Maternal genetic effects can be defined as the effect of a mother’s genotype on the phenotype of her offspring, independent of the offspring’s genotype. Maternal genetic effects can act via the intrauterine environment during pregnancy and/or via the postnatal environment. In this manuscript, we present a simple extension to the basic adoption design that uses structural equation modelling (SEM) to partition maternal genetic effects into prenatal and postnatal effects. We examine the power, utility and type I error rate of our model using simulations and asymptotic power calculations. We apply our model to polygenic scores of educational attainment and birth weight associated variants, in up to 5,178 adopted singletons, 943 trios, 2687 mother-offspring pairs, 712 father-offspring pairs and 347,980 singletons from the UK Biobank. Our results show the expected pattern of maternal genetic effects on offspring birth weight, but unexpectedly large prenatal maternal genetic effects on offspring educational attainment. Sensitivity and simulation analyses suggest this result may be at least partially due to adopted individuals in the UK Biobank being raised by their biological relatives. We show that accurate modelling of these sorts of cryptic relationships is sufficient to bring type I error rate under control and produce asymptotically unbiased estimates of prenatal and postnatal maternal genetic effects. We conclude that there would be considerable value in following up adopted individuals in the UK Biobank to determine whether they were raised by their biological relatives, and if so, to precisely ascertain the nature of these relationships. These adopted individuals could then be incorporated into informative statistical genetics models like the one described in our manuscript to further elucidate the genetic architecture of complex traits and diseases. Maternal genetic effects can be defined as the causal influence of maternal genotypes on offspring phenotypes over and above that which results from the transmission of genes from mothers to their offspring . Over thIn this manuscript, we develop a simple method for partitioning maternal genetic effects into prenatal and postnatal components that leverages information provided by adopted individuals. We assume that an adopted individual’s phenotype is influenced by prenatal intrauterine factors as proxied by their biological mother’s genome, and postnatal influences as proxied by their adoptive mother’s and father’s genomes. In contrast, we assume that the phenotype of individuals who have not been adopted are influenced by prenatal intrauterine factors resulting from their biological mother’s genome, and postnatal factors as proxied by their biological mother’s and father’s genomes. This model leads to different expectations for the covariance between an individual’s phenotype and their own and their relatives’ genotypes depending on whether they have been adopted or not.One of the challenges in applying this kind of framework to real life situations is the paucity of cohorts containing large numbers of adoptive families . RestricIn this manuscript, we introduce a new structural equation model (SEM) to estimate prenatal and postnatal maternal genetic effects on offspring phenotypes. The SEM framework allows us to model the contributions from missing individuals as latent variables, facilitating the inclusion of information from adopted singleton individuals whose adoptive and/or biological parents are not available for analysis. We estimate the asymptotic power of our model to partition maternal genetic effects into prenatal and postnatal components and confirm these results by simulation. We code our routines into a freely available R shiny app web utility that researchers can use to perform their own power calculations. Finally, we apply our methods to birth weight and educational attainment data in the UK Biobank- two phenotypes known to be affected by maternal genetic effects. We investigate the effect of inadvertently including adopted singleton individuals whose adoptive parents are genetically related to them in our model, adjust our model to correct for this misspecification and quantify the effect of this adjustment on type 1 error rates and power. We discuss the implications of model misspecification for using the UK Biobank resource in this context in future studies more broadly.We consider seven different family structures  which wem, Zp and Zo represent maternal, paternal and offspring genotypes. Likewise, let Zm_b and Zp_b denote the genotypes of the biological mother and father of an adopted individual respectively, and let Zm_a and Zp_a represent the genotypes of the adoptive mother and father of the adopted individual. These variables may be observed , paternal genetic effects (pϐ) and offspring genetic effects (oϐ) on the offspring phenotype (Y). We are specifically interested in partitioning maternal genetic effects into prenatal (γm) and postnatal genetic effects (mϐ) and we assume that the effect of the prenatal maternal genetic effect and postnatal maternal genetic effect on the offspring phenotype is additive (i.e. m + ϐmγ). We assume in the case of biological families (i.e. G1 to G4 in Let the variables ZΦ) of each individual and assume that it is the same in mothers, fathers, their children, and across both biological and adopted individuals under random mating . We account for possible covariance between maternal and paternal genotypes (e.g. through assortative mating) by estimating the covariance path ρ. This is equivalent to modelling one round of assortative mating in the parental generation and has the effect of inflating the total genotypic variance in the offspring generation from Φ under random mating to Φ + ½ρ. Finally, to accommodate the possibility that adopted individuals may have different phenotypic variances to individuals raised by their biological relatives, we permit the variance of residual sources of variation to differ across adopted and biological family structures , contribute important information for the partitioning of maternal genetic effects, since the covariance between their own genetic score and phenotype is a function of offspring genetic effects and prenatal maternal effects, but not postnatal maternal effects . This coWe used OpenMx  to calcu2χ distribution with where arameter .α=0.05) using sample sizes roughly similar to the number of European individuals in the resource who reported their educational attainment . We investigated the effect of different combinations of prenatal maternal genetic effects, postnatal maternal genetic effects, postnatal paternal genetic effects and offspring genetic effects . We also examined the effect of modifying the covariance between parental genotypes . Finally, we were curious as to the effect of increasing the number of the different family structures on statistical power, and in particular, on whether including increasing numbers of more attainable/accessible biological relatives  could increase power to partition maternal genetic effects for a fixed number of adopted individuals. We also investigated the effect of varying the sample size of adopted individuals (G5), adoptive mother-adopted offspring duos (G6), and biological mother-adopted offspring duos (G7) on power. We used our calculator to determine 80% power (α=0.05).As we were interested in whether there might be enough adopted individuals in the UK Biobank for the informative partitioning of maternal genetic effects into prenatal and postnatal sources of variation, we first calculated power  divided by 1000.https://evansgroup.di.uq.edu.au/ADOPTED). Our power calculator is built using the R shiny app (https://shiny.rstudio.com/) running the R studio software , followed by trios (G1), mother-offspring duos (G2), and then father-offspring duos (G3).We applied our model to self-reported birth weight and educational attainment data from the UK Biobank resource. The UK Biobank study was approved by the UK National Health Service National Research Ethics Service. Written consent was obtained from both the participants and their parents (for subjects younger than 18 years old). This study was approved by the Human Research Ethics Committee at the University of Queensland . We only included individuals of European ancestry, determined by: (i) projecting their genetic principal components onto the 1,000 Genome sample and then K-means clustering or (ii) those who self-reported ethnic background of either ‘British’, ‘Irish’, ‘Irish’, ‘White’, or ‘Any other white background’. Family relationships were determined by the pairwise kinship estimated across the whole UK Biobank sample using the software KING . Adoptio–8 from a recent GWAS of birth weight , Higher National Diploma (HNC), or equivalent = 19 years; other professional qualification = 15 years; A level/AS level or equivalent = 13 years; O level/General Certificate of Secondary Education (GCSE) or equivalent = 10 years; Certificate of Secondary Education (CSE) or equivalent = 10 years; none of the above = 7 years of education. We regressed years of education on year of birth, sex and the first five genetic principal components and used the residuals in the analysis. The final dataset included 943 biological parent-offspring trios, 2687 biological mother-offspring pairs, 712 biological father-offspring pairs, 347,980 singletons from biological families, 5178 adopted singletons and 39 biological mother-adopted offspring pairs. We calculated weighted PRS  and unweighted PRS  using 1267 SNPs from the current largest GWAS of educational attainment . We were concerned about the possibility that some individuals in UK Biobank might have been adopted by their biological relatives in which case our model in We were concerned about the effect that misclassifying adopted “singleton” individuals as unrelated to their adoptive parents  might have on type I error rates and estimates of prenatal and postnatal maternal genetic effects generated by our model. We therefore simulated data under four scenarios: (i) adopted singletons where 0%, 20%, 40%, 60%, or 80% had adoptive mothers who were siblings or cousins of their biological mothers, (ii) adopted singletons where 0%, 20%, 40%, 60%, or 80% had adoptive mother who were siblings or cousins of biological fathers, (iii) adopted singletons where 0%, 20%, 40%, 60%, or 80% had adoptive fathers who were siblings or cousins of biological fathers, and (vi) adopted singletons where 0%, 20%, 40%, 60%, or 80% had adoptive fathers who were siblings or cousins of biological mothers. We assumed that sample sizes and the number of individuals in the other groups were the same as in our asymptotic power calculations.r) between the genotypes of adoptive and biological parents. The covariance between the genotypes of adoptive and biological parents was fixed to r=0.5 х Φ when the adoptive parent was a sibling of the biological parent and r=0.125 х Φ when they were cousins.We further investigated type 1 error rates and estimates of prenatal and postnatal maternal genetic effects after correctly modelling the relationship between biological and adoptive parents. We created four additional family structures  to detect prenatal maternal and postnatal maternal genetic effects that account for 1.5% and 0.9% of the variance in the offspring phenotype, respectively . The power to detect prenatal and postnatal maternal genetic effects also increased slightly with increasing covariance between maternal and paternal genotypes , whereas biological mother-adopted child pairs provide a direct estimate of the prenatal maternal genetic effect (m). Likewise, adopted singleton individuals do not provide direct estimates of either prenatal or postnatal maternal genetic effects, and therefore contribute proportionally less in terms of statistical power than adoptive mother-adopted child or biological mother-adopted child pairs.The power to detect prenatal and postnatal maternal genetic effects increased with the number of adopted singleton individuals, and more dramatically when the number