Mystery Rays from Outer Space

Meddling with things mankind is not meant to understand. Also, pictures of my kids

July 30th, 2008

Tumor immunity and prognosis

Cancer cell attack (from XVivo)Does the immune system control tumors?

The current understanding says “Yes”, but with reservations. As I’ve noted in previous posts (here and links therein, among others), there’s pretty solid evidence now that the immune system controls tumors in their early development.

Probably (we don’t know this for sure, but evidence points to it) there are many proto-tumors that begin to form, taking a few steps along the long route to full-blown cancer, but that are destroyed by the immune system long before they ever become detectable. Probably many more tumors form and take those early steps, and though they are not completely eliminated by the immune system they are controlled — the immune system prevents the proto-tumor from ever becoming more than a little cluster of cells, even though that little cluster of cells may persist for many years.

By the time a cancer is clinically detectable, though, does the immune system have any effect? Again referring to the current model, proto-tumors are able to advance to the detectable stage because they have avoided immune control. Therefore, the tumor we see are almost by definition uncontrollable by the immune system, right?

Immune control of clinical tumors?

T cells infiltrating tumor Actually, that’s not quite what the theory suggests. A tumor that’s reached the detectable level grows faster than the immune system shuts it down, true; but that doesn’t mean there’s no influence of the immune system. Yes, the tumor could be growing twice as fast as it should, with no influence of the immune system. But equally, the tumor could be growing 10 times too fast, with the immune system destroying 90% of that. The overall rate would look the same; but in the latter case, we only need to push the growth rate down, or crank up the immune response, by 11%, to drive the tumor into remission.

Is there any direct evidence that the immune system slows the progression even of outright cancer? Certainly there is, though most of the evidence I know of it a little circumstantial. One line of reasoning is that, if the immune system controls tumors, then we should see a correlation between immune responses to tumors, and their prognosis. In fact, there are quite a few papers that show that: For example, a paper in Clinical Cancer Research last month.1

This group actually looked at two parameters, that might or might not be connected, and their influence on prognosis of ovarian carcinoma. On the one hand, they looked at evidence for tumor immune evasion: How stringently was the tumor avoiding cytotoxic T lymphocyte recognition? On the other hand, they looked at infiltrating T cells in the tumor: How well could T cells recognize the tumor?

(To my mind, the latter is a much more important question, because we don’t know much about thresholds and cumulative effects of immune evasion — that is, we aren’t yet able to look at the recognition molecules as such, and declare that T cells will or will not recognize the tumor. Of course, this sort of study, that correlates phenotype and function, will be critical for answering that question.)

Immune evasion is bad for survival

Tumor and T cellsImpressively, there’s a strong link between good prognosis and phenotype. Tumors that seems to have good antigen presentation, have a better prognosis than those that have apparently blocked their antigen presentation pathways efficiently. (They were able to break it down further than that, to the specific types of molecules that may be important.) And these are not trivial differences; people with defective antigen presentation survived for 1 or 2 years, those with good antigen presentation averaged 4 or 5 years or longer.

Patients with all five markers positive in the tumor lived almost four times as long (median survival 5.67 years; P < 0.01) and were 4.74 times less likely to die from their disease

The other half of the study was almost equally impressive.

Patients with complete absence of tumor-infiltrating T cells were 2.04 times more likely to die from their disease (95% CI, 1.35-3.07) than those with one or more T cells (P < 0.01; median survival 1.67 years versus 3.79 years). … [ However,] Although peritumoral presence of CD3+/CD8+ T cells was a significant survival factor in the univariate analyses limited to patients with advanced-stage cancer, it did not emerge as a significant factor in multivariate analyses.

This sort of study can’t definitively answer the question of whether there’s any significant control of clinically detectable cancers. For example, since there’s evidence that chemotherapy success is linked to the immune response, perhaps the immune parameters here are actually measuring the efficacy of chemotherapy, and the immune response is ineffective on its own. Still, it’s certainly encouraging — it suggests that the immune system really is a potential partner in treatment of many tumors, and maybe gives a pointer to which tumors are more or less likely to respond to treatment.

