Mystery Rays from Outer Space

The point of a vaccine trial is to test whether the vaccine works.  If you get an answer to that question, the trial is a success.  The answer may be “No”, in which case the vaccine is a failure, but the trial would still be a success.  (The STEP HIV vaccine trial was therefore a success, though the vaccine was a failure.)

Malaria vaccines have been desperately needed forever, and in the past year there have been a few clinical trials. ¹  An encouraging, though unspectacular, trial was reported last year, where the vaccine offered modest protection in children. ²

The most successful vaccines seem to be T-cell based, rather than antibody-based, and the latest report, of a Phase II trial in Kenya,³ drives another nail in the antibody/malaria coffin:

The FMP1/AS02 vaccine did not protect children living in Kombewa against first episodes of P. falciparum malaria; it did not reduce the overall incidences of clinical malaria episodes or of malaria infections, and did not reduce parasite densities … Because of the clearly demonstrated overall lack of efficacy in this trial, FMP1/AS02 is no longer a promising candidate for further development as a monovalent malaria vaccine. … We therefore propose that future MSP-1₄₂ vaccine development efforts should focus on other antigen constructs and formulations. ³

For more reading about immunity to malaria:

• Quantity vs quality again
• Malaria eradication: The smallpox precedent
• Malaria eradication?
• Same song, different verse: Malaria and ectromelia

1. I don’t actually know much about the history of malaria vaccines, as far as trials go, so I don’t know how unusual it is to have clinical trials.  People have been working on malaria vaccines for decades, but none have worked very well.[↩]
2. Abdulla, S., Oberholzer, R., Juma, O., Kubhoja, S., Machera, F., Membi, C., Omari, S., Urassa, A., Mshinda, H., Jumanne, A., Salim, N., Shomari, M., Aebi, T., Schellenberg, D. M., Carter, T., Villafana, T., Demoitie, M. A., Dubois, M. C., Leach, A., Lievens, M., Vekemans, J., Cohen, J., Ballou, W. R., and Tanner, M. (2008). Safety and immunogenicity of RTS,S/AS02D malaria vaccine in infants. N. Engl. J. Med. 359, 2533-2544. doi:10.1056/NEJMoa0807773

   Bejon, P., Lusingu, J., Olotu, A., Leach, A., Lievens, M., Vekemans, J., Mshamu, S., Lang, T., Gould, J., Dubois, M. C., Demoitie, M. A., Stallaert, J. F., Vansadia, P., Carter, T., Njuguna, P., Awuondo, K. O., Malabeja, A., Abdul, O., Gesase, S., Mturi, N., Drakeley, C. J., Savarese, B., Villafana, T., Ballou, W. R., Cohen, J., Riley, E. M., Lemnge, M. M., Marsh, K., and von Seidlein, L. (2008). Efficacy of RTS,S/AS01E vaccine against malaria in children 5 to 17 months of age. N. Engl. J. Med. 359, 2521-2532. doi:10.1056/NEJMoa0807381[↩]

3. Ogutu, B., Apollo, O., McKinney, D., Okoth, W., Siangla, J., Dubovsky, F., Tucker, K., Waitumbi, J., Diggs, C., Wittes, J., Malkin, E., Leach, A., Soisson, L., Milman, J., Otieno, L., Holland, C., Polhemus, M., Remich, S., Ockenhouse, C., Cohen, J., Ballou, W., Martin, S., Angov, E., Stewart, V., Lyon, J., Heppner, D., Withers, M., & , . (2009). Blood Stage Malaria Vaccine Eliciting High Antigen-Specific Antibody Concentrations Confers No Protection to Young Children in Western Kenya PLoS ONE, 4 (3) DOI: 10.1371/journal.pone.0004708[↩][↩]

Jenner vaccinating a child

As I said last week, one of the biggest vaccine fiascos was the vaccine against respiratory syncytial virus (RSV) that was introduced in the 1960s. RSV is essentially a universal infection of children; it usually causes fairly mild respiratory disease, but because it’s so common the small fraction of cases that are more severe, end up being a leading cause of hospitalization for children. The vaccine was supposed to prevent that. As it happened, the vaccine itself didn’t cause any problems on its own; but children vaccinated with this RSV vaccine, who then later on were infected with RSV, actually had worse disease than those children who were uninfected. (Two children died.)

This enhanced respiratory disease (ERD) was really puzzling at the time, because the vaccine actually did induce a good, strong antibody response. But the antibody turned out to be non-protective. Just having an antibody response is not enough; the overall immune response needs to be involved and protective.

