Transcriptional correlates of malaria in RTS,S/AS01-vaccinated African children: a matched case–control study

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1. 

   Version published to 10.7554/elife.70393 on eLife

   Jan 21, 2022
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   eLife

   Nov 18, 2021

   Author Response:

   Reviewer #1 (Public Review):

   Summary

   Moncunill et al set out to investigate a very important question: why are half of children vaccinated three times with RTS,S AS01 protected from clinical malaria - and half not? To do so they isolated PBMCs before vaccination and one month after third vaccination and stimulated them in vitro with DMSO (vehicle control), two malaria antigens (CSP (part of RTS,S) & AMA1) or HBS (hepatitis B antigen - part of RTS,S).

   They then assessed their transcriptional response by blood transcriptional module analysis and correlated those results with previous published data on antibody titers and T cell cytokine production to find associations. To assess risk of clinical malaria, responses were compared between RTS,S vaccinated children who developed clinical malaria in the one year …

   Author Response:

   Reviewer #1 (Public Review):

   Summary

   Moncunill et al set out to investigate a very important question: why are half of children vaccinated three times with RTS,S AS01 protected from clinical malaria - and half not? To do so they isolated PBMCs before vaccination and one month after third vaccination and stimulated them in vitro with DMSO (vehicle control), two malaria antigens (CSP (part of RTS,S) & AMA1) or HBS (hepatitis B antigen - part of RTS,S).

   They then assessed their transcriptional response by blood transcriptional module analysis and correlated those results with previous published data on antibody titers and T cell cytokine production to find associations. To assess risk of clinical malaria, responses were compared between RTS,S vaccinated children who developed clinical malaria in the one year follow-up (cases) and those who received RTS,S or a comparator vaccine and did not (controls). They found that responses after RTS,S vaccination did not predict protection from clinical malaria. Instead a blood transcriptional module signature related to dendritic cells, inflammation, and monocytes before vaccination may be associated with clinical malaria risk.

   Strengths

   Immune correlates of protection are evaluated in African children (who are the RTS,S target population) in a natural transmission setting.

   Excellent set of controls: children (same age) vaccinated with RTS,S or comparator vaccine alongside each other -> retrospectively stratified by whether the did or did not develop clinical malaria : controls for the effect of a developing immune system and would allow to disentangle RTS,S specific and clinical malaria specific response patterns.

   Weaknesses

   RTS,S is composed of CSP & HBS. yet when PBMCs from children vaccinated three time with RTS,S are stimulated with these peptides no transcriptional differences compared to children receiving a rabies or meningitis vaccine were detected (Figure 2). this lack of recall response impacts all downstream conclusions and comparisons made in the paper.

   The fact that bulk transcriptional profiling of Ag-stimulated PBMCs (and specifically to CSP) did not identify large significant differences in BTM expression between the RTS,S vs. comparator group could be due to several factors. First of all, the frequency of antigen-specific CD4+ T cells was very low among CD4+ T cells (Figure 4 of the manuscript shows that CSP-specific CD4+ T cells comprise < 0.004% of all CD4+ T cells). This low frequency of CSP-specific T cells is consistent with other RTS,S studies [e.g. as we state on line 330, we have previously found that CSP-specific T-cells in RTS,S/AS01 vaccines comprise < 0.10% of all CD4+ T cells (1)]. Moreover, CD4+ T cells themselves comprise approximately 45-57% of all PBMCs (2). Thus, finding an expression signal between the RTS,S vs. comparator group would require the signal to be high enough to be detected in only 0.002% of all PBMCs [0.004% (% CSP-specific CD4+ T cells out of total CD4+ T cells) x 51% (average % of CD4+ T cells out of all PBMCs) = 0.002%]. Thus, lack of detectable recall response does not mean lack of recall response. Moreover, as suggested below, we opted not to focus the rest of the manuscript on the Ag-stimulation results.

   Second of all, the PBMCs were stimulated on site for 12h and then cryopreserved. This stimulation time was chosen based on the kinetics of IFN- and IL-2 mRNA response (3), but other responses may have had different kinetics and thus have already resolved or have not yet occurred by the 12-h cryopreservation. We have added text in the manuscript to discuss these caveats (“Another potential reason for why no BTMs were found to associate with the response to RTS,S/AS01 vaccination or with protection when analyzing CSP-stimulated PBMC is that all PBMC were stimulated on site for 12 hours (this stimulation time was chosen based on the kinetics of the IFN-gamma transcriptional response) and then cryopreserved. Thus, we were unable to detect earlier transient responses that had already resolved by 12 hours, as well as more delayed response that had not yet initiated by 12 hours, if such responses occurred”).

