Repressor Factor) as a bladder and prostate cancer predisposition gene.Here we applied a powerful predisposition candidate gene identification strategy to identify rare variants shared by two related bladder cancer cases who were members of pedigrees exhibiting a significant excess of bladder cancers. We sequenced the exomes of pairs of related bladder cancer cases belonging to high-risk bladder cancer pedigrees to identify rare, shared variants shared as candidates for predisposition. A rare, shared variant in ERF (ETS Repressing Factor) was significantly associated with bladder cancer risk in an independent population, was observed to segregate with bladder and prostate cancer in relatives, and showed evidence for altering the function of the associated protein. This finding of a rare variant in ERF that is strongly associated with bladder and prostate cancer risk in an extended pedigree both validates ERF as a cancer predisposition gene and shows the continuing value of analyzing affected members of high-risk pedigrees to identify and validate rare cancer predisposition variants.Pairs of related bladder cancer cases who belong to pedigrees with an excess of bladder cancer were sequenced to identify rare, shared variants as candidate predisposition variants. Candidate variants were tested for association with bladder cancer risk. A validated variant was assayed for segregation to other related cancer cases, and the predicted protein structure of this variant was analyzed. This study of affected bladder cancer relative pairs from high-risk pedigrees identified 152 bladder cancer predisposition candidate variants. One variant in  ERF identified as a candidate was also found to show significant association with bladder cancer risk in an independent population, it was present in other prostate cancer-affected members in the pedigree in which it was identified, and the variant was predicted to alter the function of the associated protein. ERF (ETS2 Repressor Factor) is a protein coding gene that is a member of the E26 transcription factor family which may regulate other genes involved in cellular proliferation.Bladder cancer is not often recognized to cluster in families, and inherited variants are not thought to be a major risk factor, although an inherited contribution to predisposition has been suggested ,2,3. StuThe Utah Population Data Base (UPDB) resource includes the genealogy of the Utah founders in the mid-19th century to their modern-day descendants. Approximately 3 million individuals in the UPDB are part of at least three generations of genealogy that descends from a Utah founder. These individuals with extensive genealogy were analyzed here . The UPDA decades old biorepository was accessed to obtain the germline DNA samples analyzed here. This biorepository consists of DNA samples from ~36,000 members of Utah high-risk cancer pedigrees studied over many decades; many different cancer types were studied, and members of these pedigrees with cancers of any site were sampled when available. The DNA samples for 189 individuals who have linked genealogy data and a confirmed diagnosis of bladder cancer recorded in the UCR were identified; 79 of these bladder cancer cases also had a UCR confirmed diagnosis of prostate cancer. These individuals with both bladder and prostate cancer diagnoses were primarily ascertained for their membership in a high-risk prostate cancer pedigree and therefore are overrepresented in our ascertainment of sampled bladder cancer cases. All genetic relationships among the 189 sampled individuals with bladder cancer were analyzed to identify 103 independent descending pedigrees containing at least 2, and up to 11, of the sampled, related bladder cancer cases. By comparing the observed number of bladder cancer cases among the descendants in each of these pedigrees to the expected number , 9 pedigrees that included a sampled pair of bladder-cancer-affected cousins and exhibited a significant excess of bladder cancer cases (high-risk pedigrees) were identified for analysis.p < 0.05) of the number of bladder cancer cases observed divided by the number of cases expected among the descendants was used to classify the pedigree as high-risk.The sampled bladder cancer pedigrees at high-risk for bladder cancer were identified as follows. All ~3 million individuals in the UPDB with extended genealogy data as described above were assigned to a sex-, 5-year birthyear range-, and birth state- (Utah or not) cohort. The cohort-specific rate of bladder cancer was estimated for each cohort as the number of bladder cancer cases with genealogy data in the cohort divided by the total number of UPDB individuals with genealogy data in the cohort. The observed number of bladder cancer cases in the pedigree was counted; the expected number of bladder cancer cases in the pedigree was estimated by summing the cohort-specific rates of bladder cancer for all descendants in the pedigree. A statistical excess (Whole exome sequencing (WES) was performed on the bladder cancer case cousin pairs from each of the nine high-risk pedigrees at the University of Utah Sequencing Facility. DNA libraries were prepared from 1.5 micrograms of DNA using the Agilent SureSelect Human All Exon V6+UTR capture kit. Samples were run on the Illumina HiSeq  2000 instrument. Reads were mapped to the human genome GRCh37 reference using BWA-mem for alignment and variants were called using Genome Analysis Toolkit version 3.6.0.1 (GATK) software  following Broad Institute Best Practices Guidelines. Exome capture resulted in an average of 87% of target bases being covered by greater than 10× coverage across the exome with an average depth of 90x. Variants were annotated with Annovar, which contains predicted pathogenicity scores from multiple in-silico functional prediction algorithms. Rare coding variants were selected with a cutoff frequency of ≤0.005. Each cousin pair was assessed individually for concordant rare variants.p < 1 × 10−5, MAF < 0.005, duplicated position in the HRC’s reference genome, or site not included in the HRC’s reference genome. The remaining QC-passing SNPs were converted to human genome B37 forward strand orientation with GenotypeHarmonizer software  [ERF variant was also considered for association with prostate cancer risk in a set of 5,129 Finnish prostate cancer cases and 3,506 cancer free controls; genotype data were imputed genomes of the iCOGS and OncoArray studies.Each of the rare candidate variants identified as shared in the bladder-cancer-affected cousins in at least one high-risk bladder cancer pedigree were considered independently for association with bladder cancer risk if there were variant data available in a set of 2294 bladder cancer cases and 22,940 ancestrally matched controls selected from UKBiobank . UKBiobank contains 488,377 total subjects genotyped on the Illumina OmniExpress high density SNP array . The avaerlands)  and serverlands)  and MINIerlands)  with defERF protein was retrieved from UniProt (Uniprot ID: P50548) [Following our previous approaches to demonstrate the usefulness of protein prediction methods to elucidate pathogenicity ,21,22,23 P50548) . The var P50548)  on intenThe nine high-risk pedigrees selected for analysis each included a pair of bladder cancer-affected cousins (first to third-cousins); eight pedigrees had two sampled cases each and one pedigree had three sampled cases; one individual was in two independent pedigrees through different ancestors . Exome sequencing of the 18 bladder-cancer affected individuals in the 9 extended pedigrees identified a total of 14,283 exonic variants in 7545 genes at MAF < 0.005 in EXAC. Of these, 6738 were non-synonymous, frameshift indel, stopgain or splice variants; 152 of these rare variants were concordant between at least one sequenced pair of bladder cancer-affected cousins. These 152 candidate bladder cancer predisposition variants are listed in MSH2 variant are at increased risk of urinary tract cancer including bladder cancer [MSH2; two of the three cases shared the known MSH2 PV segregating in the pedigree. Patients with Lynch syndrome carrying an r cancer . No genec19orf40  and ERF . The c19orf40 variant was observed in the 2 bladder-cancer cousin cases in which an MSH2 PV was also observed, and was not pursued here. In the association study of Finnish prostate cancer cases and controls the ERF variant was observed in three cases and three controls . Only five prostate cancer cases had a family history, and none of these carried the variant.Eighty-six of the 152 candidate variants had imputed data available and were tested for association with bladder cancer risk in the 2,294 bladder cancer cases and 22,940 controls from UKBiobank. Only 2 of the variants independently showed significant association with bladder cancer: ERF variant rs144812092 was originally observed in a pair of bladder-cancer-affected first cousins. Each of these bladder cancer cases had also been diagnosed with prostate cancer decades before their bladder cancer diagnosis, which occurred in their late 70s and late 80s, respectively. The histology of the bladder cancers in the affected cousin pair were transitional cell carcinoma and papillary transitional cell carcinoma, respectively. The bladder cancer-affected cousin carriers were members of a previously sampled high-risk prostate cancer pedigree, shown in ERF variant  assay: C__25967527_10) to test for segregation of the variant with cancer. Many additional carriers of the variant were identified, including seven additional carriers diagnosed with prostate cancer, and variant carriers diagnosed with both male and female breast cancer, lung cancer, leukemia, and lymphoma. As expected, the variant was not observed in all prostate cancer cases. This includes a prostate cancer case diagnosed in their late 40s who is a member of a branch in which variant carriers were observed. While this is surprising, there are many explanations for this observation, including the presence of additional predisposition variants in cases, mispaternity, or misdiagnosis. The male founder of the pedigree  was born in the early 1800s in Scotland and has >3500 descendants in the current UPDB . Cancers observed in statistical excess among all descendants based on comparisons with cancer rates in the UPDB include: endometrial  and prostate ; a borderline excess of bladder cancer cases (n = 7) was observed  in the pedigree. None of the other bladder cancer cases in the pedigree had samples available for assay.The In ERF (rs144812092) that was significantly associated with bladder cancer risk. This variant was also found to be present in multiple cancer-affected relatives of the original bladder-cancer-affected cousin pair, who were members of an extended high-risk prostate cancer pedigree. Protein prediction modeling of the variant suggested biologically meaningful effects to the protein. These results suggest the ERF variant (rs144812092) predisposes to bladder, prostate, and perhaps additional cancers observed.Sequence analysis of a set of bladder cancer-affected cousin pairs who belonged to pedigrees with a significant excess of bladder cancer was performed to allow identification of rare, shared candidate bladder cancer predisposition variants. Analysis of available data from an independent population for the resulting set of candidate variants identified a variant in ERF aliases include ETS domain-containing transcription factor EFR, and ETS2 Repressor Factor. ETS2 is a transcription factor and protooncogene involved in development, apoptosis, and regulation of telomerase; ERF acts as tumor suppressor by binding the protooncogene ETS2 promoter. ERF has been reported to be downregulated in prostate cancer. ERF rs144812092  is a rare missense variant. Frequency estimates range from 0.00054  to 0.00066 ; 2 of 12 algorithms predict the variant as