(I think we’re seeing some parallels to this concept now with T cell responses, where we are discovering that just having CD8 T cells doesn’t necessarily offer protection against things like HIV and hepatitis C virus, whereas the quality of the CD8 cells — now being measured as the range of cytokines they can produce — seems to be correlated with protection.)

The RSV vaccine turned out to trigger a TH2 type immune response. TH1/TH2 type responses are now a fundamental concept in immunology, but that hypothesis is a relatively new. Tim Mossman proposed it in 1986¹ and there was a significant lag before it was widely accepted. I think one of the findings that helped make TH1/TH2 accepted was the finding that the RSV vaccine triggered a strong TH2 immune response,² compared to the actual virus infection which mainly causes TH1-type immunity. This — to me, anyway — abruptly made the paradigm look less like a laboratory curiosity only seen in mice, and more like a real, clinically important phenomenon.

So the TH2 immune response seemed to more or less explain why the RSV vaccine caused disease. TH1 immune responses are generally protective against viruses, while TH2 immune responses are apparently more geared toward parasitic worms; TH2 responses tend to induce eosinophils and allergic-type responses, and that’s consistent with the clinical disease seen in the vaccinated children who got ERD.

But why did the vaccine induce a TH2 response? This is, of course, a huge question, especially if you’re trying to develop a new antiviral vaccine. One suggestion was the the vaccine screwed up the viral antigens too much. The vaccine used a formalin-inactivated virus, and the proposal was that the formalin alters the virus antigens and that directly caused the abnormal response³ If so, then this is a potential problem for any formalin-inactivated vaccine.

A new paper⁴ reaches a different conclusion. They say that formalin isn’t the main problem; rather, it’s the lack of adjuvant stimulation. Specifically, they say, you need to stimulate innate immunity via toll-like receptors (TLRs). Unless you do this, B cells don’t become completely activated, and though B cells produce antibodies the B cells don’t progress toward affinity maturation. That is, the normal process where antibodies are selected and shuffled to produce ultra-strong binders to their target antigens never gets underway. As a result, the vaccine induces low-affinity antibodies, and these low affinity antibodies are not protective.

It’s not clear — according to this model — whether the TH2 bias is actually the problem. Immune responses become biased to TH2 when there’s little innate immune stimulation, so the low affinity antibody and the TH2 response go hand in hand. Steve Varga (who has a nice commentary⁵ on this paper) has shown that some of the TH2 effects that were believed to be important in the pathogenesis of the ERD are not necessarily critical after all. Still, Varga and Delgado et al do seem to still feel that the TH2 shift is part of the disease.

The really exciting part of this finding is that it might actually be easy to fix. We now know a lot about TLR stimulation, and it should be possible to include TLR ligands along with the RSV vaccine:

These findings … open the possibility that inactivated RSV vaccines may be rendered safe and effective by inclusion of TLR agonists in their formulation. ⁴

Will this induce strong, protective immunity? Hopefully we’ll find out soon.

1. Mosmann TR, Cherwinski H, Bond MW, Giedlin MA, Coffman RL. Two types of murine helper T cell clone. I. Definition according to profiles of lymphokine activities and secreted proteins. J Immunol 1986; 136: 2348-2357[↩]
2. Priming immunization determines T helper cytokine mRNA expression patterns in lungs of mice challenged with respiratory syncytial virus. Graham BS, Henderson GS, Tang YW, Lu X, Neuzil KM, Colley DG. J Immunol. 1993 Aug 15;151(4):2032-40.[↩]
3. A potential molecular mechanism for hypersensitivity caused by formalin-inactivated vaccines. Moghaddam A, Olszewska W, Wang B, Tregoning JS, Helson R, Sattentau QJ, Openshaw PJ. Nat Med. 2006 Aug;12(8):905-7.[↩]
4. Maria Florencia Delgado, Silvina Coviello, A Clara Monsalvo, Guillermina A Melendi, Johanna Zea Hernandez, Juan P Batalle, Leandro Diaz, Alfonsina Trento, Herng-Yu Chang, Wayne Mitzner, Jeffrey Ravetch, José A Melero, Pablo M Irusta, Fernando P Polack (2008). Lack of antibody affinity maturation due to poor Toll-like receptor stimulation leads to enhanced respiratory syncytial virus disease Nature Medicine, 15 (1), 34-41 DOI: 10.1038/nm.1894[↩][↩]
5. Steven M Varga (2009). Fixing a failed vaccine Nature Medicine, 15 (1), 21-22 DOI: 10.1038/nm0109-21[↩]

A couple of years ago I published a paper characterizing a mutant cell line. ¹  I had been working, on and off, on the cells for around ten years, and they were already present in the lab when I joined it.  To write the paper I needed to know the details of their generation.  I clambered the ladder to the box marked “1992 LAB BOOKS”, pulled out Ethan’s notes for the year, flipped through them for a few minutes, and copied down the procedure — concentration of EMS, duration of treatment, and so on.  

