Electrically assisted bicycles (e-bikes) have been highlighted as a method of active travel that could overcome some of the commonly reported barriers to cycle commuting. The objective of this systematic review was to assess the health benefits associated with e-cycling.A systematic literature review of studies examining physical activity, cardiorespiratory, metabolic and psychological outcomes associated with e-cycling. Where possible these outcomes were compared to those from conventional cycling and walking. Seven electronic databases, clinical trial registers, grey literature and reference lists were searched up to November 2017. Hand searching occurred until June 2018. Experimental or observational studies examining the impact of e-cycling on physical activity and/or health outcomes of interest were included. E-bikes used must have pedals and require pedalling for electric assistance to be provided.Seventeen studies  were identified involving a total of 300 participants. There was moderate evidence that e-cycling provided physical activity of at least moderate intensity, which was lower than the intensity elicited during conventional cycling, but higher than that during walking. There was also moderate evidence that e-cycling can improve cardiorespiratory fitness in physically inactive individuals. Evidence of the impact of e-cycling on metabolic and psychological health outcomes was inconclusive. Longitudinal evidence was compromised by weak study design and quality.E-cycling can contribute to meeting physical activity recommendations and increasing physical fitness. As such, e-bikes offer a potential alternative to conventional cycling. Future research should examine the long-term health impacts of e-cycling using rigorous research designs.The online version of this article (10.1186/s12966-018-0751-8) contains supplementary material, which is available to authorized users. Given the high rates of global physical inactivity  a growinTravel is an essential part of everyday life for most people, and the adoption of active travel represents an efficient way to increase daily physical activity. For example, Falconer and colleagues  found thDue to a growing body of evidence, the UK National Institute of Health and Care Excellence (NICE) now endorse active travel, with a particular focus on commuting, as a feasible method to incorporate physical activity into daily life . HoweverElectrically assisted bicycles (e-bikes) have been highlighted as an alternative method of active travel that could overcome some of the commonly reported barriers to cycle commuting . The terWhat is the intensity of physical activity associated with riding an e-bike?Does use of an e-bike lead to changes in health outcomes including cardiorespiratory, metabolic or psychological outcomes?Do physiological responses to riding an e-bike differ to those generated by other modes of active transportation ?In recent years e-bikes have become commonplace in European countries  with prohttp://www.crd.york.ac.uk/prospero). This review was conducted according to the guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines [A review protocol was registered at the PROSPERO database: Registration number CRD42018086544 , ISI Web of Science, CINAHL complete, SPORTDiscus and Scopus. Search terms were participants: adults ≥18 years of age,electrically-assisted bicycle must have pedals and be operated by the individual, with assistance available from an electric motorat least one of the following outcomes; objective measure of physical activity intensity whilst e-cycling , cardiorespiratory, metabolic or quality of life ,type of study: experimental or observational studies.Studies were eligible for inclusion if they met the following criteria:Studies could be published or unpublished in any language. For articles in a language other than English the title and abstract were translated using Google Translate. If full text screening was required, the article was translated by an individual fluent in the language. Studies were excluded if they reported using bicycles that did not require the individual to pedal to provide power, were review articles or commentary pieces, and/or used self-reported measures of physical activity. Title and abstract screening was conducted by two reviewers independently (J.E.B. and S.S.). There was a 93% agreement between reviewers on title and abstract screening. Full texts were screened by the two reviewers independently and any discrepancies were discussed.The quality of included studies was assessed using the Quality Assessment Tool for Quantitative Studies . The toA global rating for each study was then determined based on the criteria; 1) strong when no weak ratings were reported, 2) moderate when one weak rating was reported, and 3) weak when two or more components were rated as weak. This tool has been used in a previous review examining the impact of cycling on health . The bliMembers of the review team (J.E.B and either S.S. or A.R.C) independently extracted data for each study. Quality assessment was confirmed by a fourth reviewer (R.P.). Data were extracted using an adapted version of a Cochrane Data Extraction Form, which was piloted prior to use. Discrepancies regarding data extraction were resolved through discussion between reviewers. Data extracted included study design, characteristics of participants, outcomes measured, and results. Due to the heterogeneity of study design and outcomes reported, a meta-analysis was not deemed appropriate. Data were synthesized and presented narratively. The effect of the intervention on physical activity and health outcomes for each study was summarized based on reported statistical significance and effect size, both within group (pre-post) and between group where possible, or by examining means or medians when no hypothesis testing was conducted.clinicaltrials.gov but were excluded for the following reasons: 1) data not published, 2) currently recruiting, 3) authors were not reachable.A total of 4399 articles were identified through initial searches Fig. . After rEleven studies examined the acute physiological impact of e-cycling using cross over designs, five of which were randomized Table . Nine stRest periods between conditions ranged from 2-min to 1 month and distance ridden from 3.54 to 27 km. Nine studies were conducted in a natural setting with topography ranging from flat to elevations between 33.5 and 260 m. Four studies specifically examined the impact of topography on physiological outcomes by separating rides into different topographical sections  and the USA. Sample sizes ranged from 20 to 32, with a total of 153 participants. Most participants were between 30 and 50 years of age. Four studies recruited physically inactive individuals [Six studies examined the longitudinal impact of e-cycling, using a variety of study designs Table . All stuividuals , 29–31. ividuals . Interveividuals . Three sividuals , 29, 30.Studies reported a range of outcomes related to physical activity intensity. Given the heterogeneity between studies regarding route length and topography, mean values and/or percent of maximum values during conditions are reported to enable comparison between studies. Physiological outcomes reported within the manuscript include oxygen uptake, metabolic equivalents,Eight studies reported oxygen uptake , 25–27. Nine studies reported mean estimated METs while riding an e-bike at a self-selected intensity , 26, 27,Four studies assessed energy expenditure per minute , 24, 27.Twelve studies reported heart rate while e-cycling , 30, 32.Five studies assessed power output during conditions , 26, 27.Overall, e-cycling was performed at a moderate intensity, but the intensity was lower than during conventional cycling. Most studies reported significant differences in the associated outcomes between e-cycling and conventional cycling. However, one study found no differences in physiological markers of intensity between e-cycling and conventional cycling . While tFive studies directly compared the impact of e-cycling in varying topographies  of 10% following 4-weeks of e-cycling compared to a 6% increase following 4-weeks of conventional cycling [2peak following 4-weeks of e-cyling [2peak following 3-months of e-cycling [2peak was reported only in individuals with low levels of fitness, with a 9.6% increase compared to a 1.5% increase in high fitness individuals [2peak in physically inactive men and women respectively [A pilot randomized control trial of physically inactive individuals reported an increase in peak oxygen uptake . Eleven acute and six longitudinal studies were identified. There was moderate evidence that e-cycling provides moderate intensity physical activity in both physically active and inactive individuals. Furthermore, there was moderate evidence that e-cycling positively impacted cardiorespiratory fitness in physically inactive individuals. The impact of e-cycling on health outcomes beyond physical fitness was inconclusive given the sparsity of current research.The quality of all studies, bar one , was weaRegarding longitudinal studies, methods of data collection were consistently strong, but with large variation in representativeness, design and reporting of withdrawals and dropouts. Confounders were considered in the context of differences between groups and were therefore rated as strong if studies used a single-group design. One pilot randomized control trial was conducted and was rated as strong . Overallestimated METs were lower than mean measured METs during e-cycling. Estimated METs have been suggested to overestimate resting energy expenditure, thereby underestimating activity energy expenditure [To accrue health benefits, The American College of Sports Medicine recommend healthy adults engage in moderate-to-vigorous physical activity for 150-min per week . Moderatenditure . As such2peak/max ranging from 51 to 75. These values exceed the hypothesised minimum intensity thresholds required for improvements in cardiorespiratory fitness in healthy adults [Relative physiological outcomes further suggest that e-cycling is performed at a moderate intensity with the percent of maximum heart rate ranging from 67.1 to 79.1 and the percent of VOy adults , 35, 36.Three studies compared e-cycling to walking , 23, 32 2max/peak is similar between conventional cycling and e-cycling ranging from 58 to 74% and 51 to 73% respectively. Studies examining active commuting on conventional bikes have reported similar mean percent of VO2max in healthy adults ranging from 57 to 79% [In relation to conventional cycling, this review suggests that e-cycling elicits lower physiological markers of intensity than conventional cycling, however the strength of this finding depends on the physiological assessment measure and route topography. Overall, mean percent of VO7 to 79% , 37. How2max was 29 ml/min/kg for e-cycling and 37 ml/min/kg for conventional cycling [La Salle and colleagues  reported cycling . This suIn hilly terrain, where there is less opportunity to adjust effort levels to produce comparable intensity levels, the differences between conventional cycling and e-cycling may become more pronounced, with e-cycling requiring lower intensity activity, as found in studies comprised of routes with hilly features , 23. Thi2peak and maximum power output following 4-weeks of e-cycling [2peak following a 6-week intervention, though differences in maximum power output were seen. Differences between studies could be due to distance cycled. Specifically, both Hochsmann [In the current review three studies provided weekly e-cycling goals for physically inactive individuals in the context of active commuting , 29, 30.