damaging; the GERP score = 2.42, and the variant has only been reported in ClinVar as benign in relation to craniosynostosis. These results may appear to contradict the pathogenic findings reported here; however, it has been recognized that the GERP score is not always a good indicator of pathogenicity [genicity , and thaERF has been identified as a prostate cancer tumor-suppressor gene in a study of localized primary prostate tumors from 102 African-Americans [ERF were observed in 5% of cases. A germline analysis of ERF identified a different rare germline missense variant (S295I) in one high-risk prostate cancer patient in this cohort [ERF deletions were seen in 3% of primary prostate cancers and deletions of ERF were seen in 3–5% of lethal castration-resistant prostate cancers [ERF conferred increased anchorage-independent growth and generated a gene expression signature associated with oncogenic ETS activation and androgen signaling. Additionally, Bose [ERF cause decreased protein stability, and most occur in tumors without ERG upregulation; they argue that the oncogenicity of ERG is mediated, in part, by competition with ERF, and that overexpression of ERF blocks ERG-dependent tumor growth, and loss of ERF rescues TMPRSS2-ERG-positive prostate cancer cells from ERG dependency.mericans  in whichvariant S5I in one cancers ,31. It wly, Bose  showed tLimitations of this study include potential censoring, which could include individuals in the pedigree whose genealogy was not available or not linked, or individuals whose cancer was diagnosed outside Utah or before 1973. Utah’s founders were primarily of Northern European ancestry , so the ERF as a prostate cancer gene, these observations additionally confirm the role of this rare ERF variant in familial prostate cancer. The observation of variant carriers exhibiting various cancers of other sites suggests a potential role in predisposition to more than just bladder and prostate cancers, but further studies are warranted. This study exemplifies the power and efficiency of the high-risk pedigree approach used to identify rare predisposition variants in high-risk cancer pedigrees as well as the use of powerful structural bioinformatics methods to provide mechanistic insights on pathogenesis.In combination with previous work suggesting


Despite a major interest in understanding how the endothelial cell phenotype is established, the underlying molecular basis of this process is not yet fully understood. We have previously reported the generation of induced pluripotent stem cells (iPS) from human umbilical vein endothelial cells and differentiation of the resulting HiPS back to endothelial cells (Ec-Diff), as well as neural (Nn-Diff) cell lineage that contained both neurons and astrocytes. Furthermore, the identities of these cell lineages were established by gene array analysis. Here, we explored the same arrays to gain insight into the gene alteration processes that accompany the establishment of endothelial vs. non-endothelial neural cell phenotypes. We compared the expression of genes that code for transcription factors and epigenetic regulators when HiPS is differentiated into these endothelial and non-endothelial lineages. Our in silico analyses have identified cohorts of genes that are similarly up- or downregulated in both lineages, as well as those that exhibit lineage-specific alterations. Based on these results, we propose that genes that are similarly altered in both lineages participate in priming the stem cell for differentiation in a lineage-independent manner, whereas those that are differentially altered in endothelial compared to neural cells participate in a lineage-specific differentiation process. Specific GATA family members and their cofactors and epigenetic regulators  with a major role in regulating DNA methylation were among participants in priming HiPS for lineage-independent differentiation. In addition, we identified distinct cohorts of transcription factors and epigenetic regulators whose alterations correlated specifically with the establishment of endothelial vs. non-endothelial neural lineages. Cellular differentiation is fundamental in the development of multicellular organisms and has long been a subject of intense investigation. With current advancements in gene and cell therapy, a clear understanding of the cellular processes that are evoked to establish a specific cell phenotype are central towards designing appropriate cellular manipulations for therapeutic purposes. Among various cell types, endothelial and neural cells are highly attractive targets for therapeutic manipulation, due to their involvement in a multitude of prevalent vascular and nervous system disorders. Dysfunction of endothelial cells that cover the lumen of all blood vessels is a major contributing factor to many cardiovascular, peripheral vascular and cerebrovascular diseases, and many diseases of the central nervous system involve damage to neurons/neural cells. Exploration of the mechanism of endothelial cell specific gene regulation, as well as gene knock out animal models and stem cell differentiation approaches have led to identification of a number of transcriptional regulators that have emerged as major participants in the establishment of the endothelial cell phenotype ,2,3,4. TThe generation and manipulation of stem cells, especially induced pluripotent stem (iPS) cells, combined with high throughput gene expression and computational biology advancements, have provided unprecedented opportunities to design and implement in vitro experimental approaches towards addressing these questions. These approaches have been successfully used to generate a wealth of information regarding changes in gene expression patterns that accompany the establishment of specific cell phenotypes, including endothelial and neural cells ,16. HoweA potential approach towards addressing this gap in knowledge is to compare the molecular signature of two distinct cell lineages that are derived from one parental stem cell, thus possessing a similar genetic background with each other as well as with the parental stem cell. We have previously reported the generation of induced pluripotent stem cells (iPS) from human umbilical vein endothelial cells (HUVEC) the and subsequent differentiation of the resulting iPS (referred to as HiPS) back into a highly pure population of endothelial cells (Ec-Diff), as well as a population of neural cells that contained both neurons and astrocytes (Nn-Diff) ,18. AlthA comparison of HUVEC, HiPS and EC-Diff transcriptome by gene array provided information regarding transcription regulatory factors, including chromatin and DNA modifiers, as well as cofactors as potential contributors to the establishment of endothelial cell phenotype . FurtherBased on the criteria described in Methods, genes that were significantly altered in HiPS-derived endothelial (EC-Diff) or neural (Nn-Diff) cells (relative to HiPS) were identified and a scatterplot of differentially expressed genes generated. To gain insight into the nature of the genes that were altered, they were sorted according to gene ontology, generating a hierarchical structure that is based on their functional characteristics . Genes wFor both endothelial and neural lineages, signaling was the largest group of genes with significant changes, followed by transcription factors and adhesion subsets . The lowWe propose that transcription factors that are upregulated in both lineages  participFurthermore, we suggest that transcription factors, which were downregulated in both lineages , are inhThe coregulation of GATA 3 and 6 transacting factors, together with their regulators ZFPM2 (FOG2) and HEY2, may suggest their similar contribution towards differentiation of both neuronal and endothelial lineages. Among the genes upregulated in both lineages (NR2F2 (COUPTFII), MEF2C, HOXB2, HOXA4 and RARB), NR2F2 (COUPTFII) and MEF2C have previously identified roles in both endothelial and neuronal lineage specification ,25. HOXBWe propose that transcription factors that are specifically upregulated in endothelial cells but remained unchanged in neuronal cells are those that actively participate in establishing endothelial cell phenotype . The lisNotably, the top 10 most highly upregulated transcription factors in this group were ERG, FLI1, TAL1, EPAS, MECOM, TFEC, HOPX, SOX17, SOX7 and SOX18. Among this group, ERG and FLI1 are the members of the ETS family of transcription factors with well-established endothelial-specific gene regulatory function . TAL1 haMECOM is a transcription factor that, in addition to DNA-binding activity, exhibits chromatin-modifying functions ,47. It iThe SOX family of transcription factors is known to participate in cell fate determination, including maintenance of stem cell phenotype . Indeed,https://www.genecards.org/cgi-bin/carddisp.pl?gene=ZNF90, accessed on 23 April 2021).The establishment of endothelial cell phenotype may also be associated with the repression of specific transcription factors, which if present would interfere with the process. While low levels of some transcription factors may indicate no significant change during endothelial differentiation, others may be actively repressed, which could suggest a requirement for their specific downregulation to promote endothelial differentiation. Analyses of transacting factors that are specifically downregulated in endothelial cells  will proThe results presented here suggest the establishment of endothelial cell phenotype as being associated with the upregulation of specific SOX, ETS and GATA transcription factor family members, TFEC and HOXP that participate in hemato-endothelial progenitor establishment, EPAS1 that is responsive to oxygen levels and transcription factor MECOM with multiple chromatin modification functions. It is noteworthy that a number of these transcription factors have an expression pattern that is not restricted to endothelial cells, suggesting a potential combinatorial role for this cohort of transacting factors in establishing endothelial cell phenotype. Additionally, of interest is the GATA transcription factor family, which includes members such as GATA2 that participate in endothelial lineage establishment, and others such as GATA3 and 6 that are associated with the lineage-independent differentiation process.Among the factors that were downregulated in endothelial cells, two were of special interest since they were specifically upregulated in neuronal cells , thus suBased on similar reasoning to that used for endothelial cells, we propose that genes that are specifically upregulated and downregulated in neural cells are those that participate in the establishment of neural cells phenotypes. In the category of the genes that were upregulated in neural cells , the majAs discussed for endothelial cells, the establishment of neural lineage may also require downregulation of certain transcription factors. Seven transcription factors, including FOXO1, MEOX2, ZSCAN10, NFE2L3, HMX2, ARNTL2 and TCF19, were specifically downregulated in neural cells . FOXO1, Another significantly downregulated transcription factor was MEOX2, which has a major role in vascular biology . MEOX2 iAs expected, genes in this category not only included factors that are reported to participate in the maintenance of pluripotency, such as ZSCAN10  and NFE2Overall, these results suggest that differentiation towards neural phenotype is concomitant with the downregulation of genes that regulate: (i) specific cellular metabolisms, (ii) circadian cycles, (iii) response to external cues that could trigger apoptosis, (iv) vascular cell specification and (v) maintenance of stem cell phenotype.We also identified a number of transcription factors that were upregulated in one lineage and downregulated in the other. Identification of these factors provides a novel