Since 1992 I’ve used electronic data stored on 5¼-inch floppies, 3½-inch floppies (single and double-sided), Bernoulli drives, zip drives, Jazz drives, CDs, DVDs, and USB flash sticks; as well as on computer hard drives from at least four different OSes, and in God knows how many formats.  

The data on at least five of those media are now almost entirely inaccessible to me (if we were desperate, I’m fairly sure we could retrieve them, but it would be a huge chore).  Probably more than half of the different formats are almost unreadable today.  

Meanwhile, the data in those old-fashioned paper notebook are just as usable today as they were in 1992; and they will be equally usable in another sixteen years.   

I’m seeing a lot of discussion online about electronic lab notebooks, but this is an aspect that I don’t think has been emphasized nearly enough.  I know when you plan an experiment, you expect to publish it (in Nature) next week; but that’s not what always happens, is it.  And even if you do publish in a timely manner, who know what’s going to happen in fifteen years?  (I just thawed out some cells, frozen by a colleague in 1985, to analyze their antigen presentation pathways; something he had no interest in at the time.  He still has his lab notebooks describing his characterization, though, including stuff he didn’t publish at the time.)

A crude searchable experiments interface

How many of the protocols out there today are going to be functional in 15 years?  How many web sites from 1992 are still readable today?  (Since HTML wasn’t specified until 1993, the answer is “Not many”.)  History suggests that those electronic notebooks of today will be the impenetrable floppy disks of tomorrow. ²

Electronic notebooks do have one gigantic advantage over paper: Search.  I do use electronic notebooks of one kind or another, and the main reason is so I can search for the half-remembered experiment that used brefeldin A, and find out what concentration.  For years I’ve just used a cobbled-together thing I wrote myself, a HTML interface to an SQLite database linked with a Python cgi script (e.g. the screenshot to the right; click for a larger version).  It works nicely for searching, but it’s not as future-proof as I’d like (it depends on Python, which is being updated to a partially incompatible version soon; SQLite, which is likely to be stable for a few years, but I’m not counting on fifteen; and html, which is evolving as well.)  As well, it’s a little irritating to not have real data in there; so in the past year or so I’ve started using a wiki to keep lab notes in as well.  

I’ve actually made multiple false starts at the wiki/notebook thing, and there’s no guarantee that this latest version will stick, but it’s looking more promising than previous runs.  I’m using DokuWiki, which uses flat text (marked up) files for each page. I trust txt to be readable in 10 or 15 years, so even if (when) the rest of the interface is incompatible there should be usable information there.  It’s also easy to back up, and the wiki in general seems friskier and more responsive than some of the other wikis I’ve looked at.  I’m reasonably sure this will work.

But I’m still backing up to a paper lab notebook, because I know that works.

1. York IA, Grant EP, Dahl AM, Rock KL (2005). A mutant cell with a novel defect in MHC class I quality control. J Immunol 174:6839–6846. [↩]
2. Note that I haven’t looked in any detail at the electronic notebooks of today, and really have no idea how future-proof they are.  This is just my prejudice.[↩]

“Death and the Doctor” Published by William Humphrey, 1777

Last April I commented on a series of experiments  that used intravital microscopy to visualize cytotoxic T lymphocytes (CTL) attacking a tumor. ¹ Immensely cool though the movie is, I noted that I was surprised by their estimate of the rate of cell killing:

Another surprising finding — which is so different from previous work in different systems that I’m hesitant to believe it — is the timing of cell killing. Previous studies (such as the von Andrian paper² that produced this video) have suggested that CTL kill their targets in something under an hour; maybe 30 minutes or even less. Here. Bousso’s group find that the tumor cells take something like 6 hours to be killed. That’s such a large difference — and has such important implications for effectiveness of CTL killing — that, as I say, I’d like to see it confirmed before I take it to the bank.³

A new paper⁴ has run another estimate of the time it takes for a CTL to kill its target, and like most of the previous work, they conclude that it takes about a half-hour, give or take, to kill a target. They do come up with a fairly wide range of killing times, that depend on the target and the timing of the immune response — at the peak of the immune response when there are many cells the targets are killed faster (between 2 and 14 minutes), while at later stages, when there aren’t so many CTL, targets have half-lives of 48 min and 2.8 hr.

CTL killing a target cell (From a video by von Andrian)

This is not quite looking at the same thing as the video showed, though. In this paper, they were looking at the bulk effects, and that’s what almost all the previous studies have also looked at. The video was looking at a one-on-one interaction. What if targets are killed faster when several CTL gang up on them? Here, having different numbers of CTL caused the half-life of the targets to increase between about 10 and 20-fold. But this is probably simply because, with fewer CTL present, it took longer for them to find the target: Once a CTL found the target, the rate of killing was if anything faster than effectors at killing (“we find that LCMV-specific memory CD8 T cells kill more target cells per day than effectors”). ⁵

This is actually a disagreement with a previous paper ⁶ that also looked at killing rates, and offered evidence that different types of CTL can have different killing rates:

We reanalyse data previously used to estimate killing rates of CTL specific for two epitopes of lymphocytic choriomeningitis virus (LCMV) in mice and show that, contrary to previous estimates the “killing rate” of effector CTL is approximately twice that of memory CTL. ⁶

However, whichever of those studies is correct , both suggest that different types of CTL can have different killing efficiencies. This goes back to a point I’ve made several times, as have others (see e.g. Michael Palm’s TAG post here and references therein, including the comments by me and by Otto Yang) — CTL aren’t a uniform batch, and different kinds of CTL may have different types as well as rates of activities.

Returning to the intravital microscopy killing rate of 6 hours:⁷ I wonder if that reflects the nature of the CTL there, perhaps influenced by the tumor environment. Tumors are notoriously resistant to killing (probably because those tumors that are not resistant to killing were, um, killed, before they ever become clinically detectable) and it seems quite likely that an immunosuppressive tumor environment may change CTL types, or activities. I wonder if that would offer some way of intervention. Speeding up the rate of CTL killing from 6 hours to 30 minutes seems like it would be a huge influence of clearance of tumors. On the other hand, of course, it may be that the targets themselves are much more resistant to killing (again because tumor cells have been through selection to be resistant to the immune system) and cranking up CTL won’t make much difference.

1. Breart, B., Lemaître, F., Celli, S., Bousso, P. (2008). Two-photon imaging of intratumoral CD8+ T cell cytotoxic activity during adoptive T cell therapy in mice. Journal of Clinical Investigation, 118(4), 1390-1397. DOI: 10.1172/JCI34388 [↩]
2. Mempel, T. R., Pittet, M. J., Khazaie, K., Weninger, W., Weissleder, R., von Boehmer, H., and von Andrian, U. H. (2006). Regulatory T cells reversibly suppress cytotoxic T cell function independent of effector differentiation. Immunity 25, 129-141.[↩]
3. From this post[↩]
4. V. V. Ganusov, R. J. De Boer (2008). Estimating In Vivo Death Rates of Targets due to CD8 T-Cell-Mediated Killing Journal of Virology, 82 (23), 11749-11757 DOI: 10.1128/JVI.01128-08[↩]
5. There are also other videos of one-to-one killing, at least in vitro, that are more consistent with the 30-minute ballpark; see the image to the right for one example.[↩]
6. Yates A, Graw F, Barber DL, Ahmed R, Regoes RR, et al. (2007) Revisiting Estimates of CTL Killing Rates In Vivo. PLoS ONE 2(12): e1301. doi:10.1371/journal.pone.0001301[↩][↩]
7. Which I have become more relaxed about since my earlier skeptical comment[↩]

It’s been suggested for a long time that mice select mates by smelling MHC types, perhaps in the urine. MHC is by far the most variable region in vertebrate genomes, so this would offer a way for mice to avoid inbreeding: The more related the mice, the more likely they are to be similar at the MHC, so selecting a different MHC will help avoid inbreeding.

Partly as an argument by analogy, and partly through some rather poor-quality experiments, it’s also been argued that humans select mates the same way — that differences in MHC type make a partner more desirable. These are the notorious sweaty T shirt experiments that most people seem to have at least vaguely heard of.

I started off very skeptical about the human claims, because the quality of the experiments has, as I say, tended to be poor. There have been small numbers of people, indifference to alternative explanations, and a lot of post hoc hand-waving. (If the preferences turned out to be reversed, why, it was because the female was near her period, or something like that.) I think that most people who have actually looked at the data have had similar reservations, but that hasn’t stopped the concept from becoming pretty well known.

I became even more skeptical about the human experiments as I learned more about the mouse data. The evidence for MHC as a mechanism for avoiding inbreeding turned out to be relatively weak, or at least inconsistent (see here for my first discussion); and recently a paper that I found fairly convincing (discussed here) suggested that MHC is not in fact used by mice in this way at all — rather, a much more plausible, highly variable family of molecules called “major urinary proteins” (MUPs) are the source of the anti-inbreeding odor in mouse urine.