of adoptive mother-adopted offspring pairs and/or biological mother-adopted offspring pairs increased . Power tInterestingly, the power to detect prenatal and postnatal maternal genetic effects also increased with the number of biological families in the analysis (G1 to G4) ; howeverPRS for birth weight exhibited significant evidence of prenatal maternal genetic effects, offspring genetic effects (in the opposite direction), and no significant evidence for postnatal maternal or paternal genetic effects . This paIn the case of offspring educational attainment, our analyses showed evidence for a prenatal maternal genetic effect, a paternal genetic effect, and an offspring genetic effect . The absWe therefore performed sensitivity analyses where we excluded adopted individuals who reported breastfeeding information . The results of these analyses showed that estimates of the prenatal maternal genetic effect reduced in size and became non-significant. Whilst the postnatal maternal effect remained non-significant, the direction of effect changed from negative to positive. Sensitivity analyses using PRS constructed using the 72 genome-wide significant SNPs from the Our simulations showed that the presence of adoptive parents who were genetically related to their adopted offspring could in some cases increase the type 1 error rate to detect prenatal maternal genetic effects and bias estimates of prenatal and postnatal maternal genetic effects when these relationships were not accurately modelled in the SEM . In gener approaches zero .In the case of adoptive mothers being genetically related to biological parents , the prers G8 in , and adors G8 in - implyinLikewise, our simulations showed that the presence of paternal genetic effects . This makes sense since offspring and paternal genetic effects are estimated directly from the covariance between observed genotypes and phenotypes , whereas the estimation of prenatal and postnatal maternal genetic effects have to be inferred indirectly using latent (not directly observed) genotypes and the difference in the covariance between offspring genotype and phenotype in biological and adoptive families.When we correctly modelled the relationship between biological and adoptive parents’ genotypes, there was no inflation in type 1 error rates and effect estimates were unbiased . Howeverr). The consequence is that the reduced model doesn’t fit the data as well and increased power to detect association. Similar thought experiments can be used to provide intuition for the other results in In contrast, power to detect prenatal and postnatal maternal genetic effects increased when adopted singletons whose adoptive fathers were related to their biological parents were included (and modelled correctly) in the analysis. In order to understand this result intuitively, it is useful to consider the case where the offspring trait is affected by concordant prenatal and postnatal maternal genetic effects  effects. Whilst our method does not provide explicit mechanistic insight into the reason for the existence of maternal genetic effects , follow up studies could help elucidate these mechanisms by for example examining the association between maternal PRS and different prenatal and postnatal maternal phenotypes, and performing mediation and Mendelian randomization analyses .We are not the first to note that information from adopted individuals could be used to provide valuable information on the effect of the prenatal environment on offspring traits . We are We are also not the first to have used adopted individuals in the UK Biobank to estimate the contribution of indirect genetic effects to phenotypic variability.  contrastIn a previous study of cognitive and non-cognitive effects on educational attainment , used thOur power calculations show that in principle the number of adopted individuals in the UK Biobank is unlikely to be sufficient to resolve effects at individual genetic loci but may be large enough to detect evidence for prenatal/postnatal maternal genetic effects in the case of PRS. It is worth noting that, whilst primarily a function of the number of adoptive families in the analysis, the power to partition maternal genetic effects was also influenced by the number of biological families analysed. Intuitively, this is because the other family structures contribute to more precise estimation of for example paternal genetic effects, which in turn provides information on the total size of the maternal genetic component. This observation is important, because the number of adopted individuals in an analysis is likely to be severely constrained, whereas power can sometimes be improved  by increasing the sample size of other relative types that may be easier to ascertain. Regardless, the most efficient way to increase the power to detect prenatal and/or postnatal maternal genetic effects is to genotype adoptive mother-adopted child pairs and/or biological mother-adopted child pairs.Our empirical analyses in the UK Biobank showed the expected pattern of maternal genetic effects on offspring birth weight  but not on offspring educational attainment. Parental educational attainment is known to causally affect offspring educational attainment . Whilst Our model relies on several strong assumptions, some of which are shown explicitly in Whilst we expect some of these assumptions to hold , we expect that others will show varying degrees of violation with differing consequences for the parameter estimates obtained under the misspecified model. For example, it is well appreciated that adopted individuals and adoptive families may differ in several respects to the general population. For example, some studies have reported that adoptive families are better educated and have higher socio-economic status than the population average . We arguWe argue that of greater consequence for the validity of our model is that any genetic relationship between adoptive and biological parents is accurately modelled and included in the SEM. Through simulation, we have shown that the consequences of model misspecification depend upon which biological and adoptive parents are related, the nature of this relationship, and the proportion of adopted individuals in the sample who have had their relationship misspecified. Our simulations also showed that correctly modelling this relationship returns asymptotically unbiased effect estimates and correct type I error rates. Clearly, knowing these cryptic relationships in the UK Biobank would allow us to properly model them and better estimate prenatal and postnatal maternal genetic effects using this resource. We emphasize that accurately modelling these relationships does not require that actual genotypes for adoptive and/or biological parents be obtained  as our SEM allows us to model these relationships in terms of latent variables. Indeed, as large-scale resources like the UK Biobank become more common, we expect that the number of adopted individuals who have GWAS will also increase, and consequently models like the one espoused in this manuscript will become increasingly useful. High quality phenotypic information on these adopted individuals and their adoptive parents including whether they share any biological relationship will be critical to making the most of these resources.r=0.285; birth weight: r=0.076) and PRS  in complete parent-offspring trios in the UK Biobank suggest that assortative mating is likely present for educational attainment, and potentially to a lesser degree for (phenotypes correlated with) birth weight. Recent work by We have also not modelled the complex effects of assortative mating in our SEM other than including covariance terms between maternal and paternal genotypes and assuming equal genetic variances in parents and their offspring under random mating . Positive assortment induces a number of complications when attempting to decompose the offspring phenotypic variance into its constituent sources of variation, including increasing homozygosity and the genetic variance at loci for the trait undergoing assortment relative to that expected under Hardy-Weinberg equilibrium, and inducing correlations between assorting loci across the rest of the genome both within and between individuals in the same family. Spousal correlations for phenotypes  suggests that the effect of assortative mating on parameter estimates from our SEM is likely to be minor.Finally, it has not been lost on us, that our framework may provide a basis for correcting Mendelian randomization (MR) studies of intrauterine/early life environmental exposures for some of the contaminating effects of horizontal genetic pleiotropy . The preIn conclusion, we present a simple extension to the basic adoption design that includes measured genotypes and enables partitioning of maternal genetic effects into prenatal and postnatal sources of variation. We show in principle that adopted singleton individuals in the UK Biobank combined with biologically related parent-offspring trios and pairs is sufficient to partition maternal genetic effects into prenatal and postnatal sources of variation. However, power calculations suggest that such a partitioning would currently only be realistic for PRS explaining substantial proportions of the variance in offspring phenotype, and that much larger numbers of individuals would be required to achieve such a partitioning at individual loci. In addition, failure to correctly model cryptic relationships between adoptive and biological parents may produce biased estimates of maternal genetic effects and increased type I error rates. Accurate modelling of cryptic relationships is sufficient to bring type I error rate under control and produce unbiased effect estimates. We suggest that there is a possibility that many individuals in the UK Biobank who report being adopted could have been raised by their biological relatives. We conclude that there would be considerable value in following up adopted individuals in the UK Biobank to determine whether they were raised by biological relatives, and if so, to precisely ascertain the nature of the relationship. These adopted individuals could then be used in informative statistical genetics models like the one described in the present manuscript to further elucidate the genetic architecture of complex traits and diseases.R code for performing the analyses described in this manuscript is available in the Source Code File. The authors propose an innovative and sound method to leverage the adoption of a design for disentangling prenatal and postnatal genetic effects. This work will be of interest to scientists interested in the intergenerational transmission of phenotypes through genetic pathways. public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by George Perry as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Stephane Paquin (Reviewer #2).Thank you for submitting your article "Using adopted individuals to partition indirect maternal genetic effects into prenatal and postnatal effects on offspring phenotypes" for consideration by The Reviewing Editor has drafted this to help you prepare a revised submission.The reviewers of your paper were positive about your work, but also identified several limitations that are not properly accounted in the current version of the paper. Alongside the critiques the reviews are so constructive that reasonable paths forward on each of the points raised below – primarily with thorough textual changes only – should be readily apparent.Reviewer #1 (Recommendations for the authors):GWAS data used for polygenic scoresThe decision to include only genome-wide significant SNPs was not justified. Please could the authors fill the readers in on this? Especially for EA, a highly polygenic trait, why not include all SNPs? It is also not clear why they performed the sensitivity analysis with the less powered Okbay GWAS – perhaps this could be put in the Supplementary Information. Can the authors comment on how their results are affected by pre- and post-natal IGEs being captured by the original GWAS used