  1. Han, L.Y., Fletcher, M.S., Urbauer, D.L., Mueller, P., Landen, C.N., Kamat, A.A., Lin, Y.G., Merritt, W.M., Spannuth, W.A., Deavers, M.T., De Geest, K., Gershenson, D.M., Lutgendorf, S.K., Ferrone, S., Sood, A.K. (2008). HLA Class I Antigen Processing Machinery Component Expression and Intratumoral T-Cell Infiltrate as Independent Prognostic Markers in Ovarian Carcinoma. Clinical Cancer Research, 14(11), 3372-3379. DOI: 10.1158/1078-0432.CCR-07-4433[]
July 27th, 2008

Reduced immunity in the elderly

TRegs (Red with green nuclei) in skin

It’s a well-known, but poorly-understood, observation that the elderly are more susceptible to disease; their immune system isn’t as effective. Not only are older people (and animals) at more risk of disease like influenza, they are also at risk of having a reactivation of some of the many chronic infections we pick up during our lives. And, of course, cancer is more common as one ages, as well.

There are lots of reasons why this might be the case, and likely most of them are factors. A recent paper supports a new and exciting possibility, offering evidence that part of the reason is an overactive immune response: An increase in regulatory T cell activity and numbers is partially responsible for the dampened defense against pathogens.1

Regulatory T cells are a critical component of the immune response; immune responses to pathogens are by their nature destructive, and TRegs are an important way of limiting the destruction. People and animals lacking TRegs have terrible (and usually rapidly fatal) autoimmune disease.

The development and regulation of TRegs themselves is still not all that well understood, but it’s generally accepted that there are at least two sources of TRegs: “Central” TRegs, that develop in the thymus as purpose-built regulatory cells, and “peripheral” TRegs, that are converted T cells that were originally from other lineages, like ninjas converting to zen Buddhism. Perhaps somewhere along this process there’s a small imbalance that, eventually, tilts the balance toward a gradual slow accumulation of TRegs.

In any case, TRegs do seem to be relatively more abundant in older animals. However, it wasn’t clear whether these accumulated cells are actually functional — after all, it’s known that many of the immune lineages in older animals have reduced function, perhaps the TRegs are also less effective, and you need more of them for that reason.

LeishmaniaLages et al demonstrated that this is not the case — in fact, TRegs in old mice are if anything more effective than those from young mice, in terms of suppressing immune responses.  Even more interestingly — and this may translate into the clinic at some point — these TRegs are a part of the problem; reducing TRegs reduced disease, in at least some diseases. They used a model of Leishmania infection2 which is more severe in old mice than in young. Depleting the number of TRegs in older mice, though, made them much more resistant to disease — probably as resistant as the young mice.3

This is probably not universal, but it’s an exciting possibility. Perhaps things like shingles (reactivation of latent varicella-zoster virus) are actually manifestations of overactive TRegs, rather than an underactive effector response. Since we are much better at destruction of a response than creating one, this offers a much easier handle for treatment. I look forward to seeing followup on this.

By the way, my wife instructs me that we will be going on vacation for the first half of August, so I likely won’t be updating this blog as regularly for a bit. I aten’t dead.

  1. Functional Regulatory T Cells Accumulate in Aged Hosts and Promote Chronic Infectious Disease Reactivation. Celine S. Lages, Isabelle Suffia, Paula A. Velilla, Bin Huang, Gregg Warshaw, David A. Hildeman, Yasmin Belkaid and Claire Chougnet.  The Journal of Immunology, 2008, 181: 1835-1848.[]
  2. Leishmania is a trypanosome parasite, transmitted by sand flies, that causes a variety of unpleasant diseases in people, as well as other species.[]
  3. I don’t think there’s a direct comparison of young vs. old depleted of TRegs, which is why I say “probably”[]
July 24th, 2008

HIV and immunodominance, again

HIV modelOne of the reasons HIV can persist in infected people, in spite of a powerful and effective cytotoxic T cell immune response against the virus, is that the virus mutates rapidly. Because CTL each only target a short stretch of the genome (say, 9 amino acids) and a single amino acid change may allow the virus to escape recognition by a particular CTL clone, it may not take long for a viral mutant to arise that is invisible to the dominant CTL population in a particular individual.