Much of the interest in human MHC and sex has been driven by the mouse observation, so I think that if mice don’t use MHC to select mates, then likely humans don’t, either. Still, it remains possible, even probable, that difference species use different methods to select mates. And since humans don’t even have variable MUPs (as far as I know) MHC remains in the chase.

A recent paper¹ tries to look at this in a more objective manner, using genome-wide data on couples. Unfortunately the numbers are still quite small (just 30 couples each from a European-American subset, and an African subset) and the results remain slightly ambiguous. Their conclusion was that

African spouses show no significant pattern of similarity/dissimilarity across the MHC region … We discuss several explanations for these observations, including demographic effects. On the other hand, the sampled European American couples are significantly more MHC-dissimilar than random pairs of individuals … This study thus supports the hypothesis that the MHC influences mate choice in some human populations.

So, heads we win, tails you lose, because even though their hypothesis was invalidated overall, some post-hoc wiggling (“demographic effects”) lets them dismiss the data they don’t like.

I’m still pretty skeptical about any real effect from MHC on mate choice. I’m willing to be convinced otherwise, but it’s going to take a larger and more rigorous study than this one to make me interested.

1. Raphaëlle Chaix, Chen Cao, Peter Donnelly, Molly Przeworski (2008). Is Mate Choice in Humans MHC-Dependent? PLoS Genetics, 4 (9) DOI: 10.1371/journal.pgen.1000184[↩]

It’s rapidly becoming accepted, if not quite dogma, that T cell quality (rather than, or as well as, quantity) is a critical factor in controlling HIV infection. (I’ve talked about T cell quality several times previously. What it means, simplified, is that antiviral cytotoxic T cells can have a range of different functions, and those CTL with multiple functions seem to do better at controlling HIV than those with only one or a handful of functions.) As a result, there’s a lot of interest in developing vaccines that induce multi-functional CTL, in the hope that those vaccines will better control the virus itself. A recent paper¹ from Norm Letvin’s lab, though, supports the concept but doesn’t offer a lot of encouragement for the vaccine strategy.

Letvin’s group vaccinated monkeys against immunodeficiency virus using several different vaccine strategies, and evaluated the quality of the antiviral CTL elicited by those vaccines. As we have now come to expect, there were big differences in both the quantity and the quality of T cells with the various approaches. No surprises so far.

Next, they challenged the vaccinated monkeys with virus. Again, as expected, those monkeys who controlled the virus best, had the largest and most multifunctional CTL response to the challenge (“both the magnitudes and functional profiles of the virus-specific CD8+ T cells generated by vaccination were associated with control of viral replication following SHIV-89.6P challenge“).

The unexpected part, though, was that the vaccine response didn’t tell you anything about the challenge response. That is, even though some vaccines gave lots of multifunctional T cells and others gave relatively little, that did not correlate with the eventual response after challenge; “Although the different vaccination regimens generated qualitatively different virus-specific T-cell populations, those differences were lost following the virus challenge.” Letvin’s group concluded that the similar levels of virus after challenge overrode the vaccine pattern.

The good news — kind of — is that any of the vaccines seemed to work relatively well. After challenge, “the profile of cytokine production by the virus-specific T lymphocytes in the control monkeys was heavily biased toward cells that produce only IFN-?, while the virus-specific T lymphocytes of all of the experimentally vaccinated monkeys following challenge were uniformly polyfunctional.” That is, even though the vaccines didn’t ultimately differ from each other, vaccination did lead to a different, and probably better quality, CTL response than in unvaccinated monkeys.

This might suggest that even testing CTL quality as well as quantity after vaccination may not be very predictive. However, it’s also possible that the monkey model is once again being deceptive. For example, if their suggestion that the challenge dose re-set the CTL quality is correct, this might be highly sensitive to both the number of infecting viruses and, even more, to the precise kinetics of early viral replication; and there are a myriad of other differences as well. The bottom line, though, is a reminder that we really don’t understand antiviral immunity very well in any system, let alone the baroque interactions between HIV and the immune system.

1. Sun, Y., Santra, S., Schmitz, J.E., Roederer, M., Letvin, N.L. (2008). Magnitude and Quality of Vaccine-Elicited T-Cell Responses in the Control of Immunodeficiency Virus Replication in Rhesus Monkeys. Journal of Virology, 82(17), 8812-8819. DOI: 10.1128/JVI.00204-08[↩]

Our results suggest that some CD8 T cells induced by vaccination are more efficient than others at responding to a viral challenge. These findings have implications for future AIDS virus vaccine studies, which should consider the “fitness” of vaccine-induced T cells in order to generate robust responses in the face of virus exposure.

 
– Limited Maintenance of Vaccine-Induced Simian Immunodeficiency Virus-Specific CD8 T-Cell Receptor Clonotypes after Virus Challenge.   Miranda Z. Smith, Tedi E. Asher, Vanessa Venturi, Miles P. Davenport, Daniel C. Douek, David A. Price, and Stephen J. Kent. Journal of Virology, August 2008, p. 7357-7368, Vol. 82, No. 15 doi:10.1128/JVI.00607-08

For more information see:

• Effective T cell vaccines
• Adjuvants: Quality as well as quantity
• Quality vs. quantity in cancer vaccination
• Quality vs quantity again

All you want in a vaccine is that (1) it doesn’t do any harm, and (2) it prevents disease. When you’re running initial tests on a potential vaccine, though, you often can’t actually include (2) in the tests — especially for a human vaccine– because it’s rarely acceptable to infect your volunteers with, say, HIV. Instead, you identify surrogate measures like level of antibody, or number of T cells, and you judge your vaccine on those surrogate measures at first. If your vaccine doesn’t induce a lot of antibody, say, cytotoxic T lymphocytes (CTL), or whatever it is that you’re measuring, then back to the drawing board.

The problem with that approach, of course, is that we still don’t understand the immune system all that well, and so the surrogate measures that get used may not be ideal. We’ve been seeing this with a number of HIV vaccines, which in preliminary tests induced great surrogate measures but ended up not protecting against disease. This (among other things) has led to a recent focus of quality of immune responses as well as quantity. A recent paper emphasizes this, coming at the issue from a very different direction.

Malaria vaccines haved been a huge challenge to develop. Getting strong immune responses against protective antigens has been pretty difficult, and then moving these into clinical settings has usually shown rather underwhelming efficacy. One of the approaches that was tested, as a way of safely inducing immune responses, was genetic immunization (or DNA vaccines). In this approach, rather than a protein antigen, you inject your patients with DNA encoding the antigen of interest. The DNA gets taken up by cells, expresses your antigen of interest, and hopefully that antigen then induces an immune response.

As I understand it, this approach has worked pretty well in mice, but not so well in humans. Immune responses in humans vaccinated with DNA have usually been very low; so low that (based on these surrogate measures) the vaccines were abandoned and people weren’t challenged with the pathogen.

… it became clear that, for reasons that remain poorly understood, the same high immunogenicity could not be reproduced in humans.. Genetic immunisation of volunteers could induce at best CTL cells but low, or absent, CD4 T-cell and antibody responses. The immunogenicity was so low that, although reported, several clinical trials were considered unsuitable for publication. In many of these trials, challenge by the infectious pathogens was either not possible or decided against in view of the limited responses induced.¹

Pierre Druilhe, at the Pasteur institute, decided to test the approach more directly, with a challenge experiment.¹ They vaccinated some chimps with a DNA vaccine against a malaria antigen, an approach that had previously led to very weak immune responses in humans. Here again, the immune responses were not very exciting: there was no humoral (antibody) response, though there were detectable MHC class I and MHC class II-restricted T cell responses. But the effect on malaria infection was dramatic; there was 
sterilizing immunity, no detectable parasites, in 3 of the 4 chimps.

The authors comment “these findings suggest that the relative scarcity of an immune response should not necessarily exclude the assessment of the protective efficacy of a vaccine candidate when ethically feasible,” which is a reasonable suggestion, though I think their data don’t really show that this was a “scarce” immune response. Did they really see a low response when you consider T cells, or is it actually a strong response that is strictly biased to the cell-mediated immune response? They didn’t include some information that I’d consider critical — how the T cell responses here compared to a more conventional vaccine approach. They cite an earlier paper² showing some protection against schistosomiasis in cattle after a DNA vaccine in spite of undetectable antibody responses, to argue that DNA vaccine protecting in spite of a low immune response may be a general effect; but since the schistosomiasis study didn’t even look at T cell responses as far as I can see, it doesn’t answer that question either.

Overall, I think this is not a very strong paper. It’s just a handful of animals (and some of their stats are highly questionable; I don’t think you can challenge 4 animals twice and call that a cumulative N=8), and they don’t show me how the T cell responses compare to other approaches, so I don’t know if it’s even a “weak” immune response. Still, it’s an interesting observation, and emphasizes that it’s important to understand the quality of an immune response as well as quantity.