to construct polygenic scores ?Positioning among prior workThe authors could spell out more for readers their answer to the 'big picture' question of why it is useful to distinguish pre- and post-natal maternal effects, instead of just saying it's a natural question (see Introduction). Also in the introduction, it does not seem accurate to say that partitioning variation could yield mechanistic insights. Although the current contribution is very valuable, don't we need different kinds of designs and measures to understand mechanisms? In this regard, the authors should discuss the merits of their study compared to that of Armstrong-Carter et al. That study does not offer a principled way to separate pre- and post-natal effects, but it does try to work out which kinds of maternal phenotypes are involved in so-called pre-natal genetic nurture effects captured by EA PGS.The authors should also engage more with other studies to clarify the novel aspects of their contribution. They describe in the Discussion the Domingue and Fletcher (2020) paper, but more relevant is Cheesman et al's., (2020) study design using the nonadopted-adopted polygenic score comparison in UKBB to estimate post-natal parental indirect genetic effects. Demange et al., (2021) estimated pre-natal parental indirect genetic effects by comparing estimates from adoption and sibling designs . Although this was for a non-cognitive skills polygenic score and not EA or BW, it is worth discussing their approach and comparing against their estimates. The Demange et al., approach means that estimates of pre-natal maternal genetic effects may also include assortative mating and population stratification. There is a general paragraph on AM in the Discussion, but how do the authors think that AM and popstrat will load in their model? Recent evidence from UKBB suggests that it is important to consider the role of population stratification particularly when studying EA .Misc.It is neat to have a negative control phenotype for post-natal genetic effects (birthweight). Can the authors try out a negative control for pre-natal genetic effects too? If the maternal pre-natal effect were significant for something paternal substance use, this would bolster the idea that relatedness among adoptees and their adoptive parents causes inflation of this estimate.The use of breastfeeding data to identify those who were not adopted by relatives is smart. It would be worth checking how selected this sample is for some key traits . Also, could more information be leveraged to increase accuracy? Many participants just don't know the answer to that question, could have remembered incorrectly, and wetnurses  also used to be common. Adoptions in later cohorts might have become more 'organized' (happening earlier in life and involving non-relatives). Perhaps it's worth cutting out some of the older adoptees? The authors could also try limiting the sample further to those who also report their birthweight.Was a relatedness cut-off used to ensure that each family was independent?The authors could cite Wang et al., (2021) when saying that it is unfeasible for paternal genetic effects to be so much bigger than maternal effects – this meta-analysis found equal effects of paternal and maternal EA PGS on offspring educational outcomes.ReferencesArmstrong-Carter E, Trejo S, Hill LJ, Crossley KL, Mason D, Domingue BW. The earliest origins of genetic nurture: The prenatal environment mediates the association between maternal genetics and child development. Psychological science. 2020 Jul;31(7):781-91.Cheesman R, Hunjan A, Coleman JR, Ahmadzadeh Y, Plomin R, McAdams TA, Eley TC, Breen G. Comparison of adopted and nonadopted individuals reveals gene-environment interplay for education in the UK Biobank. Psychological science. 2020 May;31(5):582-91.Demange PA, Hottenga JJ, Abdellaoui A, Eilertsen EM, Malanchini M, Domingue BW, de Zeeuw EL, Rimfeld K, Eley TC, Boomsma DI, van Bergen E. Estimating effects of parents' cognitive and non-cognitive skills on offspring education using polygenic scores. bioRxiv. 2021 Jan 1:2020-09.Fletcher J, Wu Y, Li T, Lu Q. Interpreting polygenic score effects in sibling analysis. bioRxiv. 2021 Jan 1.Wang B, Baldwin JR, Schoeler T, Cheesman R, Barkhuizen W, Dudbridge F, Bann D, Morris TT, Pingault JB. Robust genetic nurture effects on education: A systematic review and meta-analysis based on 38,654 families across 8 cohorts. The American Journal of Human Genetics. 2021 Aug 19.Young AI, Nehzati SM, Lee C, Benonisdottir S, Cesarini D, Benjamin DJ, Turley P, Kong A. Mendelian imputation of parental genotypes for genome-wide estimation of direct and indirect genetic effects. BioRxiv. 2020 Jan 1.Reviewer #2 (Recommendations for the authors):I have no other specific recommendations. I think the approach and code provided are sound. I would mostly appreciate that the authors consider adding more details on the limitations (and possible extensions) of their approach regarding gene-environment interplay (both gene-environment moderation and gene-environment correlation). Reviewer #1 (Recommendations for the authors):GWAS data used for polygenic scoresThe decision to include only genome-wide significant SNPs was not justified. Please could the authors fill the readers in on this? Especially for EA, a highly polygenic trait, why not include all SNPs? It is also not clear why they performed the sensitivity analysis with the less powered Okbay GWAS – perhaps this could be put in the Supplementary Information. Can the authors comment on how their results are affected by pre- and post-natal IGEs being captured by the original GWAS used to construct polygenic scores ?We included only genome-wide significant SNPs in the construction of the EA score because the most recent GWAS meta-analysis by Lee et al., (2018) included UKBiobank individuals (and likewise for birthweight). We were concerned that sample overlap may inflate estimates of the association between the PRS and educational attainment (and any bias would be most severe for the hundreds of thousands of SNPs that did not reach genome-wide significance) which may in turn bias parameter estimates from our model. This was also our reason for including the less powered Okbay et al., GWAS as a sensitivity analysis. We have modified the text in the methods to make this clear for the reader:“We included only genome-wide significant SNPs in the construction of these scores because the most recent GWAS meta-analysis of educational attainment by Lee et al., (2018) included UKBiobank individuals. We were concerned that sample overlap would inflate estimates of the association between the PRS and educational attainment (and any inflation would be most severe for the hundreds of thousands of SNPs that did not reach genome-wide significance) and in turn bias parameter estimates from our model. For this reason, we also performed a sensitivity analysis using a second set of PRS of educational attainment using 72 SNPs from a GWAS of non-UK Biobank individuals .”As suggested by the reviewer, we have moved the results of the Okbay et al., PRS to the Supplementary Information (Supplementary File 5).weighted polygenic risk scores in a sibling between/within design. In contrast, our design doesn’t use siblings, and we have also conducted analyses using both weighted and unweighted polygenic risk scores. Unweighted scores should not be influenced by the sort of biases that Fletcher et al., describe.The Fletcher et al., (2021) study (which it is important to note at this stage is only a pre-print and has not been peer-reviewed) involves using Positioning among prior workThe authors could spell out more for readers their answer to the 'big picture' question of why it is useful to distinguish pre- and post-natal maternal effects, instead of just saying it's a natural question (see Introduction). Also in the introduction, it does not seem accurate to say that partitioning variation could yield mechanistic insights. Although the current contribution is very valuable, don't we need different kinds of designs and measures to understand mechanisms? In this regard, the authors should discuss the merits of their study compared to that of Armstrong-Carter et al. That study does not offer a principled way to separate pre- and post-natal effects, but it does try to work out which kinds of maternal phenotypes are involved in so-called pre-natal genetic nurture effects captured by EA PGS.We have changed the last two sentences of the first paragraph of the introduction to read:“Given the increasing number of variants identified in GWAS that exhibit robust maternal genetic effects, a natural question to ask is whether these loci exert their effects on offspring phenotypes through intrauterine mechanisms, the postnatal environment, or both. Indeed, resolving maternal effects into prenatal and postnatal sources of variation could be a valuable first step in eventually elucidating the underlying mechanisms behind these associations , directing investigators to where they should focus their attention, and in the case of disease-related phenotypes, yielding potentially important information regarding the optimal timing of interventions. For example, the demonstration of maternal prenatal effects on offspring IQ/educational attainment, suggests that if the mediating factors that were responsible could be identified, then improvements in the prenatal care of mothers and their unborn babies which target these factors, could yield useful increases in offspring IQ/educational attainment.”We have also added the following sentences to the discussion:“Whilst our method does not provide explicit mechanistic insight into the reason for the existence of maternal genetic effects , follow up studies could help elucidate these mechanisms by e.g. examining the association between maternal PRS and different pre- and postnatal maternal phenotypes, and performing mediation and Mendelian randomization analyses .”The authors should also engage more with other studies to clarify the novel aspects of their contribution. They describe in the Discussion the Domingue and Fletcher (2020) paper, but more relevant is Cheesman et al.,’s (2020) study design using the nonadopted-adopted polygenic score comparison in UKBB to estimate post-natal parental indirect genetic effects. Demange et al., (2021) estimated pre-natal parental indirect genetic effects by comparing estimates from adoption and sibling designs . Although this was for a non-cognitive skills polygenic score and not EA or BW, it is worth discussing their approach and comparing against their estimates. The Demange et al., approach means that estimates of pre-natal maternal genetic effects may also include assortative mating and population stratification. There is a general paragraph on AM in the Discussion, but how do the authors think that AM and popstrat will load in their model? Recent evidence from UKBB suggests that it is important to consider the role of population stratification particularly when studying EA .In order to discuss the Cheesman et al., (2020) and Demange et al., (2021) results in relation to our findings, we have added the following text to the Discussion section:“We are also not the first to have used adopted individuals in the UK Biobank to estimate the contribution of indirect genetic effects to phenotypic variability. Cheesman et al., (2020) contrasted adopted and non-adopted individuals in the UK Biobank to investigate evidence for indirect genetic effects on educational attainment and the existence of gene-environment correlation. The authors found that a genome-wide PRS for educational attainment was more strongly associated with educational attainment in non-adopted compared to adopted individuals consistent with the existence of indirect genetic effects on educational attainment. Likewise, G-REML estimates of SNP heritability were also lower in adopted individuals compared to non-adopted individuals .In a previous study of cognitive and non-cognitive effects on educational attainment, Demange et al., (2021) used three different sorts of study design to estimate the contribution of indirect genetic effects on educational attainment. They estimated indirect genetic effects by comparing between family and within family genetic effects in sibling pairs , by estimating the effect of non-transmitted parental alleles on offspring phenotype in parent-offspring trios, and by computing the difference in PRS associations between adopted and non-adopted individuals. In general, the authors found that estimates of indirect effects using the adoption study design were lower than those produced by the other two models. They interpreted this as being a consequence of the adoption study methodology not capturing the impact of prenatal maternal genetic effects  in contrast to the two other study designs. The present study explicitly capitalizes on differences between adopted and non-adopted individuals in their pattern of genotype-trait covariances to estimate the relative size of pre- and postnatal maternal genetic effects and to provide formal statistical tests for their presence. Indeed, both Demange et al.,’s (2021) and our findings are consistent with the existence of prenatal effects on educational attainment .”In order to discuss the potential impact of population stratification and assortment on our model, we ran some basic simulations to gain intuition for their likely effect on our model (not shown), and have included the following text in the discussion:2,6. It is possible that our basic model could be extended in a similar fashion to incorporate the effect of assortment and estimate some of these effects also.