   It should be noted that in all our analyses, the stimulated results were adjusted for DMSO to focus on the antigen-specific response only. This would explain why we detect signal in the DMSO samples but not in response to stimulation. We have realized that this was not very well described in the figure captions and the Methods section and have added more details, including the model description in Methods section. As such, we do not believe that these results impact all downstream conclusions. We believe that the unstimulated results provide significant new insights into the immune and molecular mechanisms of RTS,S vaccine efficacy, not necessarily directly related to the RTS,S-specific acquired immune response. Finally, we would like to highlight the fact that we have improved our model specification to directly account for the pairing of some of the samples using a random effect using the limma package. This has slightly increased statistical power, and as such the number of significantly differentially expressed BTMs in response to stimulation is a bit higher (but still much less than that for the DMSO). Originally, we had decided against the use of a random effect due to the computational cost of estimating the random effect.

   Transcriptional responses 1 month after the final RTS,S vaccination do not predict clinical malaria risk (Figure 3) - this is a key finding, which should be central to the conclusion of this paper.

   Considering that Kazmin et al. (4) showed that the transcriptional response to the third RTS,S/AS01 dose peaks at Day 1 post-injection, with some decline by Day 6 and approximately 90% of the response having waned by Day 21 (with the caveat that Kazmin et al.’s study population was malaria-naïve adults), we do not find it surprising that there were only a few BTMs whose 1 month post-final RTS,S dose associated with clinical malaria risk. However, the point is well-taken about the relative merits of the baseline. We have edited the Discussion to include discussion of the Month 3 correlates results:

   “Compared to the 45 BTMs whose baseline levels significantly associated with clinical malaria risk in RTS,S/AS01-vaccinated African children, fewer BTMs (seven) had levels at one month post-final RTS,S/AS01 dose that significantly associated with clinical malaria risk. Moreover, if a more stringent FDR cutoff had been used (i.e. 5%), six of these seven BTMs would not have been identified. Thus it is entirely possible that, at one month post-final RTS,S/AS01 dose, there is no circulating immune transcriptomic signature predictive of risk. Such a conclusion would not be surprising, given that in malaria-naïve adults, the transcriptional response to the third RTS,S/AS01 dose has been shown to peak at Day 1 post-injection, with some decline by Day 6 and approximately 90% of the response having waned by Day 21 (17). Therefore, it is likely that the sampling scheme in this study (one month post-final dose) misses the majority of the transcriptional response to RTS,S/AS01.”

   The take-home message put forward in the title/abstract (that a monocyte and DC related pre-vaccination signature predicts risk of clinical malaria in RTS,S vaccinated children) is not strongly supported by the data. It is based on blood transcriptional modules related to monocytes being picked out when comparing RTS,S vaccinated cases and controls.

   Thank you for giving us the opportunity to provide further rationale for our focus on the 7 monocyte-related and 4 DC-related BTMs shown in Figure 6B (MAL067 column) out of the 45 total BTMs whose baseline expression associated with clinical malaria risk in RTS,S/AS01-vaccinated children. The reviewer implies that these modules were chosen for focus somewhat randomly or without justification (or, even worse, “cherry picked”), which we would agree would be an imperfect method for drawing conclusions.

   First, we have always ensured to mention that the 45 baseline modules that correlated with risk in RTS,S recipients (Fig 6B, MAL067 column) belonged to many functional annotations, including DC cells and monocytes. (Abstract: “In contrast, baseline levels of BTMs associated with dendritic cells and with monocytes (among others) correlated with malaria risk”) (Main text, lines 519-522: “Compared to the results from the month 3 analysis (7 BTMs), the baseline correlates analysis of MAL067 revealed a larger number (45) of BTMs, spanning many functional categories, whose month 0 levels in vehicle-stimulated PBMC nearly all associated with clinical malaria risk in RTS,S/AS01 recipients .”

   The focus on DC cells and monocytes is due to two reasons: 1) the fact that the DC-related modules and the monocyte-related modules were some of the most significant correlations (lines 522-524: “The BTM with the most significant association with risk was “enriched in monocytes (II) (M11.0)” (FDR = 1.80E-14), followed by “inflammatory response (M33)” (FDR = 2.45E-07) and “resting dendritic cell surface signature (S10)” (FDR = 6.03E-07).”

   Second, the baseline association of DC- and monocyte-related modules appeared to generalize across populations: (Abstract: “A cross-study analysis supported generalizability of the baseline dendritic cell- and monocyte-related BTM correlations with malaria risk to healthy, malaria-naïve adults, suggesting that certain monocyte subsets may inhibit protective RTS,S/AS01-induced responses.”; Main text: “BTMs related to dendritic cells and to monocytes were most consistently associated with risk across these three studies [“resting dendritic cell surface signature (S10)”, “DC surface signature (S5)”, “enriched in dendritic cells (M168)”, “enriched in monocytes (I) (M4.15)”, “enriched in monocytes (II) (M11.0)”, “enriched in monocytes (IV) (M118.0)”, and “monocyte surface signature (S4)” significantly correlated with risk in all three studies].”