1. Daubersies P, Ollomo B, Sauzet J-P, Brahimi K, Perlaza B-L, et al. (2008) Genetic Immunisation by Liver Stage Antigen 3 Protects Chimpanzees against Malaria despite Low Immune Responses. PLoS ONE 3(7): e2659. doi:10.1371/journal.pone.0002659[↩][↩]
2. Field testing of Schistosoma japonicum DNA vaccines in cattle in China. Shi F, Zhang Y, Lin J, Zuo X, Shen W, Cai Y, Ye P, Bickle QD, Taylor MG. Vaccine. 2002 Nov 1;20(31-32):3629-31. [↩]

A couple of weeks ago, talking about tumor vaccination, I said this:

It has been suggested that cancer antigens tend to be poor MHC binders. That would mean that the peptide falls out of the MHC complex relatively rapidly and becomes invisible to T cells, so that to keep a certain level of target on the cell surface, you’d need to start with much more; in other words, the peptide would be less immunogenic. (This has been explicitly shown for a number of tumor epitopes, but as far as I know has not been globally demonstrated. It occurs to me that the Immune Epitope Database [IEDB] may have enough information to at least make a start at that analysis; maybe I’ll take a run at it, in whatever of my free time isn’t taken up by playing baseball with my fanatic son.)

I took a quick pass at the question last night and came up with a firm “Maybe“.

The question is whether tumor epitopes — that is, peptides that cytotoxic T lymphocytes recognize when they specifically attack tumors — tend to bind with lower affinity to the MHC class I molecules, compared to regular old epitopes from, say, viruses or bacteria.  Using the Immune Epitope Database I came up with 8620 MHC class I epitopes whose affinities were recorded. ¹   I exported these using a custom report.²   I compared these to the 750-odd peptides listed in the Cancer Immunity Tumor Antigen database, finding 145 hits — i.e. 145 tumor antigens whose affinity has been recorded.  I took the affinity (in nM) from these and from the remaining 8475 epitopes.

Because MHC class I binding affinity spreads over a huge range, averages are pretty meaningless.   I broke the samples down into ranges of affinity and calculated the percentage of epitopes that fell into each (see the table to the right). Results are summarized in the chart below (click for a larger version).   In brief, tumor epitopes are less likely to have very high affinity (about 3% of tumor epitopes, vs. 13% of the remainder), and more likely to have moderate affinity (89% vs. 70%).   On the other hand the tumor epitopes were less likely to have very low affinity.   I suspect this is because the tumor epitopes are more likely to have been detected clinically, which biases toward at least moderate affinity or you can’t find them.

This is just a quick and dirty check. I didn’t try to validate any of the peptides or the affinities.   I pooled together affinities that were derived using different methods.   There are only a handful of tumor epitopes with affinities known, so this is a small sample size.   Still, there does seem to be a trend there, which I didn’t expect to see — I thought I was going to throw cold water on the hypothesis, which was proposed based on only four or five epitopes. Kind of interesting, I thought.

The scripts etc that I used to work these out are available on request, though I suspect anyone able to tweak them for their own use could do a better job writing them than me.

1. Summary of the process: Select class I binders
   Limit to IC50 info: Purified MHC – Radioactivity-Competition (or equilibrium binding)-IC50 nM
   More than 10,000 so split into three parts for export:
   <10 nM: 4,716 items found
   10-100 nM: 6,855 items found
   >100 nM:  6,463 items found

   In addition: Limit to Cell bound MHC – Fluorescence-Competition (or equilibrium binding)-IC50 nM; Cell bound MHC – Fluorescence-Association (or direct binding)-EC50 nM; Cell bound MHC – Radioactivity-Competition (or equilibrium binding)-KD nM; Cell bound MHC – Radioactivity-Competition (or equilibrium binding)-IC50 nM; Cell bound MHC – T cell response-Competition (or equilibrium binding)-IC50 nM; Lysate – Radioactivity-Association (or direct binding)-EC50 nM; Purified MHC – Fluorescence-Competition (or equilibrium binding)-IC50 nM; Purified MHC – Fluorescence-Association (or direct binding)-EC50 nM; Purified MHC – Fluorescence-Competition (or equilibrium binding)-KD nM
   <100000 1,822 items found [↩]

2. Article PubMed ID
   1 Epitope Linear Sequence
   2 Epitope Source Accession Number
   3 Assay Type Category
   4 Assay Type
   5 Qualitative Measurement
   6 Quantitative Measurement
   7 Measurement Inequality
   8 Units
   9 MHC Allele
   10 MHC Allele Class [↩]

Although 750-odd tumor antigens may seem like quite a few potential vaccine targets, it’s really not so much when you’re dealing with billions of individual tumors; and so when designing a tumor vaccine, you may have to make some compromises. The peptide may bind to the MHC class I with low affinity, for example, making it relaitvely non-immunogenic. Several groups who are working on tumor vaccines have tried to work around this problem by optimizing tumor antigens in various way, hoping to boost the immunogenicity while retaining the specificity of the peptide. This has often seemed to work quite nicely, cranking up immune responses significantly while keeping the response focused on the tumor. Nevertheless, a recent study¹ suggests that this may not be a good idea.