-cycling , 30. In -cycling  reportedochsmann  reportedochsmann . The twoochsmann , 30 withochsmann . This suWithout the provision of e-cycling goals, single group studies with physically inactive individuals reported increases in maximal power output of 7 to 10% over 3–8 months, despite lower average distance travelled than other studies , 32. FitBeyond cardiorespiratory fitness, there is a lack of research examining the impact of e-cycling on physiological or psychological health outcomes, limiting our ability to draw conclusions. Peterman and colleagues  reportedOverall e-cycling can elicit at least moderate intensity physical activity. However, total energy expenditure when riding an e-bike is lower than when riding a conventional bike or walking over the same distance, given the reduced amount of time taken to complete a ride on an e-bike. Consequently, if e-cycling were to replace journeys made by walking or conventional cycling, individuals would have to ride for longer for comparable weekly energy expenditure. However, e-cycling is associated with lower ratings of perceived exertion than conventional cycling , 26, potFindings reported here suggest that e-cycling may be suitable for individuals with compromised health. Hansen and colleagues  showed tOverall, while there is a trend towards increased fitness following engagement in e-cycling interventions, more intervention research of a longer duration is required before the long-term impact of e-cycling on health can be determined. Fifty percent of the longitudinal studies in this review were approximately 1-month in length. This may not be enough time to see changes in body composition and some metabolic outcomes. Longer trials with larger samples sizes should be conducted with a focus on including a range of health outcomes in addition to cardiorespiratory fitness. These trials should utilize randomized controlled designs and clearly report their target population, recruitment process and dropouts and/or withdrawals. Interventions should also be conducted in clinical populations where physical activity is compromised. In addition, more research is needed to understand the impact of e-cycling on health based on sex or fitness level.It is also important to consider the negative outcomes associated with e-cycling when assessing their potential utilization for health promotion. In the USA, e-bike users reported feeling safer riding their e-bike than a conventional bike, stating that the e-bike helped them to avoid crashes due to their stability, powerful brakes and the acceleration to avoid incidents and keep up with traffic. However, riders reported cycling faster on an e-bike than a conventional bike and felt that other road users misjudged their speed leading to potentially dangerous situations . In the This is the first review to examine the physical activity intensity, cardiorespiratory, metabolic and psychological outcomes associated with e-cycling. This review used two pragmatic tools to assess the quality of studies and to provide an overall rating of the evidence. These tools provided an overall representation of the strength of research evidence related to e-cycling and health. Limitations of this review include the fact that some published studies may not have been identified. However, our systematic and broad search strategy makes this unlikely. It is more likely that we did not identify eligible unpublished studies or those published in an alternative language to English. Sample sizes used in studies were small and sample size calculations were rarely reported. Therefore, caution should be taken when interpreting the statistical significance of evidence. Given the heterogeneity in outcome measurement we were unable to quantify the effects of e-cycling on outcomes of interest using meta-analyses. In addition, focus on quality of life as a psychological outcome may have meant studies examining psychological outcomes such as depression or anxiety were excluded.The composite results of the 17 studies included in this novel systematic review provide moderate evidence that e-cycling elicits activity at an intensity high enough to promote some positive health outcomes. E-cycling leads to reduced activity volume and intensity over the same distance compared to conventional cycling. Therefore, e-cycling requires more frequent and longer rides to accrue comparable health benefits. However, given that most individuals travel by car to work  e-cyclinAdditional file 1:Example search strategy. (DOCX 12 kb)Additional file 2:Description of overall strength of evidence criteria. (DOCX 12 kb)Additional file 3:Outcomes of interest by route topography for acute experimental and quasi-experimental studies. (DOCX 23 kb)Additional file 4:Additional physical activity outcomes measured in acute studies. (DOCX 28 kb)Additional file 5:Additional physical activity outcomes measured in longitudinal studies. (DOCX 22 kb)


Drosophila ovary develops well-defined niches that contain on average 2–4 germline stem cells (GSCs), whose maintenance depends on systemic signals and local factors. A known player in the decline of tissue homeostasis is ageing, which correlates with the waning of resident stem cell populations. In Drosophila, ovaries from old females contain fewer GSCs than those from young flies. We isolated niche cells of aged ovaries, performed a transcriptomic analysis and identified mastermind (mam) as a factor for Drosophila ovarian niche functionality during ageing. We show that mam is upregulated in aged niche cells and that we can induce premature GSC loss by overexpressing mam in otherwise young niche cells. High mam levels in niche cells induce reduced Hedgehog amounts, a decrease in cadherin levels and a likely increase in reactive oxygen species, three scenarios known to provoke GSC loss. Mam is a canonical co-activator of the Notch pathway in many Drosophila tissues. However, we present evidence to support a Notch-independent role for mam in the ovarian germline niche.Proper stem cell activity in tissues ensures the correct balance between proliferation and differentiation, thus allowing tissue homeostasis and repair. The  Stem cells respond to signals of different types and realms of action so that their activity is regulated by environmental factors and by local and systemic signals, including ageing. Indeed, the decline in regenerative capacity characteristic of aged organs is linked to the impaired activity of their hosted stem cells . The facDrosophila have shown that niche cell numbers can decrease with time, which in turn affects the pool of stem cells hosted within [The gradual loss of tissue homeostasis during ageing is a consequence of local modifications in the stem cells themselves and in their supporting microenvironment or niche. Thus, ageing stem cells show changes in the expression of genes involved in the preservation of genomic integrity, and in transcriptional and epigenetic regulation; they can display both declined responsiveness to extrinsic signals and impaired adhesion to support cells or extracellular matrix; and they can increase intracellular concentrations of reactive oxygen species (ROS), accumulate DNA damage and show slower proliferation and deficient self-renewing divisions –8. Simild within . In addid within ,6. ThereDrosophila ovary is composed of functional egg-producing tubes called ovarioles. Each ovariole contains tissue-resident stem cells homed in an experimentally accessible niche that is susceptible to genetic manipulations and microscopic analysis. The coordinated activity of the stem cell populations in the niche fuels the generation of new egg gametes during oogenesis. Located in the germarium, a tapered structure found at the anterior tip of the ovariole, this niche is composed of extracellular matrix and a few mesodermal cell types that offer support to 2–4 germline stem cells (GSCs) and to a larger population of somatic stem cells termed follicle stem cells (FSCs) [a). Both CpCs and the anterior ECs are in contact with GSCs and are necessary to maintain the pool of undifferentiated GSCs through adhesion and signalling events [Drosophila conveys a significant reduction in the number of GSCs hosted in the niche [a–c).The s (FSCs) ,12. The g events –16. GSCshe niche ,18 (figuDrosophila and found a discrete number of candidate genes whose transcription was regulated with age. We focused our efforts on the mastermind (mam) gene, a known cofactor involved in the canonical transduction of the Notch signal and in the transcription of target genes [Notch pathway is required for the architecture of the GSC niche, where it controls the number of CpCs and thus niche size, and for the maintenance of the correct number of GSCs during adulthood [mam participates in ovarian niche ageing independent of the Notch pathway.To try to identify new genes required in niche cells and involved in GSC depletion with ageing, we embarked upon a transcriptomic analysis of maturing niche support cells in et genes . Mam fordulthood ,20–25. W2.2.1.Drosophila females contain germaria with fewer GSCs than controls [b,c). Next, we used fluorescence-activated cell sorting (FACS) to isolate green fluorescent protein (GFP)-labelled TFCs and CpCs from one-, three- and four-week-old females and performed a transcriptomic analysis using DNA microarrays (approx. 400 cells/array). We identified 30 upregulated and 32 downregulated genes in four-week-old females compared with one-week-old controls . Among the upregulated genes, mastermind (mam) was expressed 3.58- and 2.47-fold higher in three- and four-week-old TFCs and CpCs, respectively, than in one-week-old controls. This was further confirmed by measuring directly mam RNA levels corresponding to the long and short isoforms using digital polymerase chain reaction (PCR) in isolated one-, three- and four-week-old niche cells (a). Considering that mam was the only gene upregulated in the three-week-old sample and that it maintained a higher expression in older cells, we decided to analyse in deeper detail the role of mam during ovarian niche ageing.In order to identify genes involved in the ageing of stem cell microenvironments, we first confirmed that ovaries from aged, mated controls ,17,18. Ihe cells a. Consid2.2.mam in aged niches, we first looked at mam expression, making use of an enhancer of the mam gene . Second, and considering that an increase of mam levels in niche cells correlated with a decrease in GSC numbers, we specifically knocked down mam mRNA amounts in adult niche cells to check if lower mam levels could prevent the drop in GSC numbers typical of ageing niches. We used the bab1-Gal4 driver combined with the tsGal-80 to overexpress after eclosion an RNA interference (RNAi) version of the mam gene. bab1-Gal4 is expressed at high levels in adult TFCs and CpCs; it is also expressed, albeit weakly, in ECs [n) = 38; bab1>mam RNAi, 2.23 GSCs/germarium, n = 30) and of Hedgehog and DE-cadherin levels (see below), raising the flies at 25°C induced GSC loss . We interpret this result as indicating that a certain activity of mam is required for GSC maintenance .To validate a possible role for MR28A08; ) to driv, in ECs . We raismam levels could mimic niche ageing and thus GSC loss, we used a similar approach to overexpress a full-length version of the mam gene  in the somatic cells of the adult niche (hereafter referred to as the bab1>mam condition). Overexpressing mam for one or two weeks at consistent levels in the ovarian niche of freshly eclosed females gradually induced a significant GSC loss . In addition, we also observed that the proportion of niches containing one or zero GSCs rose from 15% to 30% in females