It’s been suggested that immunodominance is one of the factors that determines the rate at which HIV can escape from a particular immune response. In a highly immunodominant response, most of the CTL specific for the virus all target a single peptide epitope. If the virus manages to mutate this peptide, it has escaped the bulk of the immune response, and the new mutant virus can explode unchecked (until a new CTL response arises).

On the other hand, if the CTL response isn’t dominated by a single epitope — that is, if the response is broad, targeting many peptides — the virus has to simultaneously mutate several regions of its genome, which is exponentially less probable than single mutations. On the other hand, typically a broad CTL response would have fewer cells attacking each individual epitope, so perhaps the overall control might not be as good during the peak response.

Directly analyzing these questions is a huge task. Identifying CTL epitopes isn’t easy even when there are a few of them; looking at HIV changes isn’t easy even when there’s a concrete starting point; and in an infected patient you would need to track CTL recognition and HIV changes at short intervals, and over a long period; a task even more complicated by all the variables of a massively diverse starting population, replication and fitness issues … just an overwhelming problem.

A paper in PLoS Computational Biology1 tries to model these possibilities.

Organic computer
Organic computer

I don’t feel competent to assess the model here, in any technical way. As with most bench scientists, I suspect, I’m at best cautious, and more often outright skeptical, about computer modeling of biological problems, especially when they’re as complex as these ones. For example, the authors list a dozen parameters they took from various sources — maximal CTL proliferation rate, natural death rate of CD4 cells, and so on. (Not to mention assumptions that aren’t explicit.) Lots of these parameters are offered as single numbers: 0.01 d-1 as the death rate of CD4 target cells. Naturally, each of those numbers would have error bars in the original, and probably weren’t all measured in comparable ways, and so on. I doubt anyone would be much surprised if any of those parameters was off by 50% or more; perhaps much more. Cumulatively, how much error is in there? Or do we count on having all the errors more or less cancel out?

Still (again, probably typical of bench scientists) I’m always intrigued by computer modeling, and I’m willing to accept that modeling might well open up a problem enough to suggest new approaches. Encouragingly, the model here fits observation reasonably well; escape variants pop up intermittently over a couple of years, CTL clones decline as their targets mutate away. The model looks rather similar, in some ways, to the study a couple of years ago on a pair of identical twins infected with HIV. 2

One interesting observation from the model is that escape variants are mostly all present within a couple of years of infection, though they may later reappear as if they are new as CTL pressure varies:

After about two years, the virus population stabilizes as the ‘easy’ escapes have been done, the replicative capacity is partially restored and only few escapes are expected to appear later during infection. … If an escape is found to happen late it does not necessarily mean that it had not been selected earlier during infection

An observation and prediction arising from this is that CTL may actually become more effective later in infection (all other things being equal, of course), as further attempts by the virus to escape bump up against more severe fitness costs for the virus.

Another observation is the effect of immunodominance. A highly immunodominant CTL response results in more escape variants, as predicted by other studies. However, since escape variants are usually less fit than the Platonic essence of HIV, even though there are more cells infected with virus, that virus is less fit; so even a highly immunodominant response may be surprisingly (to me) effective, by forcing the virus into an unfit state.

A higher degree of immunodominance leads to more frequent escape with a reduced control of viral replication but a substantially impaired replicative capacity of the virus.

Presumably (I don’t think the authors of this model addressed this directly) the effectiveness (quantitatively) of an immunodominant response would depend on the fitness cost — in other words, an immunodominant response that could be escaped with only a small loss in fitness would be ineffective, whereas one that forces a big hit in fitness to escape would be effective. That would reflect what we know about the connection between elite suppressors and particular MHC class I alleles associated with immunodominant epitopes.

I’ve been rather unimpressed by highly immunodominant responses to HIV, but if this model is accurate, such responses may not as bad as I thought; though broad responses are probably still more desirable.