It has been suggested² that cancer antigens tend to be poor MHC binders. That would mean that the peptide falls out of the MHC complex relatively rapidly and becomes invisible to T cells, so that to keep a certain level of target on the cell surface, you’d need to start with much more; in other words, the peptide would be less immunogenic. (This has been explicitly shown for a number of tumor epitopes, but as far as I know has not been globally demonstrated. It occurs to me that the Immune Epitope Database [IEDB]   may have enough information to at least make a start at that analysis; maybe I’ll take a run at it, in whatever of my  free time isn’t taken up by playing baseball with my fanatic son.)

If the natural peptide doesn’t bind stably to MHC, perhaps an analog peptide — a peptide with a slightly different amino acid sequence — can be made, with the same T cell recognition properties, that does bind well; and this analog could be used for immunization. That’s just what has been done in a number of clinical trials,³ and the results have actually looked good; for example, mice immunized with an analog peptide of a melanoma tumor antigen generated far more T cells than with the natural antigen. ⁴

But bigger is not always better, and now Speiser et al. have examined the effects of an analog peptide qualitatively as well as quantitatively. Again using a melanoma antigen, they compared  the immune response to a natural and an analog peptide vaccination. As with other studies, the analog peptide induced more T cells; about twice as many. But the quality⁵ of the T cells induced by the natural peptides was much better, to the point that the less abundant natural response, was more effective in its anti-tumor function than the more abundant response induced by the analog peptides.

At first, it seems paradoxical that the “less immunogenic” natural peptide induced more strongly functional T cells. … CD8 T cells must be able to recognize low amounts of viral peptide antigen for protection. More recently, in vivo experiments in mice showed that the peptide concentration used for DC labeling and priming inversely correlated with the avidity of TCRs of memory cells. Thus, one may conclude that vaccination should be done with low peptide doses and/or peptides with low HLA binding stability (provided that one can still elicit a reasonably strong T cell response).

(My emphasis.)  This is actually strikingly reminiscent of some of the recent work on viral — especially HIV — immune responses, where T cell quality (induction of “multifunctional” T cells) seems to be more important than maxing out the number of T cells. ⁶.  I guess it’s not surprising that the anti-cancer and anti-viral responses are similar in this, as they are in many other ways.

1. Speiser, D.E., Baumgaertner, P., Voelter, V., Devevre, E., Barbey, C., Rufer, N., Romero, P. (2008). Unmodified self antigen triggers human CD8 T cells with stronger tumor reactivity than altered antigen. Proceedings of the National Academy of Sciences, 105(10), 3849-3854. DOI: 10.1073/pnas.0800080105[↩]
2. For example: Poor immunogenicity of a self/tumor antigen derives from peptide-MHC-I instability and is independent of tolerance. Zhiya Yu, Marc R. Theoret, Christopher E. Touloukian, Deborah R. Surman, Scott C. Garman, Lionel Feigenbaum, Tiffany K. Baxter, Brian M. Baker, and Nicholas P. Restifo. J Clin Invest. 2004 August 16; 114(4): 551-559. doi: 10.1172/JCI200421695. [↩]
3. Speiser et al. cite a half dozen instances; I won’t parrot them[↩]
4. Parkhurst et al. (1996) Improved induction of melanoma-reactive CTL with peptides from the melanoma antigen gp100 modified HLA-A*0201-binding residues. J Immunol 157:2539-2548. [↩]
5. Quality in this case means functional ability to deal with the cancer; it includes things like activatability, amount of cytokine production, and amount of lytic proteins produced[↩]
6. For example: Induction of multifunctional human immunodeficiency virus type 1 (HIV-1)-specific T cells capable of proliferation in healthy subjects by using a prime-boost regimen of DNA- and modified vaccinia virus Ankara-vectored vaccines expressing HIV-1 Gag coupled to CD8+ T-cell epitopes. Goonetilleke N, Moore S, Dally L, Winstone N, Cebere I, Mahmoud A, Pinheiro S, Gillespie G, Brown D, Loach V, Roberts J, Guimaraes-Walker A, Hayes P, Loughran K, Smith C, De Bont J, Verlinde C, Vooijs D, Schmidt C, Boaz M, Gilmour J, Fast P, Dorrell L, Hanke T, McMichael AJ. J Virol. 2006 May;80(10):4717-28. and references therein.[↩]