   The first two sentences of the Discussion (lines 577-580) explain our focus on monocytes and DCs:

   “Our main finding is the identification of a baseline blood transcriptional module (BTM) signature that associates with clinical malaria risk in RTS,S/AS01-vaccinated African children. In a cross-study comparison, much of this baseline risk signature – specifically, dendritic cell- and monocyte-related BTMs – was also recapitulated in two of the three CHMI studies in healthy, malaria-naïve adults.”

   Finally, we note that the title (“A baseline transcriptional signature associates with clinical malaria risk in RTS,S/AS01-vaccinated African children”) does not restrict to DC-related or monocyte-related BTMs, rather, we chose this title based on the larger number of BTMs, and higher correlations with risk, in the baseline analysis compared to the Month 3 analysis.

   We have revised all instances where we have communicated this less clearly, e.g. “for why we identified a baseline monocyte transcriptional signature of risk” has been changed to “for why we identified monocyte-related BTMs in our transcriptional signature of risk”.

   Many other modules are picked out as well e.g. cell cycle (Figure 6B). An in-depth analysis of the genes in these module and what their up and downregulation can tell us about their function is warranted to support the conclusions.

   Thank you for the suggestion to look at the cell cycle module in Figure 6B. You make a good point that this module is the only module to show a significant association with clinical malaria risk across all 4 of the RTS,S studies and should therefore be further examined. First, we have added this to the text:

   “Only one BTM, “cell cycle and transcription (M4.0)”, was significantly associated with risk across all four studies. Of the 335 genes in this module (M4.0), 130 were also present in one or more of the six “monocyte-related” BTMs shown in Figure 6B (297 genes total across all six BTMs), suggesting that the “cell cycle” and “monocyte” results may actually be picking up the same signal.”

   We have done the gene-level analysis as suggested, resulting in 8 new supplemental figures (Figure 6-figure supplements 1-8) and one new supplemental table (S5). We have also made the following revisions to the text:

   In Results: “To gain insight into specific module-member genes that may be involved in the RTS,S/AS01 baseline risk signature, we performed the same analysis on the gene level, i.e. examined associations with clinical malaria risk for each of the constituent genes in the 45 BTMs shown in Figure 6B. Figure 6-figure supplements 1-8 show the gene-level association results within the eight BTMs that were significantly associated with clinical malaria risk in MAL067 and at least two of the three CHMI studies, and had at least one gene in MAL067 that was significantly associated with risk (these eight correspond to M4.0, S10, S5, M168, M4.3, M11.0, M4.15, and S4). Within MAL067, 35 unique genes were shown to significantly associate with malaria risk (Supplementary Table 5); 9 of these genes (CCNF, MK167, KIF18A, NPL, RBM47, CFD, MAFB, IL13RA1, and CCR1) also had significant association with non-protection in one of the CHMI studies. Although no individual gene was significantly associated with risk across >2 studies, many showed consistent effect (direction and magnitude) across 3 studies. This further supports our choice to focus on modules instead of individual genes as GSEA increases power to detect more subtle but coordinated changes in gene expression data that would be missed otherwise. For this same reason, GSEA has been shown to enhance cross-study comparisons (45).”

   In Discussion: “Our gene-level correlates analyses suggest an alternative hypothesis, however. With the caveat that the gene-level analyses were performed post hoc, high baseline expression of STAB1 (which is present in DC-related, monocyte-related, and cell cycle-related modules) was found to positively associate with clinical malaria risk (Figure 6-figure supplements 1, 2, and 6). STAB1 encodes stabilin-1 (also called Clever-1), a transmembrane glycoprotein scavenger receptor that links extracellular signals to intracellular vesicle trafficking pathways (58). Interestingly, stabilin-1high monocytes show downregulation of proinflammatory genes, and T cells co-cultured with stabilin-1high monocytes showed decreased antigen recall, suggesting that monocyte stabilin-1 suppresses T cell activation (56). Thus one possibility is that stabilin-1high immunosuppressive monocytes circulating at baseline could decrease protective RTS,S-induced T-cell responses, or inhibit another aspect of adaptive immunity. Single-cell transcriptomic profiling of PBMC or purified monocyte subsets in future RTS,S trials in African children in malaria-endemic areas could help test this hypothesis.”

   Impact

   This paper will inform future studies looking for correlates of RTS,S induced protection from clinical malaria in a variety of ways:

   It validates the blood transcriptional module approach (as published by Li S, Rouphael N, Duraisingham S, Romero-Steiner S, Presnell S, Davis C, Schmidt DS, Johnson SE, Milton A, Rajam G, et al: Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat Immunol 2014) to find target cell populations which can then be investigated in much more detail.

   It shows that studying PBMC recall responses after peptide stimulation post final vaccination is not the way forward, since no response is detected (Figure 2). future studies can now take an alternative approach. e.g. since unstimulated PBMCs (vehicle control) from RTS,S vaccinated children were different from those who received a comparator vaccine (Figure 2) RTS,S vaccine signatures could be picked up much more easily by whole blood RNAseq.

   It implicates innate immune cells in shaping an individuals response to a vaccine - an exciting basis for future functional and mechanistic studies.