  1. Althaus CL, De Boer RJ (2008) Dynamics of Immune Escape during HIV/SIV Infection. PLoS Comput Biol 4(7): e1000103. doi:10.1371/journal.pcbi.1000103[]
  2. Draenert R, Allen T, Liu Y, Wrin T, Chappey C, et al. (2006) Constraints on HIV-1 evolution and immunodominance revealed in monozygotic adult twins infected with the same virus. J Exp Med 203: 529-39[]
July 21st, 2008

On T cell quality

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:

July 20th, 2008

Quantity vs quality again

PlasmodiumAll 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

Plasmodium & red blood cellsPierre 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. []
July 17th, 2008

On the transmission of influenza

We detected influenza virus RNA in the exhaled breath of 33% of subjects with laboratory-confirmed influenza.  …  In the present study, we measured generation rates of <192 and 1200 influenza virus RNA copies per hour for subjects with detectable influenza virus RNA in their breath. … This implies that the quantum generation rate we measured in our subjects is 10-fold higher than the Rudnick and Milton estimate for a “superspreader”.1

—  Fabian P, McDevitt JJ, DeHaan WH, Fung ROP, Cowling BJ, et al. (2008) Influenza Virus in Human Exhaled Breath: An Observational Study. PLoS ONE 3(7): e2691. doi:10.1371/journal.pone.0002691

  1. However, … the generation rate of infectious viruses may be as little as 1/300th of the total number determined by PCR and the resulting estimates of quantum generation rate (<0.64 to 4 quanta/h) from our data are lower than those estimated for a superspreader by Rudnick and Milton.”[]
July 16th, 2008

Viral/cancer immune evasion

Human papillomavirus capsidTwo themes I’ve repeatedly raised in this blog are viral immune evasion, and tumor immune evasion. There are similarities between them (both viruses and tumors are attacked by the same components of the immune system) and differences (a virus species comes from a common ancestor, so each member of the virus species will use the same mechanism; while tumors all arise de novo, and no matter how similar they appear clinically or histologically they have had to discover their own pathway to immune evasion).

The differences become less obvious when we talk about virus-induced tumors, and especially when it turns out that part of the viral tumorigenesis process includes immune evasion.

Most of the viruses that are good at MHC class I immune evasion are relatively large: Herpesviruses, poxviruses, even adenoviruses and retroviruses have reasonably good-sized genomes (as viruses go, which isn’t very far). Smaller viruses still have immune evasion functions, but they tend to focus more on cytokines, like interferons. This is only a rule of thumb, though, and there are a number of papers over the past years pointing at immune evasion of MHC class I by fairly small viruses. In particular, papillomaviruses (which are fairly small viruses — only about 8000 base pairs in their genome, and maybe 7 or 8 proteins) have  been implicated in this.

As far as I know the original observation was in 2002;1 the original obervation, with bovine papillomavirus, was followed and extended (mostly by the same group, Campo’s) with evidence that the viral protein E5 binds to MHC class I and prevents its expression. 2

Papillomavirus replication
Papillomavirus genome (red) and E4 protein (green);
nuclei in blue. From ref. 3

Papillomaviruses are tumor viruses, and the high-risk papillomaviruses are responsible for most cases of cervical carcinoma. The virus proteins involved in the tumorigenesis are E6, E7, and (you guessed it) E5. This at first glance seems to suggest that E5 might contribute to tumor progression by blocking antigen presentation — and that may well be the case; but the problem is that E5 often isn’t present by the time tumors are clinically relevant. That is, you need E6 and E7 in a papillomavirus-caused cancer, but while E5 may help it get going it’s not necessary for continued cancer development.

That becomes  easier to understand when we take into account a series of papers that showed that high-risk papillomaviruses attack MHC class I with their E7 protein as well. 4 The E7 protein apparently regulates MHC expression at the RNA level, so that MHC class I (and other components of the pathway) are not synthesized at all. (This is also what’s seen with some ‘tumorigenic’ adenoviruses, which don’t have the E3 protein that ‘normal’ adenoviruses use to downregulate MHC class I.)