“We have also not modelled the complex effects of assortative mating in our SEM other than including covariance terms between maternal and paternal genotypes and assuming equal genetic variances in parents and their offspring under random mating . Positive assortment induces a number of complications when attempting to decompose the offspring phenotypic variance into its constituent sources of variation, including increasing homozygosity and the genetic variance at loci for the trait undergoing assortment relative to that expected under Hardy-Weinberg equilibrium, and inducing correlations between assorting loci across the rest of the genome both within and between individuals in the same family. Spousal correlations for phenotypes  and PRS  in complete parent-offspring trios in the UK Biobank suggest that assortative mating is likely present for educational attainment, and potentially to a lesser degree for (phenotypes correlated with) birthweight. Recent work by Balboa et al., (2021) and Kim et al., (2021) have shown how PRS of transmitted and non-transmitted alleles in parent-offspring trios can be used to estimate direct and indirect genetic effects on offspring phenotype and the variation attributable to the environmental influence of parents on offspring under phenotypic assortment Also relevant to this discussion is a series of data simulations performed by Demange et al., (2021) who found that estimates of indirect genetic effects obtained in adoption designs  were less biased by assortative mating and population stratification than estimates of indirect genetic effects obtained in biologically related parent-offspring trio designs . In the case of population stratification, the intuition behind this observation is that the contaminating effects of substructure are present in both adopted and non-adopted individuals. This contamination cancels out when the PRS-trait association in adopted individuals is subtracted from the PRS-trait association in non-adopted individuals. However, this is not the case in parent-offspring trios, where estimates of indirect genetic effects remain contaminated.In the context of our SEM, we expect that unmodelled population stratification will bias estimates of maternal prenatal and postnatal effects away from the null since population stratification will increase the covariance between maternal genotype and offspring phenotype, as well as the covariance between offspring genotype and phenotype in adopted and non-adopted individuals. Because we have limited our empirical analyses to individuals of white European ancestry and residualized the phenotypes for the first five genetic principal components prior to analysis in the SEM, any influence due to population stratification is likely to have been minor in our empirical analyses in the UK Biobank. The influence of assortative mating on our model is more difficult to predict, but our limited exploration of this issue by simulation (data not shown) suggests that the effect of assortative mating on parameter estimates from our SEM is likely to be minor.”Misc.It is neat to have a negative control phenotype for post-natal genetic effects (birthweight). Can the authors try out a negative control for pre-natal genetic effects too? If the maternal pre-natal effect were significant for something paternal substance use, this would bolster the idea that relatedness among adoptees and their adoptive parents causes inflation of this estimate.We like the reviewer’s idea of investigating a control phenotype, but it is exceedingly difficult to think of a trait for which (a) there is confirmed evidence for maternal genetic effects, (b) the maternal genetic effects are so strong that we have adequate power to demonstrate and partition them in our sample, and (c) we can be confident that the maternal genetic effects are postnatal but not prenatal effects.That being said, we have hit upon the idea of using individual’s report of maternal hypertension as a control phenotype. In theory, an individual’s report of maternal hypertension should show  association with a maternal PRS for hypertension in biological families  and with offspring PRS . In contrast, in adoptive families (G5-G7), and assuming adoptive parents are not biologically related to their adopted children, we would expect no association between reports of maternal hypertension (in adoptive mothers) and their adopted offspring’s PRS. Under these conditions, our SEM should “correctly” partition the maternal genetic effect entirely into a postnatal maternal genetic component. In contrast, if adopted individuals are biologically related to their adoptive mothers, then this will tend to lead to spurious associations between adopted individuals’ PRS and (adopted mothers’) maternal hypertension. Within the context of our SEM, this will increase evidence for a prenatal maternal genetic effect and decrease evidence for a postnatal maternal genetic effect. In other words, the degree to which our SEM produces evidence for a prenatal maternal genetic effect for individual’s report of maternal hypertension provides evidence consistent with the possibility that at least some adoptive individuals in the UK Biobank are biologically related to their adoptive parents.The results of our analyses are as follows, see In summary, estimates for the fetal and paternal genetic effects are what we would expect (i.e. close to zero). In contrast, the coefficients for the maternal genetic effects are larger and in the case of the pre-natal maternal genetic effect, potentially consistent with at least a proportion of adopted individuals in the UK Biobank being biologically related to their adoptive parents. However, a complication of this analysis is that self-reported maternal hypertension is a dichotomous, rather than quantitative phenotype. The corollary is that whilst we expect the point estimates of the parameters to be consistent estimates of the true population values in a large sample such as the UK Biobank , the standard errors and tests of significance will not be appropriate for this phenotype. We have spoken extensively with the developers of OpenMx  about the possibility of fitting the SEM as an ordinal model, but the developers have told us that this is not computationally feasible at the present time. For this reason, our preference is not to include these analyses in the published manuscript, but we have included them here for the reviewers’ information and in support of our contention that there is likely to be some degree of relatedness between adopted individuals and their adoptive parents in the UK Biobank.The use of breastfeeding data to identify those who were not adopted by relatives is smart. It would be worth checking how selected this sample is for some key traits . Also, could more information be leveraged to increase accuracy? Many participants just don’t know the answer to that question, could have remembered incorrectly, and wetnurses  also used to be common. Adoptions in later cohorts might have become more ‘organized’ (happening earlier in life and involving non-relatives). Perhaps it’s worth cutting out some of the older adoptees? The authors could also try limiting the sample further to those who also report their birthweight.Adopted individuals who knew their birthweight were more educated on average than those who did not, but did not differ on average in their birthweight.Following the reviewer’s suggestion, we stratified adopted individuals into those above and below age 60 and re-ran the analysis for EA. The results suggests that the deflation in post-natal maternal effects comes from adoptions in later cohorts, see It is possible that more recent adoptions are more likely to involve biological relatives of the adopted individual. We also removed individuals who reported their birthweight in addition to breastfeeding data. However, this only removed a further 223 adopted individuals, and did not appreciably affect our results. Given that the analyses have not materially affected our interpretation of the results, we have elected not to include them in the main manuscript.Was a relatedness cut-off used to ensure that each family was independent?rd degree of relatives defined by the pair-wise kinship >0.044 . In our original analysis, families in each of the 7 groups were not related based on this cut-off. However, we did not remove families that were related across groups. We have now removed all related families from our analysis. We maximized the number of families from adopted singletons (G5), followed by trios (G1), mother-offspring duos (G2), and then father-offspring duos (G3). Results from the new analysis are similar to our original analysis and do not affect our conclusions.We would like to thank the reviewer for bringing this to our attention. The relatedness cut-off was set at 3The authors could cite Wang et al., (2021) when saying that it is unfeasible for paternal genetic effects to be so much bigger than maternal effects – this meta-analysis found equal effects of paternal and maternal EA PGS on offspring educational outcomes.similar effect sizes for maternal and paternal genetic effects . Nevertheless, we appreciate that equal paternal and maternal effects on offspring educational attainment implies the absence of maternal prenatal effects. We have therefore at the reviewer’s suggestion included the Wang et al., reference and modified some of the text in the discussion:We thank the reviewer for this suggestion- however, our analyses actually showed “The absence of a significant postnatal maternal genetic effect on offspring educational attainment was surprising given that maternal genetic effects should be mediated through maternal educational attainment, and therefore should mostly involve postnatal pathways , and previous studies have shown that the effect of paternal and maternal PRS on educational attainment are roughly similar  i.e. suggesting the absence of prenatal maternal genetic effects.”Reviewer #2 (Recommendations for the authors):I have no other specific recommendations. I think the approach and code provided are sound. I would mostly appreciate that the authors consider adding more details on the limitations (and possible extensions) of their approach regarding gene-environment interplay (both gene-environment moderation and gene-environment correlation).We have added this information to Supplementary File 6 which discusses gene-environment interaction, gene-environment correlation and other limitations.