   We are glad the reviewer appreciates the value of the study.

   Reviewer #2 (Public Review):

   This paper reports a sub-study of the RTS,S/AS01 malaria vaccine Phase 3 trial, which aimed to identify groups of genes (blood transcriptional modules, BTMs) for which expression in DMSO or antigen-stimulated PBMCs was associated with clinical malaria during a 12-month follow-up period. Study subjects were infants and children who received either RTS,S/AS01 or comparator vaccines (meningococcal C for infants, rabies vaccine for children), enrolled in the study in Tanzania and Mozambique (with some additional analyses using samples from Gabonese infants).

   Using PBMCs collected at baseline before vaccination and 3 months later (a month after the third vaccine dose), stimulated with DMSO or parasite antigens, the authors used RNA-sequencing to identify BTMs which were different between recipients of RTS,S/AS01 vs comparator vaccines; which were different between baseline and month 3 in RTS,S/AS01 recipients; and which differed between RTS,S/AS01 recipients with a malaria episode and those without a malaria episode during the follow-up period. This combination of analyses might help to distinguish BTMs specifically associated with RTS,S/AS01 vaccine efficacy from those associated with other factors influencing susceptibility to malaria. To further aid mechanistic understanding the authors examined correlations between BTMs and measures of cellular and humoral immune responses. To try to establish generalisability the authors examined whether BTMs identified in African children were also associated with developing malaria in RTS,S/AS01-vaccinated malaria-naïve adults in the United States who underwent controlled human malaria infection (CHMI).

   Strengths of the study include:

   1. The relatively large number of subjects, the large amount of transcriptomic and immunological data which has been generated (and made publicly accessible), and the extensive analysis to evaluate associations between BTMs and numerous immunological variables.

   1. Clear explanation of both the rationale and methods for most of the analyses

   1. The attempt to validate findings in the CHMI studies

   1. Matching of subjects to try to eliminate the confounding effects of age, study site, and time of vaccination

   Weaknesses of the study include:

   1. Despite the relatively large size of the study, it is hard to know whether it had sufficient power to achieve its main objective, and we are not presented with data to demonstrate how successfully the authors managed to match subjects for age, timing of vaccination and follow-up duration

   We have added the following to our “limitations” paragraph in the Discussion: “Fourth, despite the relatively large size of the study, our statistical power was limited by the number of malaria cases with available samples; sampling additional controls would not have increased our statistical power.”

   Moreover, we now also provide the new Supplementary Table 1, which provides complete information on participant match ID, site, age cohort, sex assigned at birth, and time of vaccination.

   1. The comparator group to the RTS,S/AS01 vaccine is not a single vaccine, but two vaccines, but the presentation of the data makes it difficult to identify what effect this may have had on the results

   Indeed, comparators received a rabies vaccine or the meningococcal C conjugate depending on the age cohort. However, we think that the impact on the study results and conclusions is minimal since the main results are based on baseline gene expression and its association with malaria risk within RTS,S vaccinees. Correlates of malaria risk in comparators are done separately. Comparator vaccination may be a confounding factor for age cohort, but we are not analyzing the effect of age cohort on the transcriptional profile. Comparators are only included in the analysis of RTS,S immunogenicity at post-vaccination (RTS,S vs Comparators, Fig 2A, Comparison (1)) and we have adjusted analyses by age cohort and hence by comparator vaccine. The fact that the comparators received different control vaccines only stresses that the BTMs found to be associated with RTS,S vaccination are specific to the RTS,S vaccine.

   Moreover, as an alternative way to identify RTS,S-specific transcriptional responses, we also include Comparison (2), which compares Month 3 to Month 0 transcription levels within RTS,S vaccinees. We include in the text extensive discussion of the merits and drawbacks of each comparison:

   “Two comparisons were done to characterize the transcriptional response to RTS,S/AS01 vaccination: Comparison (1): comparing gene expression in month 3 samples from RTS,S/AS01 vs comparator recipients (month 3 RTS,S/AS01 vs comparator); and Comparison (2): comparing gene expression in month 3 vs month 0 from RTS,S/AS01 recipients (RTS,S/AS01 month 3 vs month 0). Each comparison has its own advantages: Comparison (1) allows the identification of RTS,S/AS01-specific responses while taking into account other environmental factors to which the children are exposed, such as malaria exposure (albeit malaria transmission intensity was low during the study at both sites). Moreover, the very young ages of the trial participants mean that RTS,S/AS01-induced changes may be confounded with normal developmental changes in participant immune systems, further underscoring the value of Comparison (1), as it does not involve comparison across two different time points. On the other side, an advantage of Comparison (2) is that it takes into consideration each participant’s intrinsic baseline gene expression. Comparison (1) uses data from both infants and children, whereas Comparison (2) can only yield insight into RTS,S/AS01 responses in children (as baseline samples were not collected from infants).”