overexpressing mam for two weeks . These changes in GSC behaviour were not a consequence of modifying CpC numbers or fate, as determined by Lamin-C staining of control and experimental niches .Third, to determine if the mere increase of l niches c,d.mam gene encodes two different protein products, MamLong and MamShort. The latter lacks some C-terminal sequences essential for Notch signalling and it is known to act as a dominant-negative version that interferes with MamLong and that represses the Notch pathway in certain cell types [mam isoforms (a), we wished to check whether mamShort affected the GSC niche. We thus overexpressed mamShort for one week and found that GSC numbers did not change compared with controls . This result suggests that mamShort does not have a critical function in this context. Finally, we determined that one-week overexpression of full-length mam in somatic cells of the ovary with the traffic jam-Gal4 driver did not affect mamShort levels , strongly suggesting that inducing high levels of mamLong in the ovary represents a true mam gain-of-function situation.The ll types . Since aisoforms a, we wisbab1-Gal4 is expressed at high levels in adult TFCs and CpCs and in ECs [mam overexpression in TFCs/CpCs or ECs would provoke GSC depletion. To this end, we overexpressed mam for two weeks with the hedgehog-Gal4 line, expressed in adult TFCs and CpCs. Although the tendency was to find fewer GSCs in these experimental niches, there was no significant GSC loss compared with controls . Next, we drove full-length mam expression in adult ECs for two weeks using the patched-Gal4 line and found that the number of GSCs present in experimental niches, even though significantly lower in ptc>mam , was not as reduced as in bab1>mam. These data thus suggest that high levels of mam are necessary in TFCs/CpCs and ECs to account for the total GSC loss typical of aged niches. In all, from the above results, we conclude that mam levels increase with time in the niche, and that this increase is sufficient to induce GSC loss.Because d in ECs , we wish2.3.mam levels. We dismissed cell death as one-week-old bab1>mam germaria did not increase expression of the Dcp-1 caspase in GSCs . Considering the role of GSC–niche cell adhesion in stem cell maintenance [Drosophila males and females, and that this is due to a decrease in cadherin levels [mam-overexpressing niches could be due to a reduction in cadherin levels, we studied the distribution of DE-cadherin in CpC-CpC and CpC-GSC interfaces of aged and bab1>mam niches. As a reporter for DE-cadherin expression, we used a DE-cadherin::GFP fusion protein under the control of the endogenous regulatory sequences of the gene [DE-cadherin::GFP levels, with aged niches containing on average DE-cadherin levels of 74% of the controls . We then examined the effect on DE-cadherin::GFP levels of overexpressing mam for one or two weeks in experimental germaria and found that, while one-week-old overexpressing germaria behaved like controls, DE-cadherin::GFP levels were significantly reduced after two weeks of overexpression . To further confirm this result, we measured endogenous, untagged DE-cadherin protein levels by immunostaining and found that they were reduced by nearly 50% after two weeks of mam overexpression . Together, the above data demonstrate that mam overexpression in young niches recapitulates the adherens junction defects typical of older niches. They also suggest that high levels of mam are sufficient to induce premature niche ageing and the concomitant loss of GSCs by impairing cadherin levels and thus niche cell–GSC adhesion. Finally, to test if the reduction in DE-cadherin levels upon ectopic expression of mam is functionally relevant, we checked whether DE-cadherin overexpression could rescue the GSC loss characteristic of bab1>mam germaria. We found that a two-week induction of high DE-cadherin amounts in bab1>mam niche cells did not rescue the decrease in GSC contents characteristic of high mam levels .Next, we sought to determine the reason(s) for GSC loss upon induction of high ntenance , we decin levels ,6. To tethe gene . When ancontrols a,b. We t2.4.mam induced a precocious decrease in DE-cadherin levels in niche cells, we determined if membrane DE-cadherin turnover was also affected by age and/or mam overexpression. We set out to measure the dynamics of protein replacement in CpC cytoplasmic membranes by performing FRAP (fluorescence recovery after photobleaching) experiments to look at the recovery of DE-cadherin::GFP signal in one-week-old versus four-week-old females . In young samples from DE-cadherin::GFP females, bleached CpC membranes reached a maximum of fluorescence recovery close to 30% of the pre-bleached signal 30 min after photobleaching, which was in the same range as previously published data involving DE-cadherin::GFP FRAP analyses in embryos [DE-cadherin . Next, we analysed the membrane-bound fusion protein Resil::GFP as a turnover control, and noticed that one-week-old samples recovered 40% of the pre-bleached signal 30 min after photobleaching, while four-week-old TFCs and CpCs recovered around 20% . Thus, the mere ageing of niche cells can affect membrane protein replacement dynamics. It remains to be determined if the small difference in fluorescence recovery observed on DE-cadherin::GFP trafficking in aged niches compared with that on Resil::GFP implies that ageing impairs DE-cadherin recycling to a larger extent than other membrane proteins.Once we had confirmed that the overexpression of  embryos . By contcadherin a,b. Nextound 20% c,d. ThusDE-cadherin recycling from internalized endosomes [figure 4e,f; electronic supplementary material, figure S5C,D).The Rab11 GTPase has been implicated in ndosomes . Nuclearndosomes . In ordemam levels in DE-cadherin recycling, we first performed FRAP on DE-cadherin::GFP TFCs and CpCs overexpressing mam for one week but could not detect any differences in fluorescence recovery with control flies , indicating that DE-cadherin::GFP dynamics are not changed in one-week-old bab1>mam flies. This result is in line with our previous observation that ovarian niches in these experimental females show normal levels of DE-cadherin::GFP. Unfortunately , overexpression of mam for two weeks reduces DE-cadherin::GFP levels to the extent that we could not perform FRAP analyses reliably. Second, we observed that after two weeks of mam overexpression Rab11 levels were decreased, suggesting that high mam quantities affected membrane protein recycling. However, we failed to detect changes in Nuf amounts upon mam overexpression for two weeks . In all, our results demonstrate that physiologically aged niche cells recycle membrane proteins (such as DE-cadherin or Resil) less efficiently than younger ones. Moreover, considering the reduction in DE-cadherin, Rab11 and Nuf levels in aged niches (and of DE-cadherin and Rab11 in high mam conditions), our results may suggest that ageing favours the sorting of internalized DE-cadherin (and probably many other membrane proteins such as Resil) towards the lysosomal degradation pathway rather than towards the Rab11-mediated recycling [mam overexpression induces a similar scenario.To address the role of high ecycling ,35, and 2.5.bab1>mam germaria is not due to apoptosis. In addition, in the course of our studies, we found germline cells in contact with bab1>mam CpCs that possessed spectrosomes typical of CBs or fusomes characteristic of two- and 4-cell cysts—a situation never encountered in controls and indicative of GSC loss by differentiation. Thus, because mam overexpression did not affect niche cell numbers , the decrease of GSC numbers in ovarian niches expressing high levels of mam could be due to flawed niche signalling that, in turn, induced GSC differentiation. In this context, it has been shown that aged niches are less effective in transducing the BMP pathway or in keeping constant levels of the Hedgehog (Hh) protein, two events that act normally in the niche to maintain a proliferative GSC pool [mam overexpression had an effect on the signalling properties of the GSC niche. First, we quantified the activity of the BMP pathway by scoring the intensity of the BMP activity reporter phosphorylated Mothers against dpp (pMad) in aged GSCs containing normal-looking spectrosomes. In contrast to published results [bab1>mam niches, which did not show a decrease in pMad levels even after a two-week exposure to high mam levels . Second, we looked at the Hedgehog pathway. The Hh ligand is expressed in TFCs and CpCs, where it acts to maintain the GSC pool in adult ovaries [a,b). This finding is in agreement with a previous report showing that aged TFCs/CpCs exhibited lower activation of a hh-lacZ enhancer trap [mam  also displayed significantly lower levels of Hh amounts . To test if ageing and/or mam overexpression affected not only Hh levels in CpCs but also Hh signalling in the target cells, we used an antibody to Patched that gives a clean, specific staining in the wing disc and we scored Patched amounts in the anterior half of the germarium. The Patched (Ptc) receptor is a known target of Hh signalling and its transcription is a direct readout of the pathway [ptc-lacZ construct [hh-pathway signalling, Ptc-pelican-GFP [bab1>mam germaria kept for two weeks at 29°C contained Ptc levels similar to their respective controls (e–g).Our previous results indicate that GSC loss in  numbers c,d, the GSC pool ,18,36,37 ovaries ,36. Inte amounts c,d. To t pathway ,39. In construct . CpCs diican-GFP . Howevercontrols e–g.Figumam overexpression can induce a decrease in Hh levels in niche cells. Surprisingly, we also found that the activity of the pathway—at least as indicated by Ptc levels—seemed to be maintained in the above conditions, suggesting that other modulators apart from the Hh ligand may act during its signalling in the germarium. One such modulator reported to act in other tissues such as imaginal discs is dachshund [mam levels and found that it did not .From the above experiments, we conclude that both ageing and achshund ; howeverachshund , could r2.6.mam levels in the niche could induce premature ROS-dependent GSC loss, we tested whether reducing oxidative species in bab1>mam niches could improve GSC maintenance and its effect on Hh and DE-cad amounts. To this end, we increased the amounts of the well-known antioxidant enzyme superoxide dismutase (SOD1) in the cytoplasm of niche cells and determined that SOD overexpression rescued the GSC loss characteristic of bab>mam niches . We also confirmed that Hh and DE-cad levels in experimental flies were similar to those in controls . Since overexpression of SOD1 on its own in niche cells does not affect GSC numbers , we thus propose that mam overexpression in niche cells increases ROS levels and that this contributes to the reduction in GSC numbers found in bab1>mam niches.ROS have been suggested to cause ageing, and previous results indicate that reduction of ROS in either GSCs or their niche improves GSC proliferation and lifespan, pointing towards an age-dependent increase in ROS levels in niches and stem cells . In orde2.7.Drosophila) and the Notch intracellular domain, NICD or Nintra [Drosophila by increasing the dwell time of Su(H) complexes on target promoters [bab1>mam RNAi females grown at 25°C contained fewer GSCs than their controls . However, we have found that mam levels augment at least during the first four weeks in aged niche cells and that an increase in mam mRNA in somatic niche cells induces GSC loss, results that are seemingly contradictory to Mam's role as a cofactor of the Notch pathway. Hence, we set out to investigate whether the observed