insight towards dual functioning transacting factors that may function as an activator of genes required for establishing one lineage while simultaneously repressing genes associated with establishment of a different lineage. We did not identify any transcription factors that were specifically upregulated in endothelial cells while being downregulated in neural cell lineage. However, we did identify two transcription factors, IRX2 and IRX3, that were upregulated in neural and significantly downregulated in endothelial cells . IRX2 anIRX2 was shown to participate in cerebellum formation and potentially the ventricular conduction system (VCS) ,90, whilEpigenetic modification of genes is another process that significantly contributes to activation/repression of gene expression . As discDNMT3B, PRDM14 and HELLS were identified as epigenetic regulators that were downregulated in both endothelial and neuronal lineages . DNMT3B PRDM14 (a member of the PRDM family of epigenetic regulators with histone methyl transferase activity ) is a DNGenes coding for epigenetic factors that were significantly altered in endothelial cells included three genes  that were upregulated  and eighPCGF5 is a histone modifier (mediates ubiquitylation of histone H2A) which exerts both repressive and activating functions and was reported to participate in neuronal differentiation . Thus, iIn addition to alteration in DNA methylation, histone modifications and chromatin remodeling, epigenetic regulation of gene expression may also encompass regulation of mRNA levels. This may involve the function of non-coding RNAs (such as miRNA and lncRNA) or RNA binding proteins that regulate target mRNA levels (including mRNAs coding for epigenetic regulators as well as transcription factors) ,112.Of the eight genes in the epigenetic category that were downregulated , LIN28A,TET1 is a member of TET (ten–eleven translocation) family of dioxygenase enzymes that participate in DNA demethylation at the 5-methylcytosine (5mC) base, and thus contributes to an epigenetic modification generally consistent with transcriptional activation . HoweverThe cohort of genes with epigenetic function that were altered in neural cells included two genes (BCORL1 and LHX2) that were upregulated  and one Overall, epigenetic regulators, although generally lacking inherent specificity, exhibit differential expression patterns based on cellular phenotype and may exert lineage-specific effects based on their level of expression. Additional measures of specificity could be added through association of many epigenetic regulators with DNA binding transcription factors that recruit them to the target promoters. Recently, distinct RNA binding proteins have been identified as major epigenetic regulators, which exhibit lineage specificity and could present novel targets for exploring the epigenetic regulation of cell fate outside the chromatin structure ,135,136.Collectively, based on the results of the gene array analyses, we posit that the establishment of a distinct cell fate from pluripotency involves progression through stage(s) that independent of the final phenotype destination, poises the cell for differentiation. This process involves alterations in gene expression pattern compared to the pluripotency state and is independent of the final-destination phenotype. Once a cell is poised to differentiate, establishment of the final-destination phenotype  involves additional alterations in gene expression pattern that are lineage specific. Although these alterations include genes belonging to multiple functional groups, those involved in transcription regulation, including transcription factors and epigenetic regulators, are uniquely positioned to alter activities of multiple networks of target genes as master regulators. Thus, we focused on these two ontology groups and propose the following model  to descrWe hypothesized that transcription factor and epigenetic genes that are commonly up- or downregulated in both endothelial and neural lineages potentially promote lineage-independent pro-differentiation phenotypes. However, these analyses will not allow distinguishing among cells that may be poised to differentiation towards all, multiple, or a select few phenotypes. Furthermore, since cells differentiated into neural lineage constituted both neurons and astrocytes, the gene array analyses from these cells’ populations do not provide information regarding the neuronal- vs. astrocyte-specific gene expression profile. However, considering that both neurons and astrocytes belong to neural lineages, it remains informative with regard to exploring alteration in gene expression that occurs when iPS are differentiated towards endothelial compared to a distinct non-endothelial neural lineage. Notwithstanding the caveats, we propose that being poised to differentiate is associated with the downregulation of three epigenetic regulators  with major roles in regulating the DNA methylation profile. Since two of the three (DNMT3B and HELLS) are associated with de novo methylation, while PRDM14 has a demethylation function, their downregulation may alter the epigenetic landscape of a stem cell to prime lineage-specific genes for activation, while rendering stem cell-specific genes poised for repression. These observations highlight alterations in DNA methylation patterns as a major epigenetic event in priming a pluripotent cell for differentiation. Additionally, this process appears to be strongly associated with a major increase in GATA transcription factor activity, since an increase in the gene expression of select GATA family members  as well as GATA cofactor (ZFPM2), in combination with the downregulation of GATA repressor (HEY2), is prominent. The establishment of neural- or endothelial-specific lineages then requires additional gene expression alterations  that are restricted to the target lineage. While these results require experimental confirmation, it provides a potential list of target genes that could be prioritized for experimental approaches to determine their role individually and/or collectively in differentiation process.p-value from the fcros’ package was above 0.95 and their mean change in expression was greater than 1.5. Similarly, genes were considered significantly downregulated if their adjusted p-value from fcros’ was below 0.05 and their mean change in expression was less than −1.5.The initial dataset contained 49,400 observations for 19,929 unique genes ,18. Each


Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)–based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools. More recently, pathology has formed a bridge between drug discovery, translational, and clinical research programs that are striving to decipher disease pathophysiology in the context of the mechanism of action, patient selection, or patient stratification  considers evidence of efficacy on a histological endpoint to support initiation of phase 3 trials7. Additionally, pathological complete response (pCR) has been studied as a surrogate endpoint in patients with cancer for the prediction of long-term clinical benefit and favorable prognosis with the administration of neoadjuvant therapy13. More recently, pCR was associated with improved long-term efficacy in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer treated with chemotherapy plus either intravenous or subcutaneous trastuzumab14. In the immuno-oncology (I-O) arena, immune-related pathologic response criteria have been applied retrospectively to surgical specimens from patients treated with immunotherapy in the neoadjuvant or advanced disease setting to predict survival in several tumor types16.Pathology-based assessments have been used to classify disease and determine efficacy in drug development across a variety of disease areas17, but challenges pertaining to differences in laboratory methods and subjective interpretation, particularly with the evaluation of immune cell staining, may lead to inter-observer variability18. This can produce inconsistency in diagnoses, which may impact treatment decisions23. While the use of IHC assays has led to better identification of patients who respond to I-O therapy26, there remains a need to more accurately quantify complex immune markers, including cell phenotypes in a spatial context, that require advanced quantitative tools to maximize the amount of information yielded from individual samples28.Immunohistochemistry (IHC) has been used to characterize biomarkers, such as programmed cell death-ligand 1 (PD-L1), and their association with clinical benefit. Traditional pathology techniques present several advantages, such as low cost, widespread availability, and application on formalin-fixed, paraffin-embedded (FFPE) tissue samples28, indicating the need for more complex computational approaches to decipher the underlying biology and enhance clinical utility.Artificial intelligence (AI) applications in pathology improve quantitative accuracy and enable the geographical contextualization of data using spatial algorithms. Adding spatial metrics to IHC can improve the clinical value of biomarker identification approaches. For example, in a recent meta-analysis, the addition of spatial context to IHC, achieved using multiplex IHC and immunofluorescence (IF), was significantly better at predicting objective response to immune checkpoint inhibitors (ICIs) compared with gene expression profiling (GEP) or IHC alone29.The development and integration of digital pathology and AI–based approaches provide substantive advantages over traditional methods, such as enabling spatial analysis while generating highly precise, unbiased, and consistent readouts that can be accessed remotely by pathologists31. Two scanners, Philips IntelliSite Pathology Solution (PIPS)  and Leica Aperio AT2 DX System , are approved by the FDA for review and interpretation of digital surgical pathology slides prepared from biopsied tissue33.Efforts to overcome some of the challenges seen with traditional pathology methods have led to the development and adoption of complex, novel imaging systems and whole slide image (WSI) scanners that have enabled the transition of pathology into the digital era, also known as digital pathology. Within minutes, WSI scanners capture multiple images of entire tissue sections on the slide, which are digitally stitched together to generate a WSI that can be reviewed by a pathologist on a computer monitor Fig. 30,31. Tw37. The increased speed and efficiency gained in image acquisition can enhance the downstream utilization options of traditional techniques such as hematoxylin and eosin (H&E), IHC, and in situ hybridization. These slides can be converted into a remotely available image within minutes and centrally reviewed by multiple pathologists from various sites29, with applications including education, research, consultation, and diagnostics29.There are many practical advantages to using these digital pathology image systems and solutions that would bring substantial benefits to translational and clinical research. These include the organization and storage of large amounts of data in a centralized location, integration of digital workflow software to help streamline processes and improve efficiency, convenient sharing of image data to enable cross-specialty worldwide remote communication, reduced testing turnaround time, and the generation of precise and highly reproducible tissue-derived readouts reducing inter-pathologist variability38. Without the need to transport glass slides and the ensuing logistical and safety concerns, central pathology review enables secure remote working38. Additionally, the utilization of digital images allows the generation of pixel-level pattern information, leading to expanded use of computational approaches that enable a quantitative analysis of WSIs40.Recently, due to ongoing