   1. A very "liberal" false-discovery rate (FDR) threshold has been used throughout to define significant associations. An FDR of 0.2 indicates that 20% (or 1 in 5) results which are considered significant will be false-discoveries. This means that the "significant" results must be interpreted with a high degree of caution. Typically researchers use lower FDR thresholds, like 0.05 or 0.01, although one may argue for different thresholds under different circumstances

   While it is not uncommon to use a threshold of 20% for immune correlates studies [e.g. (5-10)], we agree with you that it is important to clearly state the chosen FDR rate and to discuss conclusions in the context of the FDR rate used. We see we could improve our manuscript in this respect. We have added the following:

   Results: “Compared to the 45 BTMs whose baseline levels significantly associated with clinical malaria risk in RTS,S/AS01-vaccinated African children, fewer BTMs (seven) had levels at one month post-final RTS,S/AS01 dose that significantly associated with clinical malaria risk. Moreover, if a more stringent FDR cutoff had been used (i.e. 5%), six of these seven BTMs would not have been identified. Thus it is entirely possible that, at one month post-final RTS,S/AS01 dose, there is no circulating immune transcriptomic signature predictive of risk…”

   Discussion: “Finally, while it is not uncommon to use an FDR cutoff of 20% in high-dimensional immune correlates studies [e.g. (65-70)], our results should be interpreted with the requisite level of caution. However, we do note that many of our significant modules in the baseline risk analysis would have survived even lower FDR cutoffs (in many cases even a 1% cutoff), giving us a fair degree of confidence in our results. For example, of the seven monocyte-related BTMs whose baseline levels associated with risk, all would have survived a 5% FDR cut-off, and three even a 1% cut-off; likewise, of the four dendritic cell-related BTMs whose baseline levels associated with risk, all would have survived a 5% FDR cut-off, and three even a 1% cut-off.”

   Moreover, we have revised Figures 2, 3, and 6 so that it is easy to discern whether a specific BTM correlation would also pass more stringent FDR cutoffs, through the addition of 1, 2, or 3 asterisks where appropriate: “|FDR| < 0.2 (), < 0.05 (), < 0.01 (**).” Note that, most central to the key message of the paper, many of the monocyte-related, DC-related, and cell cycle-related BTMs would have passed more stringent FDR cutoffs, with many even passing a 1% FDR cutoff (as discussed above).

   1. A perplexing finding, which is not addressed in detail, is the large number of BTMs which differ between RTS,S and comparator vaccine groups after DMSO stimulation of PBMCs, but these are not seen when PBMCs are stimulated with parasite antigens in DMSO (and a similar finding for month 3 vs month 0 samples from RTS,S recipients). This raises some concern about the stimulation experiments, because one might expect that the DMSO vehicle in the antigen preparations would trigger a similar response to DMSO alone.

   It should be noted that in all our analyses, the stimulated results were adjusted for DMSO to focus on the antigen-specific response only. This would explain why we detect signal in the DMSO samples but not in response to stimulation. We have realized that this was not very well described in the figure captions and the Methods section and have added more details, including the model description in Methods section. As such, we do not believe that these results impact all downstream conclusions. We believe that the unstimulated results provide significant new insights into the immune and molecular mechanisms of RTS,S vaccine efficacy, not necessarily directly related to the RTS,S-specific acquired immune response. We would also like to highlight the fact that we have improved our model specification to directly account for the pairing of some of the samples using a random effect using the limma package. This has slightly increased statistical power, and as such the number of significantly differentially expressed BTMs in response to stimulation is a bit higher (but still much less than that for the DMSO). Originally, we had decided against the use of a random effect due to the computational cost of estimating the random effect.

   The fact that bulk transcriptional profiling of Ag-stimulated PBMCs did not identify almost any significant differences in BTM expression between the RTS,S vs. comparator group could be due to several factors. First of all, the frequency of antigen-specific CD4+ T cells was very low among CD4+ T cells (Figure 4 of the manuscript shows that CSP-specific CD4+ T cells comprise < 0.004% of all CD4+ T cells). This low frequency of CSP-specific T cells is consistent with other RTS,S studies [e.g. as we state on line 330, we have previously found that CSP-specific T-cells in RTS,S/AS01 vaccinees comprise < 0.10% of all CD4+ T cells (1)].

   Moreover, CD4+ T cells themselves comprise approximately 45-57% of all PBMCs (2). Thus, finding an expression signal between the RTS,S vs. comparator group would require the signal to be high enough to be detected in only 0.002% of all PBMCs [0.004% (% CSP-specific CD4+ T cells out of total CD4+ T cells) x 51% (average % of CD4+ T cells out of all PBMCs) = 0.002%]. Thus, lack of detectable recall response does not mean lack of recall response. Moreover, as suggested below, we opted not to focus the rest of the manuscript on the Ag-stimulation results.

   Second of all, the PBMCs were stimulated on site for 12h and then cryopreserved. This stimulation time was chosen based on the kinetics of IFN-g and IL-2 mRNA response (3), but other responses may have had different kinetics and thus have already resolved or have not yet occurred by the 12-h cryopreservation. We have added text in the manuscript to discuss these caveats (“Another potential reason for why no BTMs were found to associate with the response to RTS,S/AS01 vaccination or with protection when analyzing CSP-stimulated PBMC is that all PBMC were stimulated on site for 12 hours (this stimulation time was chosen based on the kinetics of the IFN-g transcriptional response) and then cryopreserved. Thus, we were unable to detect earlier transient responses that had already resolved by 12 hours, as well as more delayed response that had not yet initiated by 12 hours, if such responses occurred.”.

   The authors partly achieved their aims. They identified BTMs differentially expressed between RTS,S/AS01 and the comparator vaccines, and between baseline and month 3 in RTS,S/AS01 recipients. They also identified BTMs at month 3 associated with developing malaria, and BTMs at baseline associated with developing malaria. These latter BTMs were partly replicated in the CHMI study subjects. Higher expression of BTMs associated with monocytes and dendritic cells were most consistently identified across the different analyses and their expression in stimulated baseline samples was most consistently associated with development of clinical malaria in RTS,S/AS01 recipients. However there were inconsistencies in associations between some of the studies, and it is possible that the "consistent" monocyte and dendritic cell BTMs would not be so consistent if a more stringent FDR threshold was used. However the authors conclusions are largely quite measured and for the most part they do not over-interpret the significance of their findings.

   We have added the following to the Discussion: “Finally, while it is not uncommon to use an FDR cutoff of 20% in high-dimensional immune correlates studies [e.g. (65-70)], our results should be interpreted with the requisite level of caution. However, we do note that many of our significant modules in the baseline risk analysis would have survived even lower FDR cutoffs (in many cases even a 1% cutoff), giving us a fair degree of confidence in our results. For example, of the seven monocyte-related BTMs whose baseline levels associated with risk, all would have survived a 5% FDR cut-off, and three even a 1% cut-off; likewise, of the four dendritic cell-related BTMs whose baseline levels associated with risk, all would have survived a 5% FDR cut-off, and three even a 1% cut-off.”

   Overall the work provides some evidence that baseline immunological status, particularly related to monocyte and dendritic cell responses and possibly their role in or response to baseline inflammation, may be a determinant of how well the RTS,S vaccine works to prevent malaria. This provides a basis for further work to optimise the effectiveness of the vaccine. The usefulness of PBMC stimulation to predict an individual's response to vaccination will be limited because this is not a method which can be used at scale in resource limited settings, but the concept that vaccine response could be enhanced by modifying pre-vaccine immunological or inflammatory status is potentially important. The data published with this study will be a valuable resource and will undoubtedly be used by others to address similar questions. Increasing the efficacy of malaria vaccines remains an extremely important goal, and identifying possible mechanisms which restrict the efficacy of RTS,S is important.

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   9. Fletcher HA, Snowden MA, Landry B, Rida W, Satti I, Harris SA, Matsumiya M, Tanner R, O'Shea MK, Dheenadhayalan V, Bogardus L, Stockdale L, Marsay L, Chomka A, Harrington-Kandt R, Manjaly-Thomas ZR, Naranbhai V, Stylianou E, Darboe F, Penn-Nicholson A, Nemes E, Hatherill M, Hussey G, Mahomed H, Tameris M, McClain JB, Evans TG, Hanekom WA, Scriba TJ, McShane H. T-cell activation is an immune correlate of risk in BCG vaccinated infants. Nat Commun. 2016;7:11290.
   10. Young WC, Carpp LN, Chaudhury S, Regules JA, Bergmann-Leitner ES, Ockenhouse C, Wille-Reece U, deCamp AC, Hughes E, Mahoney C, Pallikkuth S, Pahwa S, Dennison SM, Mudrak SV, Alam SM, Seaton KE, Spreng RL, Fallon J, Michell A, Ulloa-Montoya F, Coccia M, Jongert E, Alter G, Tomaras GD, Gottardo R. Comprehensive Data Integration Approach to Assess Immune Responses and Correlates of RTS,S/AS01-Mediated Protection From Malaria Infection in Controlled Human Malaria Infection Trials. Front Big Data. 2021;4:672460.

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3. 

   eLife

   Nov 18, 2021

   Evaluation Summary:

   This paper assesses whether transcriptional signatures in antigen-stimulated peripheral blood mononuclear cells can predict protection from clinical malaria after vaccination with RTS,S AS01 in African children. It adds to the large body of literature looking for immune correlates of protection following RTS,S vaccination and will be of interest to the malaria vaccine community and to those studying in systems vaccinology. An association of malaria risk with monocytes before vaccination may have been uncovered, which will require thorough testing in future functional and mechanistic studies.

   (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the …

   Evaluation Summary:

   This paper assesses whether transcriptional signatures in antigen-stimulated peripheral blood mononuclear cells can predict protection from clinical malaria after vaccination with RTS,S AS01 in African children. It adds to the large body of literature looking for immune correlates of protection following RTS,S vaccination and will be of interest to the malaria vaccine community and to those studying in systems vaccinology. An association of malaria risk with monocytes before vaccination may have been uncovered, which will require thorough testing in future functional and mechanistic studies.