Both of these proteins, E5 and E7, immune evasion functions are probably ‘designed’  for immune evasion in a natural virus infection — cancers are by no means a normal outcome for papillomaviruses, and even for high-risk papillomaviruses cancers are a rare, aberrant, and probably dead-end outcome. Even though the cancers are abnormal, though, they probably benefit from the immune evasion the virus uses normally.

I don’t know how much of this has been tested, but I speculate that the E5 probably helps early on in tumor development — letting the tumor progress a little further while sheltered from the immune system. I’m a little dubious how completely MHC class I can be turned off by transcription factors like E7, and I suspect that there would be a quite a bit of residual MHC class I left over, that E5 could then soak up. Perhaps later in tumor development, then, as the tumor has undergone repeated mutagenesis and selection by the immune system, E5 is no longer necessary and can be lost with impunity.

  1. Down-regulation of MHC class I by bovine papillomavirus E5 oncoproteins. Ashrafi GH, Tsirimonaki E, Marchetti B, O’Brien PM, Sibbet GJ, Andrew L, Campo MS. Oncogene. 2002 Jan 10;21(2):248-59. []
  2. E5 protein of human papillomavirus 16 downregulates HLA class I and interacts with the heavy chain via its first hydrophobic domain. Ashrafi GH, Haghshenas M, Marchetti B, Campo MS. Int J Cancer. 2006 Nov 1;119(9):2105-12.
    Ashrafi GH, Brown DR, Fife KH, Campo MS. Down-regulation of MHC class I is a property common to papillomavirus E5 proteins. Virus Res. 2006 Sep;120(1-2):208-11. []
  3. Doorbar, Clinical Science (2006) 110, (525–541) []
  4. Georgopoulos NT, Proffitt JL, Blair GE. (2000). Transcriptional regulation of the major histocompatibility complex (MHC) class I heavy chain, TAP1 and LMP2 genes by the human papillomavirus (HPV) type 6b, 16 and 18 E7 oncoproteins. Oncogene 19: 4930-4935.
    Li H, Ou X, Xiong J, Wang T. (2006). HPV16E7 mediates HADC chromatin repression and downregulation of MHC class I genes in HPV16 tumorigenic cells through interaction with an MHC class I promoter. Biochem Biophys Res Comm 349: 1315-1321[]
July 15th, 2008

Tumor antigens and affinity

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.

<1 0.69 7.89
1-2 2.07 5.26
2-4 8.28 5.71
4-16 25.52 19.14
16-256 55.17 45.92
256-65536 8.28 16.01
>65536 0 0.06

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.

Tumor epitopes, IC50 nM

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 []
July 14th, 2008

Unconventional antiviral immunity

Mouse polyomavirus
Mouse polyomavirus

When we talk about anti-viral T cells, we’re usually talking about cytotoxic T lymphocytes (CTL) that recognize a peptide in association with class I major histocompatibility complexes (MHC class I). MHC class I is extremely polymorphic; there are many hundreds of different MHC class I alleles.

At any rate, that’s true for the classical MHC class I genes. But as well as classical MHC class I, there are also (you’ll never guess) non-classical MHC class I genes. Lots of them. For the most part we don’t really know quite what they do. Typically they’re not very polymorphic, compared to classical class I alleles. Since the major hypotheses explaining which there are so many classical MHC class I alleles involve protection against pathogens, this might hint that non-classical MHC class I don’t behave like classicals — either they don’t protect against pathogens at all, or they do so in a very different way.

Both of these possibilities are true for various non-classicals. For example, some of the non-classical MHC class I genes act as ligands for natural killer (NK) cells, which do recognize pathogens but do so in a very different manner than do CTL. Other non-classicals seem to have little to do with pathogen recognition — there are iron-binding molecules, neuronal molecules, and so on.

But a paper from Aron Lukacher’s lab1 suggests that at least some non-classical MHC class I genes can act much like the classical genes, which has interesting implications for anti-viral vaccines.