disruptions in relation to the COVID-19 pandemic, including remote working and restricted travel, digital pathology has been crucial in the continuation of clinical and academic research, as well as routine pathology services42. Quantitative image analysis tools also enable the capturing of data from tissue slides that may not be accessible during manual assessment via routine microscopy. Additionally, performing similar tasks manually can require significant time investment and can be prone to human error, such as counting fatigue44.The use of digital image analysis in pathology can identify and quantify specific cell types quickly and accurately and can quantitatively evaluate histological features, morphological patterns, and biologically relevant regions of interest 46. Current imaging metrics can utilize multispectral unmixing strategies to reveal co-expression patterns that define unique cell phenotypes and spatial relationships  to predict recurrence in early-stage non-small cell lung cancer (NSCLC) from H&E slides64. Deep learning has also been used to construct entity-graph-based tissue representations, where cell morphology and topology are embedded within each node to effectively describe the phenotypical and structural properties of tissues and can be processed by graph neural networks (GNNs). GNNs therefore enhance the interpretability of pathological assessments gleaned from neural networks66.Deep learning takes machine learning a step further, using sophisticated, multilevel deep or convolutional neural networks (DNN or CNN) to create systems that perform feature classification from large datasets67. In the CAMELYON16 challenge, deep learning algorithms to detect breast cancer metastases in H&E-stained WSIs of lymph node sections performed similarly to the best performing pathologists under time constraints in detecting macrometastases and were better in detecting micrometastases68. However, it should be noted that the performance of any algorithm will depend on the task, due to the degree of accuracy required and the quality of the samples to be assessed37.It is important to compare AI-based interpretations with those of the pathologist to define the associated algorithm’s performance characteristics and utility. For example, when a CNN trained to classify melanoma samples was compared against manual scoring by histopathologists, the CNN was significantly superior in classifying images as malignant melanoma or benign nevi compared with manual assessment by histopathologists70. In a comparison of TP determined using AI  and manual estimates by pathologists, AI-assessed TP was found to be more accurate than visual assessment by pathologists71. Previous evidence has shown that immunosuppressive pathways are upregulated in patients with low TP, suggesting that low TP is associated with poor prognosis in some tumor types, including gastric cancer. Therefore, improved methods of evaluating TP may also aid in the identification of patients who may be suitable for immunotherapy72.Another application of machine learning in the preclinical space is the assessment of tumor purity (TP). TP estimation, currently evaluated visually by pathologists, is used to ensure a signal is derived from cancer cells rather than other noncancerous cells that may be present in the TME based on tissue morphology when tissue is used to generate orthogonal data such as transcriptome or exome58. Data used for training need to be accurate and as complete as possible in order to maximize predictability and utility39. This can be challenging when histological data are obtained from various laboratories, leading to some variability due to factors such as differences in slide preparation 73, scoring algorithms18, and inherent inter-observer variability74. These challenges become more apparent when more complex computational analytics methods are used for multiplexed imaging. Although AI could be used to overcome inter-reader variability across multiple institutions with the development of robust algorithms that take specific histological features of various tumors and subtypes into account75, further research is needed to fully understand the impact of these factors on the quality of AI data.Given the amount of additional detail and insights that can be gained from combining WSI with machine learning algorithms, this technology can be readily applied to translational research. However, one major limitation of machine learning is the large amount of high-quality data required to develop these algorithms52. IHC can be used to gain insights into the composition of the TME by facilitating the identification of different cell types expressing a protein of interest and assessing the density and spatial distribution of specific biomarkers50. Digital pathology approaches, such as quantitative analysis of TILs, present an opportunity to gain greater insight into intra-tumor heterogeneity, spatial patterns of cell phenotypes, and the complex interactions between cancer and the immune system within the TME53. Image-based techniques can be used to determine immune cell responses to immunotherapy such as macrophage activation76 or lymphocyte infiltration by regulatory T cells (Tregs) into core tumor regions in solid tumors77, which may in turn have value as a predictive indicator for the effectiveness of ICIs. Favorable cancer prognosis has also been associated with factors in the TME, including high CD8+ TIL rates79. Recently, image analysis and AI methods have contributed to the development of novel approaches to concurrently assess multiple biomarkers in preclinical and exploratory studies, revealing complex interactions within the TME and providing the potential to improve cancer diagnosis and the selection of treatment regimens. Combining multiple techniques, such as multiplex IF, with image analysis has yielded important insights into specific immune cell populations, such as those in the TME of classical Hodgkin lymphoma, and their associations with PD-1/CTLA-4+/− T cells80. These studies require multiple large cohorts to add the scale and robustness necessary to gain these important insights, to elucidate relationships that may not be apparent to the human eye, and to help overcome observer bias that may mask potential biomarker signals.Tumor evolution and progression involve many complex cellulars and molecular interactions that are spatially and temporally regulated within the TME82. Spatial analysis in bladder and gastric cancer tumor cells has demonstrated that the density and proximity of LAG-3+ were significantly greater when associated with MHC II+ vs. MHC II− tumor cells, suggesting that LAG-3–expressing TILs may be preferentially located in proximity to MHC II+ tumor cells, allowing for LAG-3 activation and the inhibition of antitumor immunity83. The insights provided by digital pathology into the number and location of immune cells relative to tumor cells may provide information on immune response84, which could guide future treatment strategies. AI has also been used to quantify immune cells within the TME to define T-cell abundance and associated geographic localization in the tumor stroma, parenchyma, parenchyma-stromal interface, and invasive margin, which are then associated with transcriptomic factors to define underlying biological associations85.Digital pathology can also be used to gain insights into a receptor-ligand binding, as proximity may be indicative of receptor engagement and activation. For example, lymphocyte-activation gene 3 (LAG-3), expressed on exhausted T cells, principally interacts with major histocompatibility-II (MHC-II) molecules, expressed on the surface of antigen-presenting and tumor cells87, AI may be particularly useful for evaluating genomic instability and the mutational landscape, with the possibility to assess pathologic and genomic features in conjunction with one another. A CNN trained with WSIs of H&E-stained hepatocellular carcinoma (HCC) tissue was used to predict the ten most common prognostic and mutated genes in HCC, with four of these  correctly identified by the model88. Similar results were obtained when a DNN was trained to predict the most commonly mutated genes in lung adenocarcinoma, with 6  being predicted from WSIs89. Deep learning has also been used to predict microsatellite instability (MSI) status from tumor tissue90. A CNN trained to classify MSI versus microsatellite stability was able to robustly distinguish features predictive of MSI in gastric and colorectal cancer samples90.Additionally, AI-based approaches may find applications in translational medicine and clinical practice by predicting gene mutations from routine histopathology slides. With genomic tests being associated with high costs and high rates of failure due to stringent sample requirements91. At the same time, AI algorithms cannot be applied in cases where actual variant allele frequencies of selected mutations can impact the classification and prognosis of individual diseases, such as hematologic myeloid neoplasms92.However, there are limitations to using AI for molecular classification. For example, current imaging techniques can only identify genetic variants when they directly impact tissue morphology, as described previously93, as well as the area and density of TILs and the proximity of TILs to each other and to tumor cells94. The features extracted from these models were able to distinguish patients who responded to nivolumab therapy94. In another example, digital image analysis was used to quantify CD8 and PD-L1 positive cell densities from patients treated with durvalumab across multiple tumor types95. Patients defined as positive for the CD8xPD-L1 composite signature had longer median survival compared with signature-negative patients, demonstrating the potential predictive value of digitally defined composite biomarkers.As a further application in translational medicine, digital pathology approaches have been used to predict response and identify patients most likely to respond to treatment. For example, studies have used spatial analysis to determine the response of patients with NSCLC to nivolumab therapy. These included training machine learning models to extract morphological details, such as the spatial arrangement of tumor nuclei and variance in shape and chromatin structure96. Results demonstrated that the model was more accurate in predicting patient survival than using current pathology practices and was able to identify regions contributing to patient outcomes96, suggesting that deep learning models can identify new features predictive of patient survival and potentially lead to new biomarker discoveries.AI and machine learning can also assist in classification and staging across various tumor types. A new approach to tumor subtyping has been developed based on a DNN (MesoNet) to predict OS of patients with mesothelioma from hematoxylin, eosin, and saffron stained WSIs, without any pathologist-provided annotations100. ICIs, such as anti–PD-(L)1 and anti–cytotoxic T lymphocyte antigen-4, have been studied in multiple clinical trials, leading to improved prognosis for patients across various solid tumors101. Evidence has shown that PD-L1 expression may be indicative of response to ICI therapy in some tumor types100, while other studies have shown that patients demonstrated durable responses to ICIs regardless of PD-L1 expression108. Given the widespread clinical use of ICIs, predictive assays are needed to help stratify patients to determine who may benefit from such treatments.Biomarker research has been an area of particular interest in the I-O space due to its potential predictive value in some solid tumors36.While the use of these assays can help determine whether a patient will benefit from ICI therapy, biomarker identification, such as PD-L1 status, using tumor biopsies is challenging. Even when