   (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

   Read the original source
4. 

   eLife

   Nov 18, 2021

   Reviewer #1 (Public Review):

   Summary
   Moncunill et al set out to investigate a very important question: why are half of children vaccinated three times with RTS,S AS01 protected from clinical malaria - and half not? To do so they isolated PBMCs before vaccination and one month after third vaccination and stimulated them in vitro with DMSO (vehicle control), two malaria antigens (CSP (part of RTS,S) & AMA1) or HBS (hepatitis B antigen - part of RTS,S). They then assessed their transcriptional response by blood transcriptional module analysis and correlated those results with previous published data on antibody titers and T cell cytokine production to find associations. To assess risk of clinical malaria, responses were compared between RTS,S vaccinated children who developed clinical malaria in the one year follow-up (cases) and those who …

   Reviewer #1 (Public Review):

   Summary
   Moncunill et al set out to investigate a very important question: why are half of children vaccinated three times with RTS,S AS01 protected from clinical malaria - and half not? To do so they isolated PBMCs before vaccination and one month after third vaccination and stimulated them in vitro with DMSO (vehicle control), two malaria antigens (CSP (part of RTS,S) & AMA1) or HBS (hepatitis B antigen - part of RTS,S). They then assessed their transcriptional response by blood transcriptional module analysis and correlated those results with previous published data on antibody titers and T cell cytokine production to find associations. To assess risk of clinical malaria, responses were compared between RTS,S vaccinated children who developed clinical malaria in the one year follow-up (cases) and those who received RTS,S or a comparator vaccine and did not (controls). They found that responses after RTS,S vaccination did not predict protection from clinical malaria. Instead a blood transcriptional module signature related to dendritic cells, inflammation, and monocytes before vaccination may be associated with clinical malaria risk.

   Strengths
   Immune correlates of protection are evaluated in African children (who are the RTS,S target population) in a natural transmission setting.
   Excellent set of controls: children (same age) vaccinated with RTS,S or comparator vaccine alongside each other -> retrospectively stratified by whether the did or did not develop clinical malaria : controls for the effect of a developing immune system and would allow to disentangle RTS,S specific and clinical malaria specific response patterns.

   Weaknesses
   RTS,S is composed of CSP & HBS. yet when PBMCs from children vaccinated three time with RTS,S are stimulated with these peptides no transcriptional differences compared to children receiving a rabies or meningitis vaccine were detected (Figure 2). this lack of recall response impacts all downstream conclusions and comparisons made in the paper.
   Transcriptional responses 1 month after the final RTS,S vaccination do not predict clinical malaria risk (Figure 3) - this is a key finding, which should be central to the conclusion of this paper.
   The take-home message put forward in the title/abstract (that a monocyte and DC related pre-vaccination signature predicts risk of clinical malaria in RTS,S vaccinated children) is not strongly supported by the data. It is based on blood transcriptional modules related to monocytes being picked out when comparing RTS,S vaccinated cases and controls. Many other modules are picked out as well e.g. cell cycle (Figure 6B). An in-depth analysis of the genes in these module and what their up and downregulation can tell us about their function is warranted to support the conclusions.

   Impact
   This paper will inform future studies looking for correlates of RTS,S induced protection from clinical malaria in a variety of ways:
   It validates the blood transcriptional module approach (as published by Li S, Rouphael N, Duraisingham S, Romero-Steiner S, Presnell S, Davis C, Schmidt DS, Johnson SE, Milton A, Rajam G, et al: Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat Immunol 2014) to find target cell populations which can then be investigated in much more detail.
   It shows that studying PBMC recall responses after peptide stimulation post final vaccination is not the way forward, since no response is detected (Figure 2). future studies can now take an alternative approach. e.g. since unstimulated PBMCs (vehicle control) from RTS,S vaccinated children were different from those who received a comparator vaccine (Figure 2) RTS,S vaccine signatures could be picked up much more easily by whole blood RNAseq.
   It implicates innate immune cells in shaping an individuals response to a vaccine - an exciting basis for future functional and mechanistic studies.

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5. 

   eLife

   Nov 18, 2021

   Reviewer #2 (Public Review):

   This paper reports a sub-study of the RTS,S/AS01 malaria vaccine Phase 3 trial, which aimed to identify groups of genes (blood transcriptional modules, BTMs) for which expression in DMSO or antigen-stimulated PBMCs was associated with clinical malaria during a 12-month follow-up period. Study subjects were infants and children who received either RTS,S/AS01 or comparator vaccines (meningococcal C for infants, rabies vaccine for children), enrolled in the study in Tanzania and Mozambique (with some additional analyses using samples from Gabonese infants).

   Using PBMCs collected at baseline before vaccination and 3 months later (a month after the third vaccine dose), stimulated with DMSO or parasite antigens, the authors used RNA-sequencing to identify BTMs which were different between recipients of RTS,S/AS01 …

   Reviewer #2 (Public Review):

   This paper reports a sub-study of the RTS,S/AS01 malaria vaccine Phase 3 trial, which aimed to identify groups of genes (blood transcriptional modules, BTMs) for which expression in DMSO or antigen-stimulated PBMCs was associated with clinical malaria during a 12-month follow-up period. Study subjects were infants and children who received either RTS,S/AS01 or comparator vaccines (meningococcal C for infants, rabies vaccine for children), enrolled in the study in Tanzania and Mozambique (with some additional analyses using samples from Gabonese infants).

   Using PBMCs collected at baseline before vaccination and 3 months later (a month after the third vaccine dose), stimulated with DMSO or parasite antigens, the authors used RNA-sequencing to identify BTMs which were different between recipients of RTS,S/AS01 vs comparator vaccines; which were different between baseline and month 3 in RTS,S/AS01 recipients; and which differed between RTS,S/AS01 recipients with a malaria episode and those without a malaria episode during the follow-up period. This combination of analyses might help to distinguish BTMs specifically associated with RTS,S/AS01 vaccine efficacy from those associated with other factors influencing susceptibility to malaria. To further aid mechanistic understanding the authors examined correlations between BTMs and measures of cellular and humoral immune responses. To try to establish generalisability the authors examined whether BTMs identified in African children were also associated with developing malaria in RTS,S/AS01-vaccinated malaria-naïve adults in the United States who underwent controlled human malaria infection (CHMI).

   Strengths of the study include:

   1. The relatively large number of subjects, the large amount of transcriptomic and immunological data which has been generated (and made publicly accessible), and the extensive analysis to evaluate associations between BTMs and numerous immunological variables.
   2. Clear explanation of both the rationale and methods for most of the analyses
   3. The attempt to validate findings in the CHMI studies
   4. Matching of subjects to try to eliminate the confounding effects of age, study site, and time of vaccination

   Weaknesses of the study include:

   1. Despite the relatively large size of the study, it is hard to know whether it had sufficient power to achieve its main objective, and we are not presented with data to demonstrate how successfully the authors managed to match subjects for age, timing of vaccination and follow-up duration
   2. The comparator group to the RTS,S/AS01 vaccine is not a single vaccine, but two vaccines, but the presentation of the data makes it difficult to identify what effect this may have had on the results
   3. A very "liberal" false-discovery rate (FDR) threshold has been used throughout to define significant associations. An FDR of 0.2 indicates that 20% (or 1 in 5) results which are considered significant will be false-discoveries. This means that the "significant" results must be interpreted with a high degree of caution. Typically researchers use lower FDR thresholds, like 0.05 or 0.01, although one may argue for different thresholds under different circumstances
   4. A perplexing finding, which is not addressed in detail, is the large number of BTMs which differ between RTS,S and comparator vaccine groups after DMSO stimulation of PBMCs, but these are not seen when PBMCs are stimulated with parasite antigens in DMSO (and a similar finding for month 3 vs month 0 samples from RTS,S recipients). This raises some concern about the stimulation experiments, because one might expect that the DMSO vehicle in the antigen preparations would trigger a similar response to DMSO alone.

   The authors partly achieved their aims. They identified BTMs differentially expressed between RTS,S/AS01 and the comparator vaccines, and between baseline and month 3 in RTS,S/AS01 recipients. They also identified BTMs at month 3 associated with developing malaria, and BTMs at baseline associated with developing malaria. These latter BTMs were partly replicated in the CHMI study subjects. Higher expression of BTMs associated with monocytes and dendritic cells were most consistently identified across the different analyses and their expression in stimulated baseline samples was most consistently associated with development of clinical malaria in RTS,S/AS01 recipients. However there were inconsistencies in associations between some of the studies, and it is possible that the "consistent" monocyte and dendritic cell BTMs would not be so consistent if a more stringent FDR threshold was used. However the authors conclusions are largely quite measured and for the most part they do not over-interpret the significance of their findings.

   Overall the work provides some evidence that baseline immunological status, particularly related to monocyte and dendritic cell responses and possibly their role in or response to baseline inflammation, may be a determinant of how well the RTS,S vaccine works to prevent malaria. This provides a basis for further work to optimise the effectiveness of the vaccine. The usefulness of PBMC stimulation to predict an individual's response to vaccination will be limited because this is not a method which can be used at scale in resource limited settings, but the concept that vaccine response could be enhanced by modifying pre-vaccine immunological or inflammatory status is potentially important. The data published with this study will be a valuable resource and will undoubtedly be used by others to address similar questions. Increasing the efficacy of malaria vaccines remains an extremely important goal, and identifying possible mechanisms which restrict the efficacy of RTS,S is important.

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6. 

   Version published to 10.1101/2021.05.19.21257227v1 on medRxiv

   May 19, 2021