higher mam levels in three- and four-week-old niche cells may act Notch-independently. First, we confirmed that loss of function of the Notch pathway (by reducing the amount of the Delta ligand in niche cells) reduced GSC numbers , corroborating that adult germaria depended on Notch signalling in niche cells for GSC maintenance [Nintra in niche cells) did not induce GSC loss, a finding in sharp contrast to the decline in GSC numbers typical of mam overexpression . To prove that the Notch pathway was activated in bab1>Nintra, we determined the number of CpCs present in control and experimental niches. It has been previously shown that ectopic activation of the Notch pathway in niche cells induces extra CpCs [Nintra overexpression nearly duplicated the number of CpCs present in experimental germaria (bab1>Nintra). By contrast, ectopic expression of mam for two weeks (bab1>mam) did not affect CpC numbers . Thus, the fact that increasing mam mRNA levels in niche cells induced GSC loss  and the observation that overexpression of Nintra augmented CpC numbers  led us to conclude that the induction of high mam mRNA levels during niche ageing affects the GSC pool in a Notch-independent pathway and that high Mam levels in aged niche cells might interact with partners different from the CSL/NICD complex Mam is a transcriptional co-activator that supports the activity of the Notch receptor by forming a ternary complex with the DNA-binding protein CSL (Su(H) in r Nintra ,43. Thusromoters . Interesromoters ,21, suggntenance ,21. Secotra CpCs . In our 3.3.1.mam as a novel factor involved in the ageing of a particular stem cell microenvironment, the ovarian niche of Drosophila melanogaster.The ageing of tissues correlates with a decline in regenerative capacity and in homeostasis, linking the activity of tissue-specific stem cells to age-related organ dysfunction. Several factors had been reported to cause stem cell waning during ageing, mainly increased ROS production, decreased signalling efficiency and compromised adhesion to niche support cells, all of which can jeopardize stem cell maintenance within the niche. These ‘ageing' factors can be intrinsic to the stem cells, or affect their surrounding microenvironments, which also contribute to impaired tissue activity in aged organisms. We have identified mam is a good example of this, as it is the only gene upregulated in three-week-old samples whereas in four-week-old niches there are 30+ upregulated genes), and that all but one candidate maintain their differential expression one week later, suggests that ageing is a gradual process in which few genes implicated in seemingly unrelated biological processes contribute to the progressive loss of stem cells from the niche.Our transcriptomic analysis reveals a discrete number of genes whose expression is either upregulated or downregulated in niche support cells aged for three or four weeks, which represents around the half-life of a laboratory fly. The fact that the identified genes in the three-week-old samples are only a small fraction of those in four-week-old cells ; and it most likely increases ROS amounts in niche support cells. The link between these important events in stem cell maintenance is currently missing, as it relates to why higher mam levels would induce oxidative stress most likely by increasing ROS quantities. We can only speculate that overexpression of mam in otherwise young niches might induce an accumulation of ROS-dependent damage in the cell, which ultimately results in impaired signalling, recycling and adhesion within the niche. Perhaps high mam amounts accelerate the cell's metabolism, thus increasing the generation of free radicals to the extent that the cell cannot cope with them efficiently. Alternatively, or in addition, mam overexpression might impede the normal activity of the antioxidant defence system present in niche cells. Whatever the mechanism, this places the physiological increase in mam levels in a preeminent position in the ageing of the ovarian niche, at least during the first half of adult life. Furthermore, the fact that ‘young' stem cells can be lost from artificially aged niches indicates that support niche cells are fundamental to regulating stem cell waning from old niches and that the physiological condition of the microenvironment may determine the maintenance of stem cells independently of stem cell age. This observation emphasizes the constraints that niche physiology poses on stem cell-based therapies.3.2.Drosophila, Su(H) acts as a Notch-independent activator to allow proper bristle formation in the adult notum [Drosophila, several studies have also reported a non-canonical role for Mam in different systems such as the central nervous system, where Notch and mam mutants display distinct phenotypes [mam may provide a link between mam, Wnt signalling and ROS in the ovarian niche. Finally, a previous report demonstrated that Mam does not act in the conventional Notch-transduction pathway in the response of FSCs to the Hedgehog ligand in the Drosophila ovary [Long activity in FSCs, where mam acts in a Notch-independent fashion. In our work, we demonstrate that high mam levels and the overactivation of the Notch pathway have distinct outcomes, supporting our claim that the naturally occurring upregulation of mam mRNA levels acts independently of Notch signalling. Furthermore, while the nature of the partners in niche cells containing elevated Mam amounts is currently unknown, they are most likely not the canonical effectors of the Notch pathway Su(H) and Nintra, suggesting that full-length Mam is able to interact with other partners to regulate GSC numbers during ageing. We envision a situation in which increased levels of the Mam co-activator could be acting in the niche to integrate multiple signalling pathways that ultimately lead to niche ageing. The consequences of this regulatory cross-talk are the reduction in niche cell–stem cell adhesion and niche signals such as Hedgehog and the increase in oxidative stress characteristic of aged niche cells.The Notch signalling pathway operates in a myriad of evolutionarily conserved developmental and disease processes. It ensures the release of NICD upon ligand binding. Partnered with the DNA-binding protein CSL and the co-activator Mam, the NICD/CSL/Mam complex regulates expression of target genes. Modulation of this process involves several factors, including ligand and receptor expression and levels, regulation of the processing steps required to release NICD and crosstalk with other signalling pathways ,46. Therlt notum . As for lt notum –53. In tlt notum . In the enotypes , or the enotypes . Interesenotypes , mam mayla ovary . These a3.3.mam overexpression reduce DE-cadherin amounts, we report that the decreased DE-cadherin levels in aged niches could be due to a less effective trafficking and/or to a differential balance between recycling and endocycling for destruction via the lysosomal compartment. This idea is supported both by the FRAP experiments—which indicate an age-dependent decline in recycling capacities of the CpCs—and by the decrease in Rab11  and in Nuf contents in aged cells, which could be indicative of unbalanced DE-cadherin recycling/degradation. Together with the finding that maturing niches present a reduced capacity to replace the fusion protein Resil::GFP, our results thus point towards a general decay in membrane protein turnover with age, hence implicating ageing in the correct trafficking of membrane-associated proteins. Whether this effect is of general importance and whether it is due to the increasing levels of oxidative stress in old niche cells remains to be seen, but the fact that protein replacement dynamics are related to ageing and that DE-cadherin recycling seems more sensitive to ageing than a control protein is of particular interest to the field, in view of the importance of stem cell–niche cell adhesion for tissue homeostasis [A landmark of aged ovarian niches is a characteristic reduction in the cadherin-mediated adhesion between stem cells and the supporting CpCs. In addition to confirming that ageing and eostasis ,60.4.The following is a short version of the material and methods used in this study. A complete account can be found in the electronic supplementary material.4.1.GSCs were identified by their position within the germarium (they are located just underneath the CpCs), by their size (they are larger that differentiating germline cells) and by the presence of a spectrosome in their cytoplasm. GSC spectrosomes adopt different morphologies depending on the cell-cycle phase. The number of GSCs found in a niche shows some variability, even in flies of the same genotype and raised in the same vial, and it ranges from 2 to 3 GSCs/germarium in the different control females used in this study. Because of this variability, in all of the experiments where we scored GSC numbers, we analysed control flies in parallel with their experimental siblings. To normalize the changes observed, we show the variation in the percentage of GSCs/germarium in all pertinent graphs. Thus, comparisons should be made between control and experimental niches of the same experiment and not between genotypes of different treatments and/or conditions. All quantifications were obtained from at least five different females/replicate of a given phenotype and from a minimum of two biological replicates.ts, ptc-Gal4bab1-Gal4, tub- G80 or hh-Gal4 constructs and determined that they did not have a significant effect on the GSC pool present in the niches.We also performed a number of controls to make sure that the chromosomes used in this study did not affect GSC numbers specifically. Thus, we quantified GSCs in females bearing the TM6B balancer or chromosomes carrying the 4.2.mam-Gal4 , hh-Gal4 [ptc-Gal4 , DE-cadherin::GFP , UAS-mam , UAS-mamShort [, UAS-Nintra , UAS-SOD1 , tstub-Gal80 , UAS-mam RNAi , UAS-Delta RNAi  and UAS-Dicer . Other stocks used were: Resille::GFP [UAS-DE-cadherin [bab1-Gal4 [traffic-jam-Gal4 [tstub-Gal80 system. Placing the flies at 29°C would induce Gal4-mediated gene expression, whereas keeping the adults at 18°C blocks Gal4-induced gene expression.Stocks obtained from public repositories were:  hh-Gal4  , 1 : 100; rabbit anti-phospho-Mad , 1 : 1000; rabbit anti-cleaved Dcp-1 , 1 : 100; guinea pig anti-Nuf [−1 in H2O).Primary antibodies used were the following: mouse anti-Hts (1B1) , 1 : 100; rabbit anti-Vasa (a gift from R. Lehmann), 1 : 3000; goat anti-GFP, FITC conjugated , 1 : 500; alpaca anti-GFP-Booster_Atto488 , 1 : 200; mouse anti–Lamin C , 1 : 30; rat anti-DE-cadherin , 1 : 50; guinea pig anti-Hh  produced in 4.4.Confocal images of control and experimental samples processed in parallel were obtained with exactly the same settings. Average fluorescence intensity was quantified with ImageJ. See electronic supplementary material and  for furt4.5.Z-stacks were then collected at 10 min time intervals.Regions of interest per niche were bleached with the 488 nm and 405 nm laser lines at 100% power for three 2.6 s long scans (400 Hz). Three 4.6.Drosophila Genome 2.0 Arrays from Affymetrix.Preparation of single-cell suspensions was done by optimizing a previous protocol . Niche c4.7.mam long and short isoforms were determined by real-time quantitative PCR  using the comparative cycle threshold (CT) method [mam long and short isoforms by Droplet Digital PCR (ddPCR).The relative mRNA amounts of ) method . Pico-pr4.8.t-tests. In all figures, only p-values of two-tailed t-tests considered statistically significant (p ≤ 0.05) are indicated. Thus, the absence of asterisks indicates no statistical significance between control and experimental values.Samples were collected from at least five different adult females. Graphs represent the mean values and standard errors. Significant differences were determined using two-tailed Student's