used by experienced pathologists, visual interpretation of PD-L1 using IHC is subjective and prone to error, which may contribute to inaccurate patient stratification. Digital scoring of PD-L1 expression can assist pathologists in overcoming these barriers by providing standardized metrics for biomarker assessment at single-cell resolution across whole tissue sections111. Furthermore, similar associations between PD-L1 expression and response to nivolumab have been reported between manual and digital scoring110. Using the combined positive score to assess PD-L1 expression on tumor and immune cells, digital image analyses and pathologists’ interpretations on stained slides  demonstrated 33 (84.6%) of 39 cases had concordant results, and statistical analyses indicated that PD-L1 expression interpreted by pathologists or digital image analysis did not differ significantly for predicting responses to pembrolizumab112. Prospective clinical trials in colorectal cancer113 and NSCLC114 are also using digital image analyses to identify potential immune cell biomarkers within the TME.Multiple studies have evaluated PD-L1 assessment using digital scoring and AI algorithms and have shown that digital-based techniques can perform better than or equal to manual pathological evaluation across various tumor types. A high correlation between AI and manual assessment of PD-L1 expression on tumor and immune cells has been observed in multiple CheckMate trials with samples from NSCLC, urothelial carcinoma, melanoma, and gastric cancer115. Beyond oncology, AI and machine learning have been studied in the context of a morphological assessment of nonalcoholic steatohepatitis/nonalcoholic fatty liver disease and liver allograft fibrosis117. In these cases, AI-based methods were able to correctly reflect markers of steatotic severity116 and assess liver allograft fibrosis progression over time117.The role of AI and machine learning in biomarker identification has been evaluated in studies outside of immunotherapy. For example, a DNN model (ConvNets) trained to automatically recognize cancer cell types were compared with conventional machine learning techniques. ConvNets achieved significantly higher accuracy than conventional algorithms, suggesting a role for computer-aided diagnosis to facilitate clinical decision-making118. The goal of a HER2-directed image analysis platform is to detect and quantify HER2 membranous IHC staining of invasive breast cancer cells and to provide an accurate, precise, and reproducible quantitative HER2 result that can then be used to guide treatment decisions119. Digital image analysis has also been used to classify biological subtypes beyond HER2, including ER- and progesterone receptor (PR)–positive subtypes. Ahern et al demonstrated considerable overlap between unsupervised and supervised computational pathology platforms using image analysis to measure ER and PR expression in breast tumors between positive and negative groups, as classified by a pathologist120. While the supervised platform had a marginally higher performance than the unsupervised platform, both platforms provided meaningful results and may have important roles in future molecular epidemiology studies120.Various platforms have been developed for the purpose of quantitative image analysis. Several have received FDA approval, including those used to detect HER2128. These include detailed information on the handling of digital images in nonclinical121 and clinical122 settings, technical aspects and performance standards for WSI devices124, validation and quality assurance of digital pathology systems for nonclinical125 and clinical use126, AI concepts and best practices127, tutorials on using deep learning frameworks for image analysis128, and reimbursement considerations129. For example, the College of American Pathologists provides comprehensive guidelines to laboratories on validating their own WSI systems for clinical use, including emulation of the real-world environment, sample set size, establishing concordance using intra-observer variability, and documentation, among others30.There are several published resources for pathologists as well as for physicians, including guidelines, position papers, and directives relating to digital pathology41. Digital images used for training purposes should be obtained from multiple staining batches, scanners, and institutions to ensure generalizability. Such datasets should be curated by pathologists, ensuring that representative images have been obtained at an appropriate magnification and that all regions of interest are comprehensively annotated depending on the diagnostic application41. Crucially, the validation of AI algorithms developed for clinical purposes increases the concordance between manual and digital pathology interpretations. The role of pathologists in the validation step is equally important in order to ensure that datasets represent the sample type of interest , encompass the entirety of a glass slide, and are big enough to reveal potential interpretational discrepancies, as well as to evaluate the accuracy and performance of the algorithm119.The performance of AI applications in digital pathology is largely dependent on the size and quality of the dataset used to train an algorithm130. However, these are offered as single-site, standalone tests, thereby limiting their applicability to the wider pathology community. Studies that have evaluated the adoption of complete digital pathology workflows have shown increases in efficiency and operational utility131.Despite the advantages of incorporating digital pathology into the clinical setting, challenges remain Table . Value d132. Some level of error with digital pathology is anticipated to be present at this point, and approaches that combine algorithm performance with manual validation, with margins of error similar to or stricter than those used for manual pathology, are likely to be the standard moving forward. This approach has already been tested in routine diagnostics, whereby pathologists interacted directly with an AI platform to conduct IHC-based intrinsic subtyping of breast cancers. The AI platform, both alone and working in consort with pathologists, was significantly more accurate in determining subtypes133. Additionally, translation and adoption into clinical practice will depend on algorithms being validated across many patient cohorts utilizing data not included in the training set. This will require large amounts of data to be acquired from multiple laboratories in order to assure the broad applicability required in a clinical setting134.Technical concerns related to reproducibility, interpretability, the accuracy of competing devices, financial costs of processing hardware, and regulatory approvals that must accompany studies of clinical utility all represent barriers to adoption135. Techniques that take into account variations in real-world practice and can influence decision-making need to be evaluated in interventional studies to ascertain true clinical value134. Although a protocol for the development of a reporting guideline and risk of bias tool has been published136, no official guidelines are available yet on the numbers of annotations, images, and laboratories needed to capture the variation seen in the real-world. Additional statistical studies will be required for application to properly determine the optimal processes and workflows to ensure full implementation of this technology in clinical practice39. Algorithms would also be subject to periodic quality assurance , similar to how assays are revalidated when there is a change in workflow or procedure137. Various quality control (QC) techniques can be used to overcome preanalytical issues such as variations in slide preparation, origin, and scanner type. One approach is to train individual models of the same architecture to recognize specific variables73. Other approaches, such as combining image metrics in a QC application138, or transformation of image patches with synthetically generated artifacts139, can be used to train an algorithm to recognize different types of histological artifacts. Other unforeseen hurdles may exist once these systems are in place, including unfamiliarity with a new system and associated need for training, technical support, security, monitoring, and software integration141. In the US, software solutions should be developed under the FDA’s Quality System Regulation and Good Machine Learning Practices. However, artificial neural networks have been described as “black boxes”, whereby data can be difficult to interpret, which may lead to regulatory concerns, as image features are extracted in ways that are difficult for a human to understand142. Despite the challenges, the efficiency gains, such as faster results and higher throughput, are key motivators for pathologists to adopt digital pathology.While there have been instances of AI being used in the clinical trial setting, most have been observational studies143. One of the first applications of AI in the clinical setting is likely to be assessing multiple IHC I-O markers within a single tissue section. Application of image analysis to multiplexed IHC–stained samples offers accelerated scan times while increasing accuracy and productivity by automatically measuring parameters that may be hard to reliably achieve by eye47.The benefits of AI can be seen across all stages of the drug development process and in the clinical setting144, would allow digital pathology readouts to be compared directly with manual pathology data and clinical outcomes. In this phase, pathologists would maintain a role in QC, but with improved efficiency. Data from the evaluation of such biomarkers with digital pathology could then be used in applications to the FDA for companion diagnostic status. Subsequent steps would introduce digital pathology as a diagnostic with novel biomarkers, with the aim of demonstrating the clinical utility of the biomarker with digital quantification. This phase would require the development of AI-based software for use in prospective clinical trials to evaluate the selected biomarker for patient stratification or selection. The next phase, and the long-term goal of digital pathology, would be to establish deep learning AI models trained using large quantities of data39 that can predict patient response and stratify patients using only WSIs.In the evolving field of digital pathology, a strategy towards the implementation of digital pathology may involve several phases culminating in the adoption of digitized images and AI technology in the clinic. A first step involves demonstrating the reliability of digital pathology with a biomarker that has shown clinical utility with manual pathology, such as approved complementary diagnostics. For example, using PD-L1 expression, which has demonstrated clinical utility across a range of tumor typesThe current advances in digital pathology offer practical advantages over manual pathology, including enhanced accuracy and precision, the ability for digital images to be uploaded and reviewed remotely by multiple pathologists, and the acquisition and processing of large and complex datasets. Within immuno-oncology, a deeper understanding of the complexity and underlying mechanisms of the TME can be achieved with the help of AI and machine learning, where datasets can be consistently analyzed and validated for application across many large cohorts, which may have implications for drug development and clinical trial design. AI and machine learning can then be utilized within the clinic to describe clinical and pathologic features across multiple patient samples. These advances will not only facilitate the entry of more precise I-O therapies, but also ultimately improve diagnostic, prognostic, and predictive clinical decision-making in cancer treatment.


Drosophila melanogaster, have provided precious insights into the requirement of Hox at multiple stages of the myogenic process. Here, we provide a comprehensive overview of Hox protein function in Drosophila and vertebrate muscle development, with a focus on the molecular mechanisms underlying target gene regulation in this process. Emphasizing a tight ectoderm/mesoderm cross talk for proper locomotion, we discuss shared principles between CNS and muscle lineage specification and the emerging role of Hox in neuromuscular circuit establishment.Hox genes encode evolutionary conserved transcription factors that specify the anterior–posterior axis in all bilaterians. Being well known for their role in patterning ectoderm-derivatives, such as CNS and spinal cord, Hox protein function is also crucial in mesodermal patterning. While well described in the case of the vertebrate skeleton, much less is known about Hox functions in the development of different muscle types. In contrast to vertebrates however, studies in the fruit fly,  Hox genes are highly conserved homeodomain transcription factors that play a fundamental role in bilaterian animal body patterning  with each cluster harboring 1–13 Hox genes. Genomic clustering of Hox genes in such a way is essential for their correct spatio-temporal expression, which is controlled by important regulatory elements located within and around these clusters. Fourth, they all bind to a very similar set of “AT”-rich DNA-binding sites, achieving functional specificity by cooperating with transcriptional cofactors, the best characterized being PBC proteins (Extradenticle/Exd in Drosophila) and MEIS proteins (Homothorax/Hth in Drosophila), also encoding for homeodomain transcription factors. However, a large number of Hox-PBC/Meis independent functions have been reported and reciprocally, PBC/Meis proteins can function without binding to Hox  composed of dorsal longitudinal muscles (DLM) and dorsal ventral muscles (DVM), two different strategies are employed: The DLMs that span the thorax antero-posteriorly develop by the fusion of myoblasts with three larval scaffolds that escape the histolysis process during first hours of pupal development; the DVMs, spanning the thorax dorso-ventrally, develop by de novo myoblast fusion without any scaffold, like in the case of the larval musculature  is expressed in the first thoracic segment, T1, Antennapedia (Antp) in T2 and T3, Ultrabithorax (Ubx) in abdominal segments A1–A7, abdominal-A (abd-A) in A4–A8 and Abdominal-B (AbdB) in A8–A9   . Importaelection . Whethercis-regulatory modules in a segment-specific manner  and collier  it was shown that the progenitor specification via Hox-mediated regulation of muscle iTFs is superimposed on the A-P and D-V positional information. Furthermore, the precise timing of Hox activity was traced to the progenitor stage, where first, Hox activity controls the number of progenitors that express col and nau in a segment-specific manner and then Hox proteins interplay with iTFs to allocate a correct number of nuclei assigned to the DA3 muscle   twi repression is mediated by direct Ubx binding to the twi promoter region, competing with binding of the transcription factors Tinman (Tin) and Muscle enhancer factor 2 (Mef2), providing another promoter-based molecular evidence about the Hox muscle patterning mechanism; (3) even though Ubx binding displaces Tin on the twi promoter, Tin must be bound to the promoter for Ubx to be recruited, once again demonstrating a tight link between Hox and tissue-specific transcription factors for the correct patterning establishment.It was recently shown that Ubx is involved in the muscle differentiation process by directly repressing the master mesodermal regulator st (twi) . Here, aCollectively, these studies provide precious insight into the way muscle progenitors are patterned. Hox proteins activate and collaborate with some of the muscle iTFs in a segment-specific fashion and by doing so they are responsible for the specification of different muscle types.Antp mutant embryos completely rescued the disorganized T2 muscle pattern provoked by Antp loss-of-function , also known as satellite cells, able to regenerate adult muscles upon injury . It is nnescence . It doeste cells . Differe muscles , but it collier (col) in both muscle and neural clusters to generate muscle/neuronal diversity  and somatic/skeletal muscles are at the basis of voluntary movements, crucial for locomotion, feeding, mating and interactions with the environment. For their establishment, MNs need to be correctly specified and differentiated and project their axons toward specific muscle groups. Neuromuscular circuitry defaults are associated with numerous neuromuscular diseases, thus the understanding of their proper development has a direct clinical significance (reviewed in reaper (rpr) gene enhancer, suggesting that their competitive binding could account for their opposed regulatory mode of motoneuron survival.In the CNS, Hox expression is shifted posteriorly compared to their mesodermal expression. Dfd is expressed in two subesophageal segments (S1 and S2), Scr is expressed in S3, Antp is expressed in the ventral cord in T1 to A9, Ubx from T3 to A9, Abd-A from A1 to A9 and Abd-B from A7 to A9. Pb is expressed in the thoracic leg motoneurons . Hox invDfd  and capricious (caps)  muscles. Ubx is expressed in both muscle cells and MNs and is required for the correct establishment of contacts between them . In the Dfd . Here, DDfd . This des (caps) .per se, but assure instead motoneuron survival and offer a unique code for their correct branching to distinct leg parts. Interestingly, this highlighted the importance of Antp protein levels, serving as a timing mechanism for correct proximal (early born) vs. distal (late-born) axon targeting . Concentrating on the LinB (Lin24) lineage of leg MNs, it was demonstrated that Pb, expressed in three of seven neurons composing this population, is required for their proper dendritic morphology and axonal patterning  and Glial cell line-derived neurotrophic factor receptor alpha (Gfrα). In this context, Hox proteins in collaboration with their cofactor Meis1 were shown to regulate the spatial pattern and expression levels of these genes in LMC neurons, required for proper MN differentiation and connectivity  in these neurons die of respiratory failure as a consequence of altered diaphragm innervation (Hox4 to Hox10) and their genetic manipulation leads to defaults in muscle-cerebellum connectivity  (Nkx2.5 in vertebrates). The posterior/abdominal part is constituted of six pairs of CMs, with two anterior ones expressing the orphan nuclear receptor Seven-up (svp) (ortholog of Nuclear Receptor Subfamily 2 members) and the four posterior CM pairs expressing tin (except A8 that contains only two tin-expressing CM pairs). Svp-expressing cardioblasts in the heart further differentiate into ostiae, inflow valves that allow for the hemolymph pumping, accounting for the partially open circulatory system in Drosophila  and Ubx (from A2 to A5 CM) and the heart expresses abd-A (from the fifth pair of A5 CM to the second pair in A8) and AbdB (in the two posterior CM pairs in A8) in both cardioblasts and at least some pericardial cells  signaling coming from the ectoderm. Interestingly, it was suggested that cardioblasts can respond to Hh signals only in Hox-expressing cells, explaining why the anterior aorta (a Hox-free region) does not express svp even though it receives Hh , a heart-specific marker, of unspecified function , involved in ionic homeostasis  expression but represses it in the pupa  was proposed as being directly or indirectly regulated by Ubx during adult VLM formation  could regulate org-1 also during this stage , specialized skeletal muscle connecting the cardiac tube at the level of Svp-expressing pericardial cells, with the lateral exoskeleton . AMs wererary AM . Furthererary AM . Like inerary AM . Howeverormation . Knowingis stage .Drosophila and vertebrate cardiac myogenesis even though at a first glance they seem very distinct. Both are developed from mesodermal precursors that converge toward the midline to give rise to a linear, contractile tube, that is further looped and developed into a multi-chambered organ in vertebrates (Drosophila tinman ortholog), GATA (Drosophila pannier ortholog) and T-box families.Many similarities can be found between tebrates . In birdHoxa1, Hoxa2, Hoxb1, and Hoxb2) are expressed in a subpopulation of Mesp1-expressing cardiovascular progenitors that seem to be the last to emerge from the primitive streak. Interestingly, progenitors that do not express Hox seem to be unipotent in contrast with Hox-expressing progenitors that are bipotent, contributing either to cardiomyocytes and smooth muscles or cardiomyocytes and endothelial cells  are also expressed in SHF progenitors and their expression patterns subdivide this cell population in distinct domains: Hoxa1 and Hoxb1 are expressed in AHF with different anterior limits and Hoxa3 is expressed in posterior SHF. While these progenitors contribute to both poles of the heart, Hoxb1-expressing progenitors are found only in the proximal OFT and atria and cells expressing Hoxa1 and Hoxa3 only in the distal OFT and some regions of the atria. SHF cells are thus pre-patterned before their addition to the developing heart  defects. In this case, Hoxb1 mutation led on the one hand to the upregulation of fgf8 levels and abnormal SHF proliferation and on the other hand the upregulation of the SHF differentiation markers, α-actinin and MF20 and thus premature SHF differentiation. Furthermore, in compound Hoxa1, Hoxb1 mutant mice, the OFT and ventral septum deficits were exacerbated, suggesting a genetic interaction between them  and B (Nppb) expression  that e streak . Mice mutterning . Additioeen them . Interesrmations . Extendipression .Hoxa3-mutant mice display defects in carotid arteries as well as defaults in size and shape of heart compartments  are expressed in the midgut visceral mesoderm and all except Scr and AbdB pattern the unsegmented gut together with ectodermal cues. Their expression in this tissue is parasegmental (Scr and AbdB excepted), and non-overlapping (a parasegment is a metameric unit composed of a posterior part of one segment and an anterior part of the adjacent segment). Scr mostly overlaps with PS4, Antp is expressed in PS5 and 6, Ubx in PS7, abd-A in PS8-12 and AbdB in the end of the midgut  expression can be detected , a function presumed to be independent of Hox patterning function  expression in the visceral mesoderm and together with AbdA controls wingless (wg) expression , driving its expression was also identified  expression and Ubx, AbdA, Wg, and Dpp regulate its expression in the central part (pointed (pnt) and odd-paired (opa) in the specific posterior midgut mesodermal regions  the ileocecal sphincter  doesn’t form and the ileocecal smooth-musculature is disorganized  provokes agenesis of the caecum (at the junction of the ileum and large intestine). Interestingly, it has been demonstrated that it is not the combined deletion of these genes but instead the resulting strong ectopic Hoxd12 expression that accounts for this phenotype (HoxD genes, it has been recently reported that Hoxd3 deletion alone results in gut growth deficit, giving an essential role in the gut development to this Hox gene (Few Hox functions have been identified in the mouse gut, notably because of the co-expression in the same gut regions of two or more Hox from different paralogous groups, leading to high functional redundancy. Despite this, it has been shown that phincter . When alrganized . Hox genhenotype . Trying Hox gene .Hoxc4, whose deletion results in esophageal musculature disorganization and obstruction of the organ (Hoxa4 transgene, resulting in its strong overexpression in the gut, develop a congenital megacolon phenotype characterized by a largely distended colon (Hoxa5 leads to stomach morphogenesis defaults, presumably by controlling the epithelio-mesenchymal signaling. Indeed, in the Hoxa5-deficient mice stomach epithelium, the expression pattern of Ihh, Shh, and Fgf10 changed and the expression levels of Ptc and Gli increased (Hoxc8 under the control of Hoxa4 regulatory elements, resulting in a shift in the anterior boundary of its expression, results in several hamartomatous lesions, where gastric epithelium was found embedded within the stomach musculature (A role has also been attributed to Hox genes from other paralogous groups, such as he organ . Mice caed colon . Deletioncreased . Misexprculature .Drosophila and vertebrates. While how they achieve this is not completely resolved, plethora studies in different model organisms have gone a long way to determine their role in the numerous muscle types. One emerging concept would appear that Hox act at numerous stages of muscle development, where at early stages, Hox appear to play a specifying role by providing spatial cues along the anterior–posterior axis, and at later stages controlling basic cellular functions such as proliferation, cell survival, death etc. This follows the role Hox play in patterning of the ectoderm and so it is perhaps without surprise that Hox contribute similarly in muscle development through mesoderm patterning.Hox genes are involved in the patterning of all muscle types in As in patterning of the ectoderm, in muscle development there is often requirement for the cooperation between Hox and other tissue-specific transcription factors, which are themselves Hox target genes together with signaling molecules. Examples about Hox functional conservation can be found mainly in vertebrate cardiac muscle, but conservation also exists in skeletal and gut muscles, further suggesting a universal role for Hox in mesodermal patterning. While how this achieved is not fully understood, knowledge gained in how Hox generate diversity in the CNS should help understand their role in generating different muscle types, which together allow for the development of more complex organisms.cis-regulatory regions of Hox target genes was historically a difficult and laborious process, but now with the vast advances in genome-wide approaches, both spatially and temporally, at the level of whole tissue or single cell, it is soon possible to better define Hox regulatory regions and target genes. Such a genome-wide spatio-temporal approach will thus allow us to fully grasp the complex and intricate networks defining how Hox proteins regulate muscle development.Even though a large number of Hox-dependent functions across different mesodermal derivatives are now known, there is however only sparse evidence about the underlying molecular mechanisms. To fully understand how Hox orchestrate muscle development, it is essential to define the network of genes they regulate, in addition to the tissue-specific transcription factors such as the identity TFs in muscle and temporal TFs in the CNS. The discovery of novel GP designed and drew illustrations, and wrote the manuscript with inputs from CM-Z, YG, and AS. All authors contributed to the article and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


TRPC2) and same-sex sexual behavior (SSSB) in primates. This novel theory is an attractive prospect for researchers investigating sexuality in the natural world. The proposal relies on evidence from proximate mechanism studies of TRPC2 knockout (KO) experiments in mice, in which non-functional TPRC2 alters the development of an olfactory sensory structure called the vomeronasal organ (VNO), resulting in an increase in SSSB in both males and females  and SSSB. Firstly, there is already a growing body of evidence for an epigenetic and polygenic underpinning of homosexuality and SSSB  cannot completely explain the expression of the behavioral phenotype for SSSB.Before discussing the evidence for a potential link between the TRPC2 with another gene, tryptophan hydroxylase 2 (TPH2). Initial work suggested that TPH2, which facilitates 5-HT neurotransmitter synthesis and is critical for serotonergic neuron function, had a strong role in the modulation of SSSB , although SSSB nevertheless increased in TPH2 KO males  as argued by Pfau et al. However, the evidence for such a connection is at present limited primarily by taxon sampling. Previous reconstructions establishing the ancestral pseudogenization of TRPC2 only assessed up to 15 species as representatives of the respective 77 extant primate genera  versus Old World monkeys and apes , brown lemurs (Eulemur), and sifaka (Propithecus)  appear to possess functioning TRPC2 and yet also exhibit SSSB  and Atlantic forest marmosets  also perform SSSB, but they have an incomplete VNO with a reduced vomeronasal epithelium (VNE), through which TRPC2 might not be able to express the phenotype for increased same-sex aversion , and interrupted or interspersed VNE in tamarins (Saguinus) and lion tamarins (Leontopithecus)  are reported to have a complex VNO  is comprised of opportunistic anecdotes involving mere two copulatory mounts  of one gene with premature stop codons are frequently compensated for by the upregulation of orthologues from the same gene family  and/or pleiotropy with reduced aggression is unappealing, but we also note that a preliminary question, for example, would be to ask why TRPC2 loss has not then been documented in the multi-male multi-female group-living diurnal lemurs, squirrel monkeys, and capuchins , it is important to distinguish between proximate hypotheses (“how it works”) from ultimate hypotheses (the “why” question). We note that Pfau et al. refer to the link between non-functional Sussman, .TRPC2 KO in New World monkeys or lemurs, although by deleting the gene entirely and not by introducing premature stop codons. If Pfau et al.’s theory is true, then those KO mutants should exhibit increased SSSB. One might even attempt to rescue the function of TRPC2 in an Old World monkey or ape and thereby expect suppression of SSSB, given that the true redundancy of their VNO has been contended Below is the link to the electronic supplementary material.


Migrants globally, including refugees and asylum seekers, have experienced adverse clinical and socioeconomic impacts of the COVID-19 pandemic. For approximately 56,000 refugees and asylum seekers in Reception and Identification Centers (RICs) and Reception Sites (RS) in Greece, living in severely substandard living conditions, prevention measures have been impossible with limited provision in terms of routine testing, surveillance, and access to healthcare. These migrant populations have experienced prolonged lockdowns and restricted movement since the pandemic began. We aimed to assess the impact of COVID-19 on refugees and asylum seekers in reception facilities in Greece and explore implications for policy and practice.A retrospective analysis of policy documents and national surveillance data was conducted to identify COVID-19 outbreaks and estimate incidence among asylum seekers and refugees residing in these camps during the first 9 months of the epidemic in Greece (26th February – 15th November 2020). Incidence proportion (IP) of COVID-19 confirmed cases was calculated for three population groups  during three time periods .p-value<0.001). The risk of acquiring COVID-19 infection was higher among refugee and asylum seeker populations in RSs on the Greek mainland  but higher still among refugee and asylum seeker populations in RICs in the Greek islands and the land border with Turkey , where living conditions are particularly poor.Twenty-five COVID-19 outbreaks were identified in refugee and asylum seeker reception facilities, with 6 (85.7%) of 7 RICs and 18 (56.3%) of 32 RSs reporting at least one outbreak during the study period. The overall 9-month COVID-19 IP among refugee and asylum seeker populations residing in RSs on the Greek mainland was 1758 cases per 100,000 population; in RICs the incidence was 2052 cases per 100,000 population. Compared to the general population the risk of COVID-19 infection among refugees and asylum seekers in reception facilities was 2.5 to 3 times higher (We identified high levels of COVID-19 transmission among refugees and asylum seekers in reception facilities in Greece. The risk of COVID-19 infection among these enclosed population groups has been significantly higher than the general population of Greece, and risk increases as living conditions deteriorate. These data have immediate implications for policy and practice. Strategies are now needed to ensure refugee and asylum seeker populations are included in national response plans to reduce transmission in at-risk groups for COVID-19, alongside inclusion in plans for COVID-19 vaccine roll out.None. There is very limited peer-reviewed and grey literature available on the impact of the COVID-19 pandemic refugee and asylum seekers in Greece, particularly those living in closed settings such as reception centres. Peer reviewed literature available at global level on the impact of the COVID-19 on migrants is also limited, with only one systematic review by Hayward et al. reporting on clinical outcomes and risk factors for COVID-19 among migrant populations in high-income countries. This systematic review documents potentially higher levels of transmission in closed settings, including migrant reception and detention centres, and suggests migrants in general may be over-represented in cases and deaths.Our study explores the impact of COVID-19 on refugees and asylum seekers residing in reception facilities in Greece, and its implications for policy and practice. Our data suggest that these overcrowded camps may have resulted in increased infection risk for COVID-19. For the first 9-month epidemic period in Greece (first and second wave) our findings show that refugees and asylum seekers in these reception facilities have almost a 2.5 to 3 times higher COVID-19 incidence compared to the general population , with highest risk in migrant populations in reception facilities on the Greek islands and at the Greek-Turkish border, where living conditions are considerably worse.Our data highlights that refugee and asylum seeker populations residing in reception and identification centres and reception sites in Greece are at higher risk of acquiring SARS-CoV-2 infection. Going forward, an urgent focus must be placed on improving living conditions in these facilities, decongesting those that operate beyond their capacity, reinforcing epidemiological surveillance, testing strategies and data transparency, and ensuring free, comprehensive and universal access to care. Refugees and asylum seekers in closed settings need to be urgently prioritised in the COVID-19 vaccine roll-out.Alt-text: Unlabelled box1Migrants – including marginalized groups such as asylum seekers, refugees, low-wage labor migrants, and undocumented migrants – globally have experienced adverse clinical and socioeconomic impacts of the COVID-19 pandemic Greece is a major entry point for people seeking safety in Europe, and since the early 2000s hundreds of thousands of asylum seekers have crossed into Greece by land and sea. In 2015, over 800,000 people arrived in Greece, the majority fleeing the war in Syria, as well as conflicts and violence in Afghanistan, Iraq and some sub-Saharan African countries Both the RICs and mainland RSs are characterized by severely substandard living conditions, poor sanitation and a lack of access to adequate – or sometimes any – healthcare, creating significant risk for their inhabitants in the event of an outbreak of infectious disease The COVID-19 epidemic in Greece started on 26th