Ductal carcinoma in situ (DCIS) is a non-obligate precursor of invasive ductal carcinoma (IDC), whereby if left untreated, approximately 12% of patients develop invasive disease. The current standard of care is surgical removal of the lesion, to prevent potential progression, and radiotherapy to reduce risk of recurrence. There is substantial overtreatment of DCIS patients, considering not all DCIS lesions progress to invasive disease. Hence, there is a critical imperative to better predict which DCIS lesions are destined for poor outcome and which are not, allowing for tailored treatment. Active surveillance is currently being trialed as an alternative management practice, but this approach relies on accurately identifying cases that are at low risk of progression to invasive disease. Two DCIS-specific genomic profiling assays that attempt to distinguish low and high-risk patients have emerged, but imperfections in risk stratification coupled with a high price tag warrant the continued search for more robust and accessible prognostic biomarkers. This search has largely turned researchers toward the tumor microenvironment. Recent evidence suggests that a spectrum of cell types within the DCIS microenvironment are genetically and phenotypically altered compared to normal tissue and play critical roles in disease progression. Uncovering the molecular mechanisms contributing to DCIS progression has provided optimism for the search for well-validated prognostic biomarkers that can accurately predict the risk for a patient developing IDC. The discovery of such markers would modernize DCIS management and allow tailored treatment plans. This review will summarize the current literature regarding DCIS diagnosis, treatment, and pathology. Ductal Carcinoma in Situ (DCIS) refers to the non-invasive proliferation of neoplastic epithelial cells confined within the ducts of the breast. In the normal breast, the mammary gland consists of a ductal-lobular branching system lined by an inner luminal epithelial cell layer and an outer layer of contractile myoepithelial cells . Given tCompared to the age-adjusted average 10-year risk of a new diagnosis of DCIS of less than 3% , a womanSince up to 88% of untreated cases will remain in situ and potentially not progress, there is considerable overtreatment of DCIS patients. Standard locoregional breast cancer treatment (lumpectomy and radiation) costs the US Medicare system an estimated $10,538 per patient , equatinThe advent of nationwide mammographic screening programs has resulted in a substantial worldwide increase in breast cancer diagnoses . Today, Given that most DCIS is surgically resected, the natural course of the disease, including factors influencing its progression, are difficult to evaluate . HoweverThe prognostic uncertainty of DCIS means 98% of patients receive locoregional treatment involving surgery, with or without radiation treatment . SurgicaFour early randomized trials revealed that recurrence rates are significantly reduced by the addition of radiotherapy after conservative surgery for DCIS –24. The For the 70–80% of ER + invasive early breast cancers, treatments targeting the estrogen signaling pathway  are the standard of care in the adjuvant setting for the majority of patients . MoreoveThere are reservations regarding the de-escalation of the treatment of DCIS. These reservations are supported by evidence showing there is little to distinguish DCIS from invasive disease at the genomic level , suggestPhenotypically, the progression of DCIS to IDC is characterized by disruption of the myoepithelium and the basement membrane Fig.  43]. Th. Th43]. Our understanding of DCIS and factors influencing its progression to invasive disease is limited by how it can be modelled for research. A handful of DCIS cell lines, mostly from cell derived xenografts, are available for analysis. Three-dimensional organoid cultures can be made using these cell lines to recapitulate DCIS structures that may show invasion , althougGiven the difficulty in predicting an individual patient’s risk of DCIS recurrence, there has been considerable interest in identifying specific clinicopathological features that have predictive potential. Numerous randomized controlled clinical trials and observational studies have investigated the correlations between various clinicopathologic features and DCIS recurrence. These study findings have been examined in four independent meta-analyses that have established composite correlations with overall recurrence (in situ or invasive) , 67 and Breast cancer is recognized as a heterogeneous disease, and classification into clinically different subtypes is an important component of standard treatment. For IDC, subtype classification and estimation of risk of recurrence have traditionally been achieved by the assessment of a combination of clinical and pathological features, including tumor size, grade, node involvement, expression of ER and PR, and amplification of HER2. More recently, advances in high throughput molecular profiling approaches have demonstrated that breast cancers could be more accurately classified by considering the expression patterns of specific gene signatures. This finding has resulted in the development of several gene signature-based prognostic assays for clinical application . Two comAs DCIS cells evade ductal confinement, they undergo a number of phenotypic changes. Invasion and migration through the stroma is aided by epithelial to mesenchymal transition (EMT), where polarized epithelial cells de-differentiate, biochemically changing to a mesenchymal phenotype . CompareGiven that the majority of genetic changes occur early in the development of DCIS, the transition to invasive disease might not be regulated entirely at the genomic level but instead may be influenced by cellular relationships and interactions within and between DCIS and its microenvironment . EmerginImmune modulation is a critical hallmark of tumor progression, although until recently, breast cancer has largely been considered an immunologically silent cancer. That is, the immune infiltrate in breast tumor microenvironments was not used to classify the tumor or guide patient treatment. However, recent evidence proves that breast cancer can be highly immunogenic, containing immune cells from both the innate and adaptive immune system . Immune The progression from normal to neoplastic breast tissue is accompanied by quantitative, compositional, and phenotypic changes to the immune cell infiltrate. Immunogenicity progressively increases as normal breast tissue becomes more neoplastic Fig. . There iThe immune infiltrate of DCIS tissue is predominantly seen in the stroma and is less concentrated in the transformed ducts themselves. Recent reports suggest that early immune evasion by the DCIS cells is likely aided by an intact myoepithelium, where one study reported that DCIS regions with disturbed myoepithelial cell phenotypes display T cell infiltrations within the duct whereas in areas with normal myoepithelial cell phenotypes the T cells remain in the surrounding stroma . A recenAn increased immune presence in DCIS tissue is associated with poor prognostic features including high grade, hormone receptor negativity, HER2 positivity and comedo necrosis, suggesting that more aggressive tumors trigger a more intense immune response than less aggressive tumors Table  84, 85,, 113–119, there is substantial overtreatment of DCIS patients. The co-morbidities, toxicities, mental and financial impacts from the widespread overtreatment of DCIS patients motivates the identification of prognostic biomarkers that can distinguish between low and high-risk patients. Patients identified as having a high risk of invasive progression would benefit from extra medical intervention, including radiotherapy and systemic treatment, whereas those identified as having a low risk may require minimal treatment such as surgery alone or active surveillance. To identify predictive markers, the transition from DCIS to invasive ductal carcinoma needs to be better understood. There is increasing evidence for the critical role the microenvironment plays in tumor progression across all cancers. Such information can assist in classifying tumors and predicting treatment response. For DCIS, the limited mutational difference between in situ and invasive tumor cells, suggests it is likely that other cell types govern the transition. While the tumor cells themselves largely remain the same, stromal and immune cells are phenotypically different in DCIS and IDC tissue. Furthermore, some of the phenotypic abnormalities in these cells are evident to a greater extent in DCIS tissue that has invasive counterparts. Perhaps early expression changes can predict disease outcome. Future investigation involving larger cohorts combined with long-term follow up may generate noteworthy observations that could assist in creating a prognostic tool to accurately predict the fate of DCIS tumor cells. Such a tool would be implemented at the time of DCIS diagnosis and would optimize individual patient therapy, reducing the incidence of overtreatment. It is clear that the clinical value of the information embodied in the DCIS microenvironment has been under-exploited to-date and that implementation of a whole-tissue strategy to guide treatment has the potential to substantially improve personalized management of DCIS.Although non-invasive, DCIS may progress to invasive disease if left untreated. Since there is no accurate means of predicting which patients’ DCIS will progress to invasive disease, every patient is presently considered to have the same risk of progression. Clinically, this means patients are treated with surgery, radiotherapy and in some cases endocrine therapy. The current management of DCIS patients undoubtedly mitigates the risk of recurrence and progression. However, considering that up to 60% of untreated cases will never progress to IDC and will instead remain indolent


Personacardiorespiratory and musculoskeletal deconditioning, ii) dietary interventions to counteract disease and/or treatment-related malnutrition, support anabolism and the metabolic cost of exercise; iii) psychological interventions to reduce stress, anxiety and associated morbidity; iv) the cessation of adverse health behaviours; and v) behavioural modification to support intervention initiation and adherence in the perioperative setting, whilst establishing self-management skills for long-term health behaviour change. In the perioperative setting, prehabilitation interventions are often combined with medical optimization of comorbidities  through collaboration with specialists expert in the management of long-term conditionsMultimodal prehabilitation interventions are often comprised of two or more of the following: i) aerobic and resistance training to attenuate Santa Mina et al. set the scene by introducing the concept of multiphasic prehabilitation across the cancer continuum. Multiphasic prehabilitation is an innovative paradigm shift away from reactive assessments, that happen too late in the patient cancer pathway to be of any use in preparing or optimising patients for major surgery or cancer treatment, towards proactive early intervention. Multiphasic prehabilitation is intended to provoke investigation of proactive interventions that focus on periods of relative health where the ‘maximum tolerable dose’ for a health intervention can be pursued more readily in the absence of active treatments that often erode fitness, appetite, mental health and motivation . Multiphasic prehabilitation is individualised through screening and targeted assessment. This requires nuance and tailoring to the existing and anticipated experiences at each phase of the cancer journey to minimize treatment-related side effects and subsequent treatment delays, thereby improving wellbeing and potentially improving long term outcomes. The ‘aggressive’ push for patient preparation in this setting, may be akin to training models of high-performance sport with cyclic rounds of training prior to competition, both with similar goals: to optimize health preceding an anticipated stressor, ensuring ‘maximal performance’ and rapid recovery.Waterland et al. illustrate the growing prehabilitation literature base and recent clinical recommendations in their updated systematic review and meta analyses. Prehabilitation improved preoperative functional capacity and substantially reduced hospital length of stay, however they did not find a significant reduction in postoperative complications, 30-day readmissions or postoperative mortality. They emphasise various points for future research including, the assessment of prehabilitation cost effectiveness, the need for new technology to tailor interventions and outcome, and measurement standardisation across the literature to allow for more efficient data utilisation and cleaner meta-analyses that minimises research waste. Mao et al. provide an interesting updated systematic review and meta-analysis illustrating that pulmonary rehabilitation is a meaningful addition to the whole perioperative patient pathway and demonstrating its value in reducing post-operative pulmonary complications. This review lends its support to previous literature in the pulmonary rehabilitation field, where exercise prescription has always been an integral component.Brahmbhatt et al. demonstrated an improvement in functional capacity using a multimodal exercise intervention composed of resistance and aerobic exercise. Although not inherently novel, this interventional study carefully documents participant experiences interrogating intervention design preferences, perceived benefits, behaviour change, prehabilitation as education and the ability of patients to regain control. Unanimously, participants spoke positively about multimodality prehabilitation with inclusion of dietetic and psychological support to manage emotional stress and optimise health, echoing the concept of multiphasic prehabilitation across the cancer continuum  represent ‘active ingredients’ in boosting the success of prehabilitation strategies, using goal setting, graded tasks and self-monitoring, to promote longer-term behaviour change. This can also be a research endpoint, where behavioural scientists may set out to understand behaviour strategies to improve motivation and compliance with the exercise component of prehabilitation. Working alongside clinical colleagues, behavioural scientists are well-placed to employ intervention mapping processes, behavioural analysis, and patient-centred intervention development. They can also provide training to colleagues delivering the programs to ensure the identified BCTs embedded within it are employed appropriately. Grimmett et al. highlight the importance of including qualitative process evaluation in the design of new prehabilitation trials as it together with BCTs, are vital to the integrity of the multimodal prehabilitation intervention and maximise positives outcomes.ontinuum . ImportaGillis et al. through their scoping review show that current prehabilitation literature lacks standardised and valid nutritional assessment methods, often coupled with interventions that lack an evidence basis. They conclude that nutrition interventions were inconsistently applied and lack adherence to accepted nutritional guidance published in oncology or surgery. Large gaps in the evidence exist in adopting validated nutritional screening and malnutrition assessment methods, interrogation of the ‘effect modification’ of a single interventional treatment effect on outcomes independent of nutritional status, intervention monitoring, adherence, evaluation, and cancer cohort heterogeneity. Gillis et al. go a step further in describing the nutrition care process for perioperative patients advocating its adoption in surgical prehabilitation. Here they emphasise the importance of developing a core outcome and measurement dataset to effectively address these substantial research gaps in surgical prehabilitation studies, allowing data meta-analyses and reduce research waste.Barberan-Garcia et al. discuss the challenges in digital innovation in surgical prehabilitation, highlighting real-world issues and potential solutions e.g. digital physical activity prescription and behaviour change techniques aimed at improving perioperative outcomes. Digital technologies and remote monitoring is the future of true personalised prehabilitation.The implementation of prehabilitation in real-world clinical practice is limited, mainly due to logistical large-scale deployment, patient access and economical sustainability. These limitations can be overcome by innovative use of technology to facilitate screening, assessment, remote monitoring, and true personalisation of interventions. We hope that this Research Topic presents a balanced view of the current challenges in cancer prehabilitation. We must move away from a ‘one size fits all’ approach and usher in a new era of individualised patient tailored multi-modal prehabilitation focussed on improving patient care and outcomes throughout the cancer journey.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.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.


In this review, we have conducted an extensive literature review and have drawn from our collective experiences to outline critical insights in designing preclinical studies for botanical foods as anticancer investigational new drug (IND) applications as well as to highlight the critical criteria when initiating early-phase clinical trials for botanical food products. This review is for those who wish to understand how botanical foods and their derivatives can contribute to cancer pathobiology, as well as to develop relevant therapeutic approaches to treat cancer by conducting clinically oriented and evidence-based translational studies under United States Food and Drug Administration (USFDA) regulatory frameworks.Cancer progression and mortality remain challenging because of current obstacles and limitations in cancer treatment. Continuous efforts are being made to explore complementary and alternative approaches to alleviate the suffering of cancer patients. Epidemiological and nutritional studies have indicated that consuming botanical foods is linked to a lower risk of cancer incidence and/or improved cancer prognosis after diagnosis. From these observations, a variety of preclinical and clinical studies have been carried out to evaluate the potential of botanical food products as anticancer medicines. Unfortunately, many investigations have been poorly designed, and encouraging preclinical results have not been translated into clinical success. Botanical products contain a wide variety of chemicals, making them more difficult to study than traditional drugs. In this review, with the consideration of the regulatory framework of the USFDA, we share our collective experiences and lessons learned from 20 years of defining anticancer foods, focusing on the critical aspects of preclinical studies that are required for an IND application, as well as the checkpoints needed for early-phase clinical trials. We recommend a developmental pipeline that is based on mechanisms and clinical considerations. Although extensive efforts have been dedicated to improving therapeutic strategies, cancer remains a significant global health burden and a leading cause of death . For manA growing number of multi-omics technologies, as well as advanced experimental and computational tools, have allowed researchers to delve deeper into the complexities of cancer . Within Although significant advances in therapy have been made for oncogene-driven cancers and in immuno-oncology, both approaches still face significant challenges because such treatments are effective only in a subset of patients whose cancers express the relevant targets ,10. OfteTo achieve long-term survival benefits, drug combinations simultaneously target multiple molecular alterations or cellular compartments, and thus have been extensively discussed and actively tested in preclinical and clinical settings ,13. The The shift in the approach to cancer therapy has led to the search for multitarget anticancer drugs ,19. BecaThe intent of this review is not to provide an overview of anticancer foods along with their phytochemical profile and mechanism of action, because there have been a wide variety of review articles in this direction. Rather, we would like to share our collective experiences in the characterization of anticancer foods under USFDA regulation. The Botanical Drug Development Guidance for Industry was issued by the USFDA in 2016 , whereasThe public and social media have widely promoted the concept of “anticancer super foods” when referring to foods that claim to protect humans against cancer incidence and progression. While the concept may have been exaggerated, there is some validity to the claims. “Anticancer super foods” largely consist of botanical foods, such as edible mushrooms, berries, broccoli, tomatoes, walnuts, grapes, and others . As noteTo fully understand the obstacles faced by health care professionals, we have evaluated several professional sources  to extraOur review of these six databases has led to the identification of the ten botanical foods that have evidence-based anticancer activity . In the Botanical food products are widely accepted as non-toxic agents, but consuming botanical products derived from plants may expose cancer patients to risk from food–drug interactions related to cytochrome P450 (CYPs) enzymes ,98. CYPsAlthough some health care providers are enthusiastic about using plant foods as cancer therapy from the limited data obtained in in vitro, in vivo, and in silico approaches, most require much more knowledge of the mechanisms of action of these agents. Before designing preclinical and clinical studies, it is important to be aware of the unique physiological and pharmacological processes that take place in the human body when consuming botanical foods or related products compared to the processes involved in consuming conventional single-agent synthetic drugs. Unlike synthetic drugs that are artificially modified for bioavailability and therapeutic effects, the bioactive compounds such as carotenes, flavonoids, and phenols found in botanical foods are naturally occurring chemicals. Therefore, the intrinsic bioavailability of functional molecules is a critical factor, which can ultimately affect their anticancer activity . The phyIn addition to bioavailable phytochemicals, extracellular vehicles (EVs) derived from plant foods are emerging as novel functional components. EVs are lipoprotein structures with a wide range of sizes, ranging from 20 to 500 nm. The fruits, flesh, and roots of many plant species can be used to isolate plant-derived EVs (P-EVs), which contain proteins, RNAs, and lipids . From imThe small intestine is also the site where food-induced immune responses are processed . Gut-assIn addition to the gut epithelial barrier and mucosal immune system, gut-microbiome (including microbiome-encoded enzymes) has proven as another determinant of drug pharmacokinetics and therapeutic response by modifying the drugs’ physicochemical properties  and/or transforming the drugs’ activity  . ParticuIn summary, scientists could better conceptualize and design preclinical and clinical research toward transforming botanical foods into anticancer therapies if they have a comprehensive understanding of the interface between food and drugs, as well as the physiology and pharmacology of foods in humans.treat, cure or prevent any disease involving safety and clinical endpoint evaluation”, the USFDA needs to approve an Investigational New Drug (IND) application prior to conducting human studies. Published by the USFDA in 2016, the Botanical Drug Development Guidance for Industry  has been used. We have introduced Lentinan, the bioactive β-glucans and potent immunomodulator isolated from Shiitake mushrooms, as the reference drug . LentinaThe “totality-of-the-evidence” approach is a rigorous quality control (QC) process that is recommended by the USFDA for food-based botanical drugs ,30,31. WBased on “prior human experience” and mechanism-based clinically relevant preclinical studies, our team at the City of Hope has conducted the first single-arm open-label clinical phase I trial of WBM tablets in 36 patients with biochemically recurrent (BCR) prostate cancer. It is a trial without a placebo control, in which both the investigators and the patients are aware of the drug being given while the patients in the control groups do not receive WBM tablets . In thisBased on our phase 1 trial findings, we have conducted several reverse-translational studies to understand the various biological activities that WBM exerts. Through those studies, we have demonstrated that WBM contains AR antagonists that can block the activation of AR signaling, which is the driving force of prostate cancer progression . The knoWe thus have designed two cohorts in our currently ongoing open-label randomized control (RCT) phase 2 trials (NCT04519879) using WBM. Patients with BCR prostate cancer (cohort 1) and low-risk prostate cancer (cohort 2) are chosen as the targeted populations. The neuroendocrine type of prostate cancer is more aggressive and is excluded from our clinical trial . ParticuThe information gained from the aforementioned studies has greatly improved our overall understanding of how WBM may contribute to the prevention and treatment of prostate cancer while serving as an important scientific basis for the future development of WBM as anticancer medicine.There is an enthusiastic public interest in the idea of anticancer superfoods since these foods are considered by many in the public to be safer than synthetic drugs because anticancer superfoods are natural products. Following a diagnosis of cancer, nearly half of American patients begin taking food-derived products or seek advice on consuming these products . HoweverAmong the controversies about anticancer foods is whether they should be consumed as whole foods or as dietary supplements derived from them. It is a concern that the manufacturing and application of dietary supplements in cancer care are largely unregulated  AccordinAnother trending discussion with great clinical interests involves combining botanical products as (neo)adjuvant treatments or concurrently with conventional cancer treatments such as chemotherapy, radiotherapy, and immunotherapy ,215. SucAs a final note, we hope that this article will provide researchers, health care providers, and industry sponsors with landscape-based perspectives and insights. This article is for those who wish to understand how botanical foods and their derivatives contribute to cancer pathobiology, as well as develop relevant therapeutic approaches to treat cancer by conducting clinically oriented and evidence-based translational studies under USFDA regulatory frameworks.


DNA viruses often persist in the body of their host, becoming latent and recurring many months or years later. By contrast, most RNA viruses cause acute infections that are cleared from the host as they lack the mechanisms to persist. However, it is becoming clear that viral RNA can persist after clinical recovery and elimination of detectable infectious virus. This persistence can either be asymptomatic or associated with late progressive disease or nonspecific lingering symptoms, such as may be the case following infection with Ebola or Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Why does viral RNA sometimes persist after recovery from an acute infection? Where does the RNA come from? And what are the consequences? Most RNA viruses cause acute infections that are cleared from the host as they lack the mechanisms to persist; however, phenomena such as "long COVID" suggest that viral RNA can persist after clinical recovery and elimination of detectable infectious virus. This Unsolved Mystery article explores the meaning, origins and consequences of such persistent RNA. Viruses are obligate intracellular infectious agents that are maintained in a population by continuous transmission to new susceptible individuals. In the absence of a reservoir, such as an insect vector or animal population capable of facilitating transmission to humans, viruses require alternative strategies to remain within human populations . HerpesvHowever, it has become increasingly clear that recovery, elimination of infectious virus, and development of immunity to acute nonretroviral RNA viruses do not necessarily mean simultaneous elimination of the viral RNA –22. The The occurrence of long-term persistence of viral RNA has been known for decades, particularly in sites with specialized relationships to the immune system , an early example being the identification of measles virus as the cause of subacute sclerosing panencephalitis (SSPE), a progressive fatal central nervous system (CNS) disease that becomes manifest many years after apparent recovery from the original acute measles virus infection –53. MoreAdaptive immune response—production of virus-specific antibodies and T cellsAntigen—viral component, usually a protein, which stimulates production of virus-specific antibodies and T cellsCardiomyopathy—dysfunction of the heart muscleCytolytic—causing death of a cell due to lysisCpG—pairing of cytosine and guanosine in nucleic acid that is unusual in cellular RNA and DNAEncephalomyelitis—inflammation of the brain and spinal cord that can be a response to viral infectionImmunocytochemical assays—methods for microscopically visualizing proteins, such as viral proteins, in cells using antibody to the proteinInnate immune mechanisms—intrinsic cellular responses to infection that usually occur rapidly and can often control pathogen replication and spread prior to induction of adaptive immune responsesMHC class I—polymorphic MHC; molecule that can bind viral peptides produced by infected cells, displaying them on the cell surface for presentation to virus-specific CD8 T cells that may be able to kill the infected cellPeripheral blood mononuclear cells—lymphocytes and monocytes present in circulating blood that come primarily from bone marrow and lymphoid tissue and may infiltrate sites of infectionRibonucleocapsid—viral RNA surrounded by nucleocapsid proteinReverse transcriptase polymerase chain reaction (RT-PCR)—it is a method for converting RNA into a DNA copy for subsequent amplification using a thermostable DNA polymerase and primers specific for the gene of interest. The amplified product can be quantified or sequenced.Uveitis—inflammation of the uvea, which is the middle vascular layer of the eyeHowever, viral RNA persistence is not restricted to sites classically considered immune privileged, but can also occur in other sites including blood, lymphoid tissue, joints, respiratory tract, gastrointestinal tissues, and kidney, with a variety of known and unknown consequences ,14,58–61Viral RNA persistence in the absence of culturable virus is typically detected in RNA extracted from secretions, blood, or tissue samples. For long-lived cells such as neurons or cardiac myocytes, this RNA is presumed to come from the originally infected surviving cells present in those samples. However, few studies have attempted to identify or characterize the cellular source of the RNA detected, and clearance from some tissues may be more effective than from others. For example, after recovery of experimentally infected nonhuman primates from acute Ebola and Marburg filovirus infections, viral RNA is no longer detectable in primary sites of replication such as the liver but can often be found in the eyes and testes, where macrophages and Sertoli cells, respectively, remain RNA positive ,46,50. TDetection of infectious virus is inherently less sensitive than detection of viral RNA and may be influenced by the presence of neutralizing antibody in the sample. Cocultivation of cells from tissues or secretions with susceptible cells is required to recover viruses such as measles but may not have been attempted for studies reporting the presence of viral RNA. Therefore, lack of detection of infectious virus may be due in part to differences in sensitivity and availability of the assays used. Development of techniques that can more easily identify the presence of assembled virions capable of initiating infection would provide increased understanding of the clearance and persistence of RNA viruses.Because infectious virus cannot be recovered and RNA is susceptible to degradation, it is often assumed that what is detected by reverse transcriptase polymerase chain reaction RT-PCR;  is fragmFor picornaviruses, positive-strand RNAs are detectable for longer than negative-strand RNAs, and for coronaviruses, genomic RNAs are detectable for longer than the subgenomic RNAs that are produced during active virus replication ,79. HoweFor Borna disease virus that replicates in the nucleus, persistently infected cells retain genomic RNA in aggregates of viral ribonucleoproteins tethered to host chromosomes with host nuclear proteins that are maintained in daughter cells through the cell cycle ,81. HoweInnate immune mechanisms  can contComplete clearance of virus and virus-infected cells requires both prevention of virus spread to new cells and elimination of previously infected cells, either through virus-induced or immune-mediated cell death. Although viruses frequently lyse cells in tissue culture, primary cells and cells infected in vivo are often resistant to induction of cell death. These cells activate intrinsic cellular pathways that promote survival and combine with both host and viral strategies to downregulate replication and prevent lethal damage to the infected cell  Fig 3)..Fig 3). + T cells that recognize viral peptides expressed in the context of MHC class I molecules. In addition, binding of antibodies to the infected cell surface can direct cells toward antibody-mediated cytotoxicity or phagocytosis by immune cells [Immune mechanisms for eliminating virus-infected cells that survive infection include cell killing by cytotoxic cells such as natural killer cells, which recognize a lack of major histocompatibility complex (MHC) class I expression , and CD8ne cells –96. Therne cells ,98. Antine cells –103. Thune cells –107. T cne cells ,108. ThuTo escape clearance, viruses must avoid both elimination by the immune response and killing of all infected cells, processes that are more likely to occur in some types of cells and tissues than in others. Avoiding immune-mediated clearance mechanisms requires the infected cell to become invisible to the immune system or unresponsive to cytolytic immune effectors by eliminating both surface expression of viral proteins and MHC presentation of viral peptides. Viruses infecting long-lived cells in immune-privileged tissues may be particularly likely to survive and retain persistent RNA after infection ,109–113.Survival of infected cells is often accompanied by acquisition of viral mutations that foster persistence. For example, for viruses that are assembled and released from the cell surface, mutations that limit or prevent cell surface expression of viral proteins can prevent recognition by