Substantial improvements in survival have been seen in multiple myeloma (MM) over recent years, associated with the introduction and widespread use of multiple novel agents and regimens, as well as the emerging treatment paradigm of continuous or long-term therapy. However, these therapies and approaches may have limitations in the community setting, associated with toxicity burden, patient burden, and other factors including cost. Consequently, despite improvements in efficacy in the rigorously controlled clinical trials setting, the same results are not always achieved in real-world practice. Furthermore, the large number of different treatment options and regimens under investigation in various MM settings precludes the feasibility of obtaining head-to-head clinical trial data, and there is a temptation to use cross-trial comparisons to evaluate data across regimens. However, multiple aspects, including patient-related, disease-related, and treatment-related factors, can influence clinical trial outcomes and lead to differences between studies that may confound direct comparisons between data. In this review, we explore the various factors requiring attention when evaluating clinical trial data across available agents/regimens, as well as other considerations that may impact the translation of these findings into everyday MM management. We also investigate discrepancies between clinical trial efficacy and real-world effectiveness through a literature review of non-clinical trial data in relapsed/refractory MM on novel agent−based regimens and evaluate these data in the context of phase 3 trial results for recently approved and commonly used regimens. We thereby demonstrate the complexity of interpreting data across clinical studies in MM, as well as between clinical studies and routine-care analyses, with the aim to help clinicians consider all the necessary issues when tailoring individual patients’ treatment approaches. These improvements are associated with the introduction and widespread use of multiple novel agents and regimens4, as well as with the emerging treatment paradigm of continuous therapy or long-term maintenance therapy8, an approach that has been shown in clinical trials to offer prolonged survival versus fixed-duration or shorter-term therapeutic approaches9.Substantial improvements in overall survival (OS) have been seen in multiple myeloma (MM) over recent years11. Strict patient selection criteria in clinical trials is another important aspect and is the subject of a recent American Society of Clinical Oncology initiative to broaden clinical trial eligibility criteria to be more representative of patient populations12. Determination of the appropriate regimen for MM patients in clinical practice requires individualized assessment of various patient-related, disease-related, and treatment-related characteristics. The gap between efficacy and effectiveness is also associated with toxicity and comorbidity burden, patient and physician motivation, different distributions of academic versus community centers at which patients receive their treatment, strict protocol-enforced surveillance, treatment access issues, and other determinants contributing to premature discontinuation of treatment regimens outside of clinical studies.However, there is the potential for discrepancies between clinical trial efficacy, for which an intervention is studied under ideal circumstances, and real-world effectiveness, which reflects the true benefit of an intervention to patients in routine practice. This is associated with a number of factors and confounds translation of clinical trial data into the real world. For example, continuous or long-term therapy may have limitations in routine clinical practice, associated with toxicity burden, patient burden, and other factors such as cost13, using hazard ratios (HRs) may prove more valid in this context. Nonetheless, while rigorously conducted network meta-analyses are useful to inform treatment decisions, known limitations, including publication bias, study heterogeneity, and differences in study populations and median follow-up across clinical trials, may still lead to biased estimates of the comparative treatment effects14.A related issue is that relative efficacy in the clinical trial setting represents a key consideration for regimen selection. There is a temptation to use cross-trial comparisons to compare data across regimens; however, many factors can influence clinical trial outcomes, and there may be differences between studies that may confound indirect comparisons. Indirect comparisons of relative efficacy across homogeneous study populations, such as the network meta-analysis approach recently utilized to compare PFS across multiple treatment options for RRMMThis review explores the various factors that physicians need to consider when evaluating clinical trial data across agents and regimens, focusing on studies in relapsed/refractory MM (RRMM), and addresses considerations regarding how these findings will translate into everyday MM management.16 and elotuzumab17), proteasome inhibitors , and the histone deacetylase inhibitor panobinostat22. Real-world data in this setting are emerging for carfilzomib and ixazomib26, but such data are unfortunately limited for monoclonal antibodies28 and panobinostat to date30.Regulatory approvals based upon the results from randomized, phase 3 clinical trials remain the gold standard. Table 32. Differences in eligibility criteria across clinical trials can result in imbalances between trial populations that can affect outcomes, as exemplified by the range of outcomes seen with the Rd ( ± placebo) comparator arms in phase 3 trials in RRMM  compared with trial-eligible patients 33. Broadening eligibility criteria to increase the generalizability of clinical trial results is a recognized need towards more informed treatment decision-making34.Similarly, real-world populations may have substantially different patient/disease characteristics compared with clinical trial cohorts, and thus, clinical trial outcomes may not reflect real-world experience. In a recent analysis of the CONNECT-MM registry, it was reported that up to 40% of patients treated in routine care would be ineligible for enrollment to randomized controlled trials in newly diagnosed MM due to common stringent eligibility criteriaThere are a range of prognostic patient and disease characteristics that may vary between clinical trial populations and between clinical trial and real-world cohorts, potentially driving differences in outcomes. These are reviewed below.35, clinical presentation may differ between regions. For example, Chinese MM patients have much more advanced-stage disease at diagnosis compared with Western MM patients36. Furthermore, regional variation in therapies that patients are exposed to prior to and after trial interventions may also impact outcomes in intention-to-treat analyses of protocol therapy. For example, the use of melphalan–prednisone-based regimens is common in Europe38 but rare in North America, where the majority receive Rd in the non-transplant setting4. Long-term outcomes such as OS will also depend on the number of available subsequent active treatment options in a particular country or region. Given these differences, real-world outcomes with a specific regimen in a particular region may not reflect outcomes from clinical trials conducted in a geographically broader or different patient population. These aspects highlight the importance of considering the regional composition of patient populations when evaluating different clinical trial or real-world data.Geographic differences in patient populations and in treatment availability may affect outcomes. In addition to the potential for drug metabolism/pharmacology to be affected by ethnic differences39. The presence and extent of comorbidities are also prognostically important43, with greater comorbidities and poorer performance status resulting in worse responses to treatment; however, these are rarely reported in trials. Comorbidities such as diabetes (21.5% vs. 16.3%), cardiovascular disease , and some of the CRAB criteria for MM–hypercalcemia (11.0% vs. 5.5%), renal impairment (38.9% vs. 6.2%), and anemia (59.5% vs. 39.5%)–have been shown to be more common in clinical trial-ineligible versus clinical trial-eligible patients in the CONNECT-MM registry, further contributing to the gap between efficacy and effectiveness33.Patient age is a key predictive parameter in MM, with median OS decreasing from 6.4 years in patients aged < 50 years to 2.5 years in patients aged ≥ 80 years in one analysis of OS by age cohort20 to avoid imbalances between study arms, as it has important prognostic implications. However, the relative proportions of patients with stage I, II, and III disease may vary substantially across clinical studies and may be reported at different time points, i.e., either at initial diagnosis or at study entry. For example, in the CASTOR phase 3 study of daratumumab plus bortezomib-dexamethasone in RRMM, 39%, 37.5%, and 23.5% of patients on the investigational arm had stage I, II, and III disease at screening, respectively16, whereas in the TOURMALINE-MM1 phase 3 study of ixazomib-lenalidomide-dexamethasone in RRMM the respective proportions were 64%, 24%, and 12%20. In contrast, disease staging in the ASPIRE phase 3 study of carfilzomib-lenalidomide-dexamethasone in RRMM was reported from initial MM diagnosis, at which time 17.4%, 24.4%, and 43.7% of patients had stage I, II, and III disease, respectively, with 14.5% unknown19.International Staging System (ISS) stage is widely reported in MM clinical trials and frequently used as a stratification factor33. Such a discrepancy may result in poorer outcomes among real-world versus clinical trial populations.Similarly, the relative proportions may vary substantially between clinical studies and real-world patient populations; in the analysis of patients in the CONNECT-MM registry, 29%/27%/22% of clinical trial-eligible patients had stage I/II/III disease, compared to 13%/22%/40% of patients ineligible for clinical trials, suggesting that real-world populations may include a greater proportion of patients with advanced-stage disease32. Although it is suggested that the Revised-ISS is a more powerful prognostic system than the original ISS classification system, it has not been substantially implemented to date in MM; therefore, these data are not currently widely reported in clinical trials or in real-world analyses, adding another source of potential unobserved confounding.More recently, the ISS has been updated to incorporate the presence of cytogenetic abnormalities and lactate dehydrogenase levels as additional prognostic factors44, which may have a variable prognosis44, whereas others have disease that is evaluable according to free light chain (FLC) levels/ratio only. Furthermore, the variability in FLC levels/ratio in patients with non-secretory MM45 and differences in FLC/light chain development during follow-up in non-secretory patients46 may result in challenges for interpretation of response and progression. Serum FLC ratio is prognostic for PFS and OS in MM, but discrepancies have been reported in FLC-based relapse versus conventional relapse, with serum FLC escape occurring a median of 3.8 months earlier than conventional relapse in 20% of patients in one retrospective investigation47. Thus, there is a potential impact on the interpretation of overall outcomes between studies that include or exclude FLC-only evaluable patients.A small proportion ( < 3%) of MM patients have non-secretory disease48. The exclusion criterion for creatinine clearance is commonly defined in clinical trial eligibility criteria as < 30 mL/min but has also been defined as < 20, < 30, or < 50 mL/min in phase 3 trials in RRMM;20 reported rates of renal impairment in phase 3 trials include 7–20% of patients with creatinine clearance < 50 mL/min19 and 24–27% with creatinine clearance < 60 mL/min20. Such differences in eligibility criteria and patient populations can impact outcomes and the translation of findings to the real-world setting.Renal impairment is a known adverse prognostic indicator and a frequently applied exclusion criterion in clinical trials; however, as many as 59% of RRMM patients treated in routine care have a history of renal impairment  at the time they initiate salvage therapy49. Not all of the abnormalities now recognized as conferring poor prognosis are routinely collected or reported in clinical trials or observational studies, including deletion 17 [del(17)], the translocations t, and t20, 1q amplification50, and 1p deletion51. The proportion of patients with cytogenetics reported is often low in clinical trials , leading to a high level of missing information, although this proportion was higher in the TOURMALINE-MM1 (76%)20, CASTOR (71%)16, and POLLUX (77%)15 trials. Similarly, these data appear to be limited in analyses of routine practice, with conventional cytogenetic and fluorescence in situ hybridization (FISH) analysis being reported in only 37–63.2% of patients in recent real-world analyses;54 a contributory factor may be the limited availability of these analytical techniques, notably FISH, at different sites around the world. This raises the possibility of ‘hidden’ imbalances in prognostic markers across these studies, making indirect comparisons even more difficult. Additionally, there is no consistent methodology (and therefore sensitivity) or cut-off to define the presence of a high-risk abnormality; for example, presence of del(17p) has variously been defined in recent clinical studies based on abnormalities in a single cell17, through 1.5–7.5% of cells20, up to 60% of cells19. Yet, different clone sizes may have different prognostic impact, and thus patient populations appearing to have the same rate of a specific cytogenetic abnormality may have different outcomes.There is substantial genetic complexity in MM, and multiple primary and secondary cytogenetic abnormalities can potentially impact outcomes55 and number and type of previous lines of therapy56 are known determinants of response to subsequent therapy; however, these may be confounded by the use of different definitions for determining ‘refractory’ disease and classifying a line of therapy between clinical trials and compared with the real-world setting. Patients typically have poorer outcomes with successive lines of therapy, including shorter disease-free intervals or periods of disease control, both in clinical trials and in the real-world setting57, associated with increasing disease refractoriness and accumulating frailty/comorbidities. Standard clinical trial eligibility criteria typically exclude more heavily pretreated patients38. Additionally, type of prior therapy can impact response to treatment. For example, in North America, many patients receive Rd4 initially and therefore may develop lenalidomide-refractory disease. However, phase 3 clinical trials of regimens consisting of an Rd backbone have often excluded lenalidomide-refractory patients20, making it difficult to translate the efficacy of the studied regimen to effectiveness in a real-world patient population.Prior therapy exposure can markedly affect treatment outcomes. Refractoriness to previous therapy58, leading to possible differential withdrawal rates associated with more patients on the control arm withdrawing from the study for reasons other than disease progression and potentially receiving a subsequent therapy without having disease progression. These issues may be overcome through the use of central blinded review58. Similarly, there is the potential for bias in quality of life (QoL) and patient-reported outcome endpoints in unblinded studies, associated with a more ‘upbeat’ evaluation of their status by patients enrolled to receive the novel, ‘exciting’ treatment option59.An important consideration in the interpretation of clinical trial results is the study design, specifically whether an open-label or placebo-controlled double-blind design was used. An unblinded randomized trial design may introduce bias when using PFS as an endpoint61 however, older studies are more likely to have used the European Group for Blood and Marrow Transplantation (EBMT) criteria62 or the original IMWG uniform response criteria63. Therefore, PFS may be affected by the stringency of the criteria used for determining relapse from CR; this so-called ‘CR penalty’ when using the EBMT criteria64 arises because relapse from CR is defined as M-protein reappearance on immunofixation62, whereas for PFS evaluation, the IMWG criteria require an absolute increase in serum or urine M-protein to define relapse from CR63. Furthermore, response and progression assessment may differ in the real-world setting, with potentially less rigorous criteria used to determine progression and to evaluate response than in a clinical trial. For example, bone marrow confirmation and flow cytometry/PCR assessment of minimal residual disease (MRD) may not routinely be conducted or repeated for cases of suspected CR/stringent CR. Additionally, depending on the rigor of response assessment and extent of available documentation, all subcategories of depth of response may not feasibly be reported in the real-world setting.Discrepancies occur between clinical trials with regards to criteria used for response assessment and schedule of assessments, potentially impacting the interpretation of response and/or progression data. The International Myeloma Working Group (IMWG) has recently published updated consensus criteria for assessment of response and progression;65, there are examples of clinical trial data in which the response rates do not correlate with PFS and/or OS69. These discordances may arise for a number of reasons, such as the effects of toxicity  or more aggressive relapse, and should be considered when interpreting clinical trial data. In a phase 3 study of lenalidomide plus high-dose versus low-dose dexamethasone in NDMM66, the response rate was higher (79% vs. 68%) in the high-dose dexamethasone arm, but due to toxicities associated with the higher dose of dexamethasone, the 1-year OS rate was lower (87% vs. 96%). Similarly, in a randomized study of thalidomide-dexamethasone (TD) versus melphalan-prednisone (MP) as frontline therapy in transplant-ineligible NDMM patients69, response rates were higher with TD versus MP  but median OS was significantly shorter (41.5 vs. 49.4 months). This was also seen in the phase 3 FOCUS study of single-agent carfilzomib versus best supportive care in heavily pretreated RRMM patients; carfilzomib resulted in a higher response rate (19% vs. 11%), but this did not translate into improved outcomes in these ‘end-stage’ patients, in whom survival times would be expected to be limited–median PFS (3.7 vs. 3.3 months) and OS (10.2 vs. 10.0 months) were similar between arms67. Additionally, in the phase 3 CLARION study of KMP versus VMP in NDMM, the response rate was slightly higher with KMP  but PFS was similar (median 22.3 vs. 22.1 months)68.While it is generally established that, with all other things being equal, depth of response is clearly associated with improved long-term outcomes70 or the improvement of response over the course of treatment71. Evaluation of best response alone may not capture evolution of response and the impact of late, evolving responses on outcomes72, nor does it capture short-lived responses, which reflect aggressive disease. These aspects may account for discordance between response rates and long-term outcomes. Additionally, standard depth-of-response assessments may potentially mask responses of greater depth in both clinical trials and the real world. For example, while CR is associated with improved PFS and OS in MM73, this standard category of depth of response may include patients with differing levels of MRD. Increasing depth of MRD elimination has been associated with improvements in outcomes;74 thus, not all CRs are equal, and an imbalance in the ‘hidden’ rate of MRD elimination may confound interpretation of clinical trial and real-world data. In the GEM2005 phase 3 study in transplant-ineligible NDMM patients of VMP versus VTP induction plus VT vs. VP maintenance, although the CR rate favored the VTP arm (20% vs. 28%), among patients achieving CR on VMP and VTP, 70% and 45%, respectively, were MRD-negative by multiparameter flow cytometry, which translated into longer median PFS (32 vs. 23 months) and median OS (63 vs. 43 months) in the VMP arm37. This imbalance in MRD elimination may have contributed to the apparent disconnect between the relative CR rates and PFS/OS with VMP and VTP.A more important prognostic factor for improved outcomes in clinical trials may be achievement of a sustained responseFurthermore, cross-trial comparison of MRD-negative rates may also be confounded by the use of different techniques, with different sensitivities, for determining MRD elimination. Of note, the limited use of MRD negativity as a treatment goal in clinical practice may result in poorer outcomes compared to a clinical trial, with real-world patients potentially discontinuing treatment upon having achieved a CR but before obtaining possible MRD elimination.76. MM patients typically have a high symptom burden and impaired QoL76, and health-related QoL questionnaires have been employed with the aim of determining the extent of disease-related effects and the impact of treatment response and toxicity on QoL75. Recent novel therapies for RRMM have demonstrated a limited adverse impact or a positive impact on patients’ QoL in phase 3 trials, likely associated with a reduction in disease symptoms79. However, in interpreting these findings, it is important to consider the study design–as QoL data are susceptible to bias in open-label studies. Additionally, the instrument(s) employed should be evaluated; prior to the introduction of more recent instruments, there was a historical lack of an MM-specific QoL questionnaire to reflect the key aspects of disease and treatment burden on patients in MM;80 thus, while improvements may be recorded in more generic instruments, these may not capture some MM-specific issues of importance to patients. Furthermore, current instruments may not be sufficiently sensitive to detect QoL variations, depending on the type of treatment, particularly effects specific to recent novel agents. Although these tools are validated and widely used, they may lack the power to reflect the details of the real impacts of treatment on QoL81.Patient-reported outcomes are also of importance in MM, particularly in the context of long-term or continuous therapy administration, which is made feasible if therapies have minimal impact on patients’ QoL82. Intra- and inter-trial differential attrition and compliance rates across comparator arms may contribute to data missing not at random that may bias the relative treatment effects. Furthermore, there is often variation across trials in the types of analytic methods applied, including non-uniform approaches that address missing data, which may have a substantial impact on the point estimates of treatment effects. Patient-reported outcomes should thus be interpreted in this context.The timing of the patient-reported outcome assessments may also influence the extent to which effects of treatment toxicity are adequately captured, with patient-reported outcomes from clinical studies typically collected over a relatively short follow-up period and at the beginning of a treatment cycle, thus only reflecting patient states with resolved adverse events and/or short-term QoL informationThere are inherent difficulties in obtaining and analyzing complete sets of real-world data. Several methods can be used, including prospectively designed observational studies, retrospective chart reviews, and claims database analyses. However, currently there are no standardized methods for claims-based outcomes research in MM. The varied application of MM treatment algorithms across studies can also present challenges in retrospective interpretation of electronic medical record (EMR) analyses and other real-world data, for example, how to determine lines of therapy or when a patient is receiving maintenance rather than extended induction therapy.To investigate discrepancies between clinical trial efficacy and real-world effectiveness, we conducted a targeted literature review to identify sources of real-world, non-clinical-trial data in RRMM and evaluated these data in the context of phase 3 clinical trial results. For a description of the methodology, please see the supplementary information. Data from 61 relevant publications and abstracts are summarized in Supplementary Tables 84. Therefore, these data were evaluated similarly to PFS/TTNT data  appears smaller than in earlier lines of therapy for RRMM. It may be speculated that this could be due to closer alignment between the real-world and clinical trial patient populations in this setting or that the generally limited benefit in this setting results in more aligned outcomes.Patient selection is a known contributor to the gap between efficacy in clinical trials and effectiveness in the real-world setting89. Similarly, multiple unplanned dose modifications outside of the clinical trial setting, such as dose reductions, treatment delays, or dropping a component of therapy may adversely impact the relative dose intensity of active drugs, which has been associated with poorer OS90.Other potential sources for the gap between DOT achieved in the real-world versus the clinical trial setting may include treatment center effect , study design , and physician and patient preference. Furthermore, protocol-directed treatment rigor in clinical trials dictates dose modifications that may lead to better tolerability and longer duration of therapy in clinical trials. This is important because, as demonstrated in a number of clinical studies, a fixed duration of therapy may be associated with poorer outcomes in real-world practice, e.g., if patients discontinue therapy due to poor tolerability or high burden of treatment not seen or anticipated in the clinical trial92, including direct and indirect costs associated with medications and attending regular clinic visits. The burden of treatment, such as the route of administration and travel to the treatment center93, may not be captured by standard clinical trial endpoints but may substantially affect the feasibility of long-term treatment in the real-world setting as patients may succumb to treatment fatigue outside of the ‘motivating’ environment of clinical trial participation. Differences in adherence to treatment between a stringently monitored clinical trial setting and routine care may thus impact the generalizability of trial results to the real world.Real-world considerations such as the tolerability, convenience, and practicality of therapy are not captured within clinical trial reports or using conventional endpoints; however, they may also contribute to discrepancies between clinical trial and real-world outcomes. For example, treatment with a novel agent or regimen in the real-world setting may be associated with substantial patient time and economic burden82. These considerations must be acknowledged in tandem with clinical trial efficacy and safety data when interpreting data on different regimens; in this context, it is also important for patients to be well educated about treatment options and for clinicians to consider patient goals of therapy for what remains a generally incurable disease. For example, while a highly active and aggressive treatment approach, involving considerable burden and possibly toxicity, may be acceptable for younger, fitter patients if it offers the potential of long-term remission and elimination of MRD, for other groups of patients, the achievement of long-term disease control with minimal toxicity and preserved QoL may be an equally meaningful endpoint.Similarly, as noted earlier when discussing patient-reported outcomes, clinical trial data may not necessarily capture the cumulative long-term burden of the disease, its treatment, and the associated comorbidities and toxicity, or the psychosocial effects and lifestyle impact of living with MM95.In consideration of these issues, a number of recommendations may be made to aid the broader real-world use of drugs and attempt to close the efficacy versus effectiveness gap. First, greater education of academic and community physicians can provide them access to clinical trial management algorithms to improve utilization of novel agents and regimens. Associated guidance on side-effect management, QoL monitoring, and support regarding adherence to medication should be provided to enable prolonged therapy. Second, ensuring drug labels are updated for use in routine practice following approval of a novel agent in combination with an existing agent is recommended, as clinicians who are utilizing drugs per label need up-to-date guidance on the use of these drugs in all approved indications. Indications for use will need to be updated/maintained regularly to help ensure optimal use of therapy. Furthermore, the use of adaptive study designs, including for regulatory pathway studies, that could be adjusted to reflect changes in the treatment landscape would help with providing relevant data to the current real-world setting once results become availableIn conclusion, interpretation of data across clinical studies in MM and between clinical studies and real-world analyses is highly complex, with a multitude of factors confounding simple interpretation of efficacy, safety, and effectiveness between regimens. Both clinical trials and observational studies provide complementary information of importance in treatment decision-making. Clinical trials isolate treatment efficacy in selected patient populations and are less prone to bias, with good internal validity, while observational studies provide insights into treatment effectiveness in heterogeneous patient populations. In the absence of head-to-head comparisons between regimens, indirect comparisons of clinical study findings should be made with extreme caution; the most valid approach may be indirect comparisons of relative efficacy versus a common comparator using hazard ratios. Equally importantly, clinicians need to consider patient-related factors that may impact the translation of clinical trial outcomes to daily practice, such as QoL, tolerability, and burden of treatment, which may also help with tailoring treatment approaches for individual patients and thus optimize outcomes.In the future, it will be important not only to systematically assess the discrepancies between clinical trials but also the inconsistencies between the real-world and clinical trial settings, and to evaluate in greater detail the drivers of these differences. This may be supported by increased utilization of patient-reported outcomes, which are of increasing importance in informing treatment. Real-world effectiveness should be a metric considered in routine clinical practice, as it will be important to develop drugs and combinations that will be effective in the real world across patient populations, outside of the rigorously controlled clinical trial setting.Supplement - clean


HIV diagnosis is a critical step in linking HIV-infected individuals to care and treatment and linking HIV-uninfected persons to prevention services. However, the uptake of HIV testing remains low in many countries. HIV self-screening (HIVSS) is acceptable to adults, but there is limited data on HIVSS feasibility in community programmes. This study aimed to evaluate the feasibility of HIVSS in South Africa.We conducted a prospective study that enrolled participants through mobile site, homebased, workplace and sex worker programmes in two townships from May to November 2017. Following an information session on HIVSS, interested participants were offered one of three methods of HIVSS testing: supervised, semi-supervised, and unsupervised. Participants who opted for unsupervised testing and those who tested HIV positive after semi- or supervised HIVSS were followed up telephonically or with a home visit one week after receipt of the test kit to confirm results and linkages to care. Follow-up visits were concluded when the participant indicated that they had used the kit or had accessed a confirmatory HIV test.Of the 2061 people approached, 88.2% (1818/2061) received HIV testing information. Of this group, 89% (1618/1818) were enrolled in the study and 70.0% (1133/1618) were tested for HIV with the kit. The median age was 28 (IQR:23–33) years with an even gender distribution. Of those enrolled, 43.0% (696/1618) were identified through homebased outreach, 42.5% (687/1618) through mobile sites, 7.3% (118/1618) at their workplace and 7.2% (117/1618) from sex worker programmes. A total of 68.7% (1110/1616) selected unsupervised HIVSS, whereas 6.3% (101/1616) opted for semi-supervised and 25.0% ((405/1616) chose supervised HIVSS. Overall, the HIV prevalence using the HIVSS test was 8.2% (93/1129). Of those newly diagnosed with HIV, 16% (12/75) were initiated on ART. Almost half  of those tested were linked to a primary HIV test as follows: supervised ; semi-supervised  and unsupervised .Unsupervised HIVSS was by far the most selected and utilised HIVSS method. Linkages to primary and confirmatory testing for the unsupervised HIVSS and further care were low, despite home visits and telephonic reminders. Timely detection of human immunodeficiency virus (HIV) status is an important step in linking HIV-infected individuals to care and treatment and HIV-uninfected persons to prevention services. Early initiation of HIV treatment has been shown to reduce rates of HIV transmission, improve health outcomes and reduce cost of care –3. ThereIn 2016, South Africa had 270,000 new HIV infections and 7.1 million people living with HIV. Of those only 56% were receiving antiretroviral medication . DespiteThe objective of this study was to conduct a demonstration project on the feasibility (practicability and suitability for integration into existing community based HIV testing programs) of HIV self-screening among men, young women and sex workers in two areas in Gauteng.The study was conducted across two townships in the northern Johannesburg Metro Municipality, Gauteng. The two sites each have formal and informal settlements with an estimated population of 140,000 and 180,000 respectively in 2011. The provincial Gauteng HIV prevalence in 2012 was 12.4 and 17.6% in 2017 for adults aged 15–49 years –15. HIV The HIV self-screening demonstration project was a mixed-methods study involving prospective enrolment of participants for HIV testing from May–November 2017. The study population included those at high risk of contracting HIV, such as young women aged between 18 and 35 years, sex workers, and men who do not generally access healthcare services. These townships were selected as they had an existing outreach HIV-testing programme. Eligibility criteria included being 18 years or older, residing in the two study sites, having access to a telephone and willingness to provide contact information and to share HIV status with the study staff.Mobile HIV testing sites were set up temporarily with colourful gazebos in areas with substantial numbers of passing community members such as shopping centres, college campuses and sports fields. Six sites, three per township, were erected for 5–10 days each time, depending on the demand for HIV testing services.Homebased recruitment involved door-to-door visits in selected areas in each township based on the existing HIV-testing programm outreach schedule. Every street in the selected area was visited to recruit participants and enrol them in their homes.workplace testing, two companies  in one township with a high number of males were approached and offered HIV counselling, screening and testing to all employees.Through Sex workers programme: We collaborated with two established organisations in the two townships that provided HIV care and treatment services to sex workers to ensure representation of this high HIV risk population in the study.We used four strategies to recruit potential participants.Information sessions on the OraQuick HIV self-screening and HIV testing were provided to individuals and/or groups of 10 people or less. Participants not willing to use the HIV self-screening were offered an HIV rapid test according to South African HIV-testing guidelines .SupervisedA study staff member explained how to use the HIVSS test according to instructions on the package insert of the OraQuick test. The package insert was in English and was translated to other local languages. Participants were offered three different methods of self-screening:Semi-supervisedA lay HIV counsellor assisted the HIV self-screener on site with all aspects of HIV self-screening: the testing procedure, interpretation of results, and the provision of pre- and post-test counselling. The counsellor then referred the participant appropriately to ensure linkage to care.UnsupervisedA lay HIV counsellor assisted the self-screener on site with some aspects of HIV self-screening on request. This included the testing procedure, the interpretation of results, pre- and post-test counselling and appropriate referrals.The participant would complete testing without any assistance from study staff. However, group or individual demonstration of HIV self-screening was provided and pre- or post-counselling was accessible, either at their homes or telephonically.Since HIV self-screening is not recognised as a diagnostic test in South Africa, participants that opted for supervised and semi-supervised self-screening methods were also offered routine HIV rapid testing. The test strategy and algorithm for rapid testing were according to the current DoH guidelines . These pFollow-up assessments were conducted telephonically for all participants who tested HIV positive after either semi- or supervised HIV self-screening and for those that selected unsupervised HIV self-screening to evaluate linkages to care. A maximum of three telephonic follow-up calls (with the first telephonic follow-up being a week after distribution) or until the participant indicated that they used the kit or accessed a primary and confirmatory test. If three telephonic attempts at different times of the day were unsuccessful, a home visit was done. If both telephonic and home follow-up visits were unsuccessful, the participant was considered lost to follow-up. HIV-negative participants’ follow-ups were concluded once a primary HIV test was done, while HIV-positive participants’ follow-up was concluded after the primary and confirmatory test and if they were linked to HIV care and treatment.Trained researchers used hard copies of structured questionnaires to administer the pre-, follow-up and post-interviews. Questions included in the pre-HIV self-screening questionnaire administered were: participant demographics; prior HIV testing; knowledge of HIV self-screening; reasons for choosing self-screening; preferred method of screening; expectations of the kit; and if they would recommend the self-screening kit to others. The follow-up questionnaire focused on HIV self-screening results; if the participants has shared their result with anyone; and if they accessed primary; and confirmatory tests. Once the participant was tested or reported using the kit, a post-test questionnaire was administered to explore their experience of using the kit; whether initial expectations were met and if they would recommend that HIV self-screening should be made available more widely. Although the questionnaire guide was in English, trained researchers used local languages  when required. Anonymised collected data were captured into REDCap, a secure (password-protected) online application system and exported for statistical analysis .Data was analysed in Statistical Analysis Software SAS Enterprise Guide 7.1 . Fifteen participants with missing critical key variables  were not included in the analysis. Continuous data were analysed by medians and interquartile ranges (IQR), whereas frequencies were determined for categorical data and compared by method of HIV testing using the chi-square test of proportions. The proportion testing HIV positive using the OraQuick test was determined by the results from the supervised and semi-supervised, and self-report for those that performed unsupervised HIV self-screening.A total of 2061 people were approached through mobile sites, their workplaces, sex worker programmes (SWP) and house-to-house recruitment for HIV testing within seven months in 2017. The majority agreed to receiving HIV-testing information  and were offered either OraQuick self-screening and standard HIV rapid test using a fingerprick. Among the people that received HIV-testing information, 89.8% (1633/1818) opted for HIV self-screening, of whom 1618 participants enrolled in the study  of participants who were issued an HIV self-screening kit were older than 24 years of age (median 28 years inter quartile range 23–33).) Half the participants were male  ; 42.5% (687/1618) at mobile sites, 7.3%, (118/1618) the workplace, 7.2%, (117/1618) and through SWPs. Overall preferred method of HIVSS was unsupervised , followed by supervised  and semi-supervised . Unsupervised HIVSS was preferred across all the four recruitment strategies  used the kit, 2.0% (33/1618) reported that they did not use the kit, and 0.5% (7/1618) gave their kit to someone else to use ; and by testing strategy, unsupervised 10.0%, (64/640) compared to supervised 5.6% (22/394) and semi-supervised 7.2% (7/97). HIV prevalence based on HIVSS kits in individuals recruited through the homebased strategy was 9.1% (44/481), in the mobile site strategy 4.7% (23/491), in workplaces 2.3% (2/87) and in the FSW programmes was 34.3% (24/70)  stated that they had previously tested for HIV. Of these 41.7% (600/1440) had tested at least one year prior to study enrolment, 53.1% (319/600) were males, and 70.3% (422/600) were > 24 years old. Participants reported government primary healthcare facilities and hospitals  as the main sites where they previously tested, while others had tested at private healthcare facilities and workplace programmes.During follow-up of the participants who reported a HIV-positive result, 50.9% (29/57) shared their results with their partners. Two  HIV-infected participants in the unsupervised method, reported experiencing violence after sharing their results.During follow-up, access to post-test HIV services was assessed a week after receiving the HIVSS test kit. Participants confirmed linkage to care and treatment: counselling  across all groups, initiation on antiretroviral therapy  and CD4-testing  for those screening HIV positive.Overall, nearly half  of the participants that used the HIV self-screening kit received a primary HIV rapid test using the current serial HIV testing algorithm according to the DoH guidelines ; overallIn this study, HIVSS using the OraQuick assay self-screening through homebased, mobile site, workplace and sex work programmes was feasible and the preferred method of HIVSS was unsupervised compared to semi-supervised and supervised methods of HIVSS. HIV self-screening appeared to facilitate access to populations at high risk of HIV infection, and to people who had not tested for HIV within the past year.Other studies of HIV self-screening have also included high-risk population groups such as men having sex with men (MSM), pregnant women, healthcare workers and injecting drug users . HoweverThere was a high proportion (88%) of people who were interested in receiving HIV testing information and virtually all wanted to try HIVSS. Our findings are similar to those of previous studies , 20, 21 Linkage to primary and confirmatory testing was low despite participants being issued with referral letters on enrolment. Although data were not collected on reasons for failure to access additional HIV services in this study, reasons mentioned in other studies include no integration of HIV and primary healthcare services, travel costs, family responsibility and substance abuse .This study was a field demonstration project designed to asses feasibility and attempting to replicate conditions of existing primary healthcare programmes and other initiatives to increase access to HIV testing. The study also assessed different recruitment strategies , which were successful as the study accessed high-risk groups . The test kits used in the study were pre-qualified by the WHO.Participants in our study refusing HIV self-screening were not followed up to assess their linkages to other healthcare services. People approached for participation in the study received information on the HIV self-screening method from the study team, which could have biased their perceptions and uptake in favour of HIVSS. Furthermore, the HIVSS uptake could have been lowered by the requirement to have rapid HIV finger-prick test after HIV self-screening. The work place recruitment strategy proved to be challenging as it would have required work schedules to be suspended to accommodate the information session provided by the study team.The overall follow-up in the study was low as many participants could not be reached over the phone or through home visits. This high rate of lost-to-follow-up might have resulted in underreporting of HIV diagnosis, as well as participants not being interested in going to healthcare facilities to get tested for HIV again. The study did not assess linkages to HIV prevention services for participants that tested HIV negative and at high risk of contracting HIV.Linkages to confirmatory testing and further care were relatively low amongst those that opted for unsupervised HIVSS. Potential approaches to scale up and accelerate HIV self-screening integration should consider distribution methods, increasing awareness, cost and cost effectiveness implications, and strengthening linkages to confirmatory testing and further care possibly using mobile teams to travel to those that test HIV infected. HIV self-screening was feasible and easily implemented through homebased, mobile site, workplace and SWPs and unsupervised was the most preferred and utilised HIV self-screening method. The study was successful in reaching young women, men over the age of 24 years and sex workers across the two regions.