February 2020, and during the first epidemic wave Greece managed to keep COVID-19 infections, Intensive Care Units hospitalized cases and deaths at relatively low levels compared to other European countries 2We conducted a retrospective observational study analyzing secondary surveillance data and policy documents from multiple sources in order to: (a) identify the number of COVID-19 outbreaks and cases in refugee and asylum seeker reception facilities in Greece and (b) compare the incidence of COVID-19 among refugee and asylum seeker populations residing in Greek RICs and RSs to the incidence among the Greek host population. We analyzed data from the first nine months of the epidemic in Greece (26th February until 15th November 2020), which was divided into two distinct time periods: the first epidemic wave of COVID-19 in the country (26th February until 30th June 2020) and part of the second wave period (1st July to 15th November 2020). Our study period starts from 26th February 2020 when the first COVID-19 case was confirmed in Greece and ends on 15th November 2020 which, at the time of embarking on this study, was the most up to date data available regarding COVID-19 cases among refugee and asylum seekers in Greece.We focused on two particular migrant groups: ‘asylum seekers’ who are individuals with an active asylum claim pending, and ‘refugees’ who have been granted international protection and are recognized refugees in Greece with their claims processed, residing in two types of accommodation in Greece: (i) seven RICs – 6 RICs on 5 specific Greek islands (including Moria and Kara Tepe facilities on Lesvos) and one RIC on the Evros border; and (ii) 32 IOM-run RSs on the Greek mainland.2.1We identified a range of data sources for data pertaining to cases in both the general population, RICs, and RSs. The total number of COVID-19 laboratory confirmed cases in the overall population in Greece were derived from the COVID-19 epidemiological surveillance report published daily by the Hellenic Public Health organization (NPHO), which is the national center for disease control and prevention Official information about the number and specific location of COVID-19 outbreaks in RICs and RSs in Greece is not publicized by Greek authorities 2.2No ethics approval was required; all the data sources are anonymous and freely available online.2.3Incidence proportion (IP) of COVID-19 confirmed cases was calculated for our three study population groups  during three time periods . IP reflects the new COVID-19 cases during a specific time period divided by the population at risk at the beginning of the relevant period. Because we anticipated that the size of refugee and asylum seeker populations residing in RICs and other RSs would change over time, we calculated the mean population at risk based on reports on the number of refugees and asylum seekers in RICs provided on a weekly basis by the Hellenic Ministry of National Defense P-value less than 0.05 was considered statistically significant. Data were extracted by EK and duplicate data extraction was done by DP. The analysis was carried out by DP and EK using STATA (version 11) and OpenEpi Total number of COVID-19 cases in the general population was calculated by combining the two publicly available data sets on COVID-19 confirmed cases ,32, and 2.4None. EK accessed the database and EK, MO, SH, and AV took the decision to submit the paper for publication.3During our study period (26th February – 15th November 2020) 77,527 COVID-19 confirmed cases were reported among the general population in Greece (approximately 10.8 million people), most of which  were observed during the second epidemic wave, and 3212 (4.1%) during the first.3.1During the first epidemic wave (February to June 2020) three outbreaks were reported in refugee and asylum seeker RSs on the Greek mainland . All these outbreaks took place between late March and late April 2020, when the first epidemic wave was partly under control and overall cases in the country were rapidly declining . ImportaThroughout the second epidemic wave, 22 COVID-19 outbreaks were detected in both RICs and RSs. Among these 22 outbreaks, five (22.7%) took place in the RICs on the Greek islands, one (4.5%) in the RIC on the Greek-Turkish border, and 16 (72.7%) outbreaks occurred in other RSs on the Greek mainland . All the3.2Throughout both waves of the epidemic in Greece, 1106 COVID-19 confirmed cases have been reported amongst the refugee and asylum seeker population residing in reception facilities; 228 cases (20.6%) were reported during the first epidemic wave and 878 (79.4%) during the second . This paThe overall 9-month COVID-19 incidence proportion among refugee and asylum seeker populations residing in RSs on the Greek mainland was 1758 cases per 100,000 population, while for people residing in RICs the incidence increases to 2052 cases per 100,000 population .Table 3CDuring the first epidemic wave the risk of infection was almost 28 times higher in refugees and asylum seekers residing in the 32 IOM-run RSs on the Greek mainland compared to the general population , a finding likely related to the three outbreaks that were recorded in mainland RSs during the first wave. Throughout the second epidemic wave the risk of infection was almost 4 times higher in refugees and asylum seekers residing in RICs on Greek islands and the Evros border compared to the general population . This is linked to the outbreaks that were recorded in almost all RICs (with the only exception being the RIC in Kos Island) during the second wave.p-value<0.001). The risk of infection  increases between the general population and refugee and asylum seeker populations in RSs on the Greek mainland , and is highest in migrant populations in RICs on the islands and Greek-Turkish border, where living conditions are considerably worse   .4We identified high levels of COVID-19 transmission among asylums seekers and refugees in RICs and RSs on the Greek mainland and surrounding islands. The risk of infection among these enclosed population groups has been significantly higher than the general population of Greece, and risk increases as living conditions deteriorate and overcrowding increases. These data have immediate implications for policy and practice.There were several identified study limitations. First, there is poor epidemiological surveillance and data collection in Greece and we were reliant on national statistics which were limited in scope and access. The likely under-reporting among refugees and asylum seekers not presenting to healthcare providers means we do not have a clear picture of the rates of COVID-19 amongst this population. In addition, the datasets we had access to provided no information on health outcomes in COVID-19 cases involving refugees and asylum seekers, including hospitalisations in COVID-19 clinics, hospitalisations in Intensive Care Units (ICUs), or deaths and additionally no information regarding age and sex distribution of COVID-19 cases among refugees and asylum seekers was publicly available.The refugee and asylum seeker population tends to be younger, with one NGO report stating that over 60% of refugees are under 30 years old Living conditions in the RICs and RSs make adherence to COVID-19 public health guidance and prevention very challenging, and most likely explain the higher transmission our data show in these settings. Physical distancing and hygiene measures (e.g. handwashing) were encouraged by authorities at the start of the pandemic, but due to the severe overcrowding of the RICs Asylum seekers and refugees in Greece face multiple administrative and legal barriers to accessing healthcare, further disadvantaging them in the context of COVID-19. Healthcare provision for those living in RICs and RSs is inadequate, with long wait time to see a doctor if there is one present at all, and often with limited options for swiftly accessing secondary care ,41. FurtDespite the ECDC's recommendation to consider ‘measures to decongest and evacuate residents’ in these enclosed settings if distancing and other risk mitigation measures cannot be implemented Although Greece was swift to impose early nationwide public health restrictions, the lockdown measures and mass quarantining were applied more stringently and for longer periods of time to refugees and asylum seekers in RICs and RSs. The government declared these targeted measures to be in the public interest to ‘“limit the spread of COVID-19 in areas of overcrowding…” Despite calls for inclusion of refugees and asylum seekers in the COVID-19 response from multi-laterals such as WHO, UNHCR, ECDC and IOM , and acaIn order to ensure inclusion of refugees and asylum seekers in the COVID-19 response, a greater emphasis must also be placed on developing culturally and linguistically adapted information about COVID-19 prevention measures and vaccination. In a rapid review public health communication targeting migrant populations during the COVID-19 pandemic conducted in June 2020, for example, only half of the European member states had translated their COVID-19 risk communications into one migrant language; and no government produced risk communications on COVID-19 prevention targeting people in refugee camps or informal settlements, leaving migrants excluded from national health system responses (1)In order to reduce the risk of transmission of COVID-19 and to protect the right to health for all, the Greek government should urgently improve living conditions in all reception facilities including the provision of adequate sanitation and hygiene services, dignified and safe accommodation where COVID-19 prevention measures can be followed.(2)Interagency guidance on protection and response to COVID-19 issued by multiple agencies in the first half of 2020 should be adhered to a by all governments (3)Restrictive public health measures, such as lockdowns and mass quarantining, should only be applied where proportionate to public health need, not as a blanket measure for certain population groups such as migrants. Quarantine and lockdown measures should be applied equally to all without discrimination; healthcare, social and psychosocial support, and basic needs as food, water and other essentials should be provided to those in quarantine; and mass quarantine avoided where possible, in line with ECDC and WHO guidelines.(4)Epidemiological surveillance systems must be strengthened to better record COVID-19 cases, hospitaliations, and deaths involving refugees, asylum seekers, and other migrants, in Greece and Europe-wide to inform the public health response going forward and implications for COVID-19 vaccine roll out. A rigorous system for public data sharing should be established, with an emphasis on transparency and timeliness.Our data quantitatively shows the greater risk of COVID-19 infection among refugee and asylum seeker populations residing in substandard living conditions. Acknowledging this higher risk is of crucial policy importance. Going forward, all enclosed population groups need to be urgently included and prioritized in the national vaccination program, without any exception. Greece should follow the example of the Republic of Serbia which, in late March became one of the first European countries to begin vaccinating its refugee, asylum seeker and other migrant populations living in reception centres and has included these populations in their national vaccination plan NoneNo individual participant data was used. All data sources for data pertaining to cases in both the general population, RICs, and RSs, are freely available online. Any query on the manuscript can be submitted to the corresponding authors.MO is Executive Director of Lancet Migration: global collaboration to advance migration health. All other authors declare no conflicts of interest.