antibodies. In the measles virus-induced late disease SSPE, virion proteins required for particle assembly at the plasma membrane  have acquired changes that prevent cell surface expression and virion assembly but promote cell-to-cell ribonucleoprotein transfer to uninfected cells, thereby allowing continued spread of viral RNA without producing infectious virions –124. SimPersistence in cells that are replaced more frequently  may continue for shorter periods of time. In lymphocytic choriomeningitis virus (LCMV) infection of cell fate reporter mice, noncytolytic clearance from hepatocytes is accompanied by continuous infection of new cells to maintain persistence . EpithelAvoiding virus-induced cell death usually requires limiting virus replication ,128. A vReplication may also be restricted through activation of IFN pathways and expression of IFN-stimulated genes encoding antiviral proteins. For example, production of defective viral genomes (DVGs), particularly so-called “copy-back” DVGs, by many RNA viruses leads to induction of innate immune responses that control virus replication and permit persistence . Copy-baViral RNA alone may stimulate innate immune responses and inflammation associated with IFN production to drive chronic inflammation . HoweverConsequences of chronic immune stimulation associated with persistent RNA are dependent on the site of persistence. For example, persistence of RNA in the CNS of mice that have recovered from acute alphavirus-induced encephalomyelitis is accompanied by detection of viral protein weeks after infection, and maintenance of B cells secreting antiviral antibodies and T cells producing IFN-γ for more than a year ,149–152.Determining the importance of RNA persistence is of particular relevance for understanding the failure to fully recover from acute infections such as occurs after SARS-CoV-2 infection and Ebola virus disease. PASC afflicts 30% to 50% of those recovering from COVID-19  and enco+ T cells and antibody-secreting cells in circulation and affinity maturation of antiviral antibody [Persistence and long-term immune stimulation in lymphoid tissue may also provide benefit to the host via prolonged stimulation and induction of durable immunity to reinfection ,70. Macaantibody . This coantibody –161.Clinical recovery, elimination of detectable infectious virus, and development of immunity after infection with RNA viruses that cause acute infections do not necessarily result in complete elimination of the viral RNA. Both virus and host mechanisms can prevent production of infectious virions while allowing persistence of viral RNA in previously infected cells. Viral mechanisms include mutations in genes coding for proteins required for assembly or replication and evasion of the adaptive immune response. Host mechanisms include the use of noncytolytic clearance mechanisms that allow infected cells to survive and cell type–specific activation of innate immune responses that suppress virus replication in infected cells. How RNA is protected from degradation is unclear, but occasional late transmission and continuing stimulation of adaptive immune responses indicate persistence of genomic and translatable viral RNA.Our understanding of the long-term consequences related to disease and durable immunity and the mechanisms of persistence will benefit from further investigation and development of appropriate animal models. Future studies will be needed to identify the types and locations of cells harboring viral RNA and the metabolic state of these cells compared with uninfected cells. In addition, a better understanding of the state of the viral RNA, how it is protected from degradation, the relative amounts of full-length and DVG or fragmented RNA, and the contribution of continued RNA synthesis to persistence will help to solve this mystery and inform potential interventions. Identification of the role of RNA persistence in late disease could be advanced with longitudinal studies that evaluate treatments that suppress RNA replication and examine their effects on RNA persistence and long-term outcomes.


While these cycles are usually planned with elite athletic performance in mind, they can also be incorporated into the clinical setting, for instance to monitor athlete or patient response after injury or illness. Although similar conceptually, the design of the periodization (training load and recovery ratio) and mix of training variables  would be very different between athlete and diseased population.While scientific knowledge in the performance and training aspects of exercise physiology has grown in the past few decades, especially in the application of molecular and cellular techniques to interrogate mechanistic pathways, similar progress has not been achieved in elucidating the science of recovery from exercise training. In elite and recreational sports, there has been a worldwide explosion of interest in marathons, ultra-endurance races, as well as high intensity interval training (HITT) programs implemented across a wide variety of settings. These events involve large numbers of participants, meaning that individuals now have access to, and may be performing significantly higher amounts of exercise than may be optimal for their health. In addition, there has been growing acceptance for exercise training as concurrent treatment programs for clinical disease, such as for cancer. In such clinical populations, the duration of recovery between exercise sessions could be very different from healthy populations. This special collection called for submissions investigating physiological responses during recovery or rest phases. Such rest phases are usually contextualized within athletic training periodization, where an entire season of training is termed the Allan et al.), raised an important concept for exercise training in cancer patients, which is the often-underappreciated metabolic stress of the disease, particularly for those with advanced disease and experiencing tissue cachexia. The authors contend that the additional metabolic stressor may counteract some beneficial adaptations of exercise, given a continuous tissue breakdown, increased systemic and tissue inflammation, as well as the increased energetic needs for anti-tumor immunity. This situation thus results in the competition between the tumor and the host for metabolic resources, ultimately compromising potential benefits that could have been gleaned from exercise training. The authors suggest that exercise programs prescribed for cancer patients should be lower in terms of volume/intensity and furthermore, to allow sufficient time for recovery, meaning that the frequency of exercise sessions must be tapered down and personalized for each individual.In the first submission,  is explicit and can be quantified accurately , accurate quantification of internal training load  is more challenging, especially in field settings. Accurate qualification and matching of the internal and external training loads can potentially offer advantages in optimizing training adaptation and in personalizing training program design. The measurement heart rate and rating of perceived exertion (RPE) have been time-tested tools for quantifying internal training load in both research and sport training. However, fundamental gaps exist in their sensitivity to accurately reflect internal training load when used alone, indifferent forms, or in combination with other parameters.Pind et al.) investigated these classical issues of sport science and training in their study on elite rowers undergoing 4 weeks of training camp. The elegance of this study is found in the fine dissection of HR and RPE, in different forms and combination, to track the dynamics of the external training load over 4 weeks. A key finding from this study is that as training intensity increased, RPE had a stronger association with external training load than heart rate, and the state of fatigue was positively associated with RPE, but not with heart rate. These results support the notion that “internal” adaptations to training stresses is due to the sum of both psychological and physiological responses, which is better sensed by RPE than by heart rate. This study should also arouse the curiosity of researchers and practitioners on the potential cross-talks between the psychological and physiological systems in regulating training response and adaptation i.e., human performance as a whole being.Pind and others , assessed the electrocardiogram (ECG) analyses, transthoracic echocardiographic examinations and cardiopulmonary exercise testing (CPET) of 27 young male professional basketball players. The cohort was segregated retrospectively into 12 with early polarization (ER) pattern and 15 without ER pattern. Once thought to be benign, there have been higher associations of cardiac events or abnormalities in endurance trained, and non-athletic populations with ER pattern. This study addressed a gap by studying professional basketball players. The authors found that professional male basketball players with an ER pattern had higher absolute and relative peak oxygen uptakes in CPET compared to those without ER pattern, concurring with the common ER phenomenon in athletes and that increased exercise training is associated with a greater occurrence of ER pattern. The professional basketball players with ER patterns also had larger left ventricular end-diastolic diameter, left and right atrial end systolic diameters, and left ventricular mass Index without significant differences in relative wall thickness and ECG parameters at rest and during exercise testing. These findings contribute towards our understanding of cardiac remodeling in athletes with ER pattern. Specific athletes present with abnormalities such as early repolarization, which may suggest a greater risk for atrial fibrillation and sudden cardiac death, even during early pre-season, when they are engaging in low-intensity and high-volume training, This study highlights the need for echocardiographic assessment of professional basketball players as part of individualized prescription, to monitor cardiac remodeling throughout the season.In “Electrical and Structural Adaption Athlete’s Heart and the Impact on Training and Recovery Management in Professional Basketball Players Retrospective Observational Study”,  regarding “Higher muscle damage triggered by shorter inter-set rest periods in volume equated resistance exercise”. This article nicely shows the impact of rest period variations on inflammatory responses and muscle damage due to a small variation of recovery time between five sets of 10 reps resistance exercise performed at the same volume and at the same intensity of the 10RM. One- or 3-min rest periods significantly influenced circulating concentrations of selected biomarkers. Shorter recovery between sets produced greater increases in selected variables post exercise compared to longer recovery. The authors concluded that a 1-min rest condition in volume equated resistance exercise promoted greater overall muscle tissue damage with a longer duration of the 24 inflammatory processes compared to the 3-min rest conditions. These results clearly highlight the need to consider additional variables of training load beside intensity and volume of exercise such as highlighted recently  principle . This prrecently .The articles presented in this special collection have addressed the science of rest or recovery from exercise training across the clinical, recreational and professional exercise settings. In this regard, recovery needs to be personalized to achieve the greatest benefits from training for both health and performance. There is much still unknown in terms of recovery optimization and should be a focus for future research.