There are mice that have no classical MHC class I genes at all. (Hidde Ploegh’s lab generated them, by crossing mice lacking the H-2K MHC class I gene with those lacking the H-2D MHC class I gene. Since these genes are side by side, the frequency of crossing-over is very low, and Ploegh got the mice by pure brute force, examining thousands of mice to find the single mouse that had crossed over appropriately to generate a double knockout. I have always felt a mixture of admiration and sympathy for the post-doc assigned that project.) These mice actually do reasonably well as far as controlling viruses — the immune system is highly redundant, and there are many antiviral systems in play – so it wasn’t a huge surprise to find that the classical MHC class I-less mice could control infection with polyomavirus quite well.

What was a surprise was that eliminating CD8 T cells also eliminated polyomavirus control. 2 If there’s no classical MHC class I, what could the CD8 T cells be recognizing? The answer turned out to be, non-classical MHC class I. 3 WIth a bit more mapping, it seems that the CD8 T cells were recognizing a non-classical MHC class I gene called “Q9” (catchy, eh?).

Qa-2 non-classical MHC class IQ9 is a member of the Qa-2 family, which I have always believed were involved in natural killer cell recognition,4 although in retrospect I see that there’s evidence that CD8 T cells can recognize them.5  That’s a picture of Qa-2 off to the right (click for a larger version), and there are more images of it in my previous post comparing the different types of MHC.

What’s more, the CD8 cells recognize a really pretty conventional epitope. Classical MHC class I alleles present peptides that are around 9 amino acids long, while various non-classical proteins present anything from glycolipids to nothing at all. The Q9 complex turned out to present a rather boring 9-amino acid peptide6 that, to my eye, could have been cheerfully presented by any of a hundred classical MHC class I complexes.

So basically, this is a very conventional-looking anti-viral response, but directed against an unconventional MHC. There are occasional hints in the literature that this might be more than a one-off,7  though it’s not clear how common it is.  (This unconventional response might normally be drowned out by the conventional response, so that it’s hard to see unless you look in the MHC class I knockout mice.)

Why is this interesting (apart from the obvious point that anything remotely to do with MHC is intrinsically interesting, of course)? As I said, classical MHC is highly polymorphic, while non-classical MHC is not. There are only a handful of Q9 alleles known. If you made an antiviral vaccine with a peptide that binds to Q9, it should work in most mice, whereas if you make a similar vaccine directed to classical MHC class I, you’d need to tailor the vaccine to each individual mouse strain. Humans don’t have Q9 (the non-classical MHC are much less conserved between species than are the highly conserved classicals), but they do seem to have analogous proteins that are non-polymorphic and that may be able to work in antiviral contexts.

There’s a couple of other interesting things about this (Q9 binds to a wide range of peptides,8 for example, which reminds me of an existential question I had that was prompted by chicken MHC; and Lukacher makes the interesting suggestions that polyomavirus and Q9 might be the product of specific co-evolution), but I have a pair of intrepid little boys who camped out in our back yard for the first time last night, and they are proudly hiking the five feet to the house to tell me all about it now.

  1. Swanson, P.A., Pack, C.D., Hadley, A., Wang, C., Stroynowski, I., Jensen, P.E., Lukacher, A.E. (2008). An MHC class Ib-restricted CD8 T cell response confers antiviral immunity. Journal of Experimental Medicine, 205(7), 1647-1657. DOI: 10.1084/jem.20080570[]
  2. At any rate, the virus titre went up something like 50-fold.[]
  3. A clue came from their previous finding the mice lacking β2-microglobulin were highly susceptible to polyomavirus infection. Many, though not all, non-classical MHC class I proteins need β2-m to form a normal structure.[]
  4. The nonclassical major histocompatibility complex molecule Qa-2 protects tumor cells from NK cell- and lymphokine-activated killer cell-mediated cytolysis. Chiang EY, Henson M, Stroynowski I. J Immunol. 2002 Mar 1;168(5):2200-11. []
  5. Chiang, E.Y., and I. Stroynowski. 2005. Protective immunity against disparate tumors is mediated by a nonpolymorphic MHC class I molecule. J. Immunol. 174:5367-5374.
    Correction of defects responsible for impaired Qa-2 class Ib MHC expression on melanoma cells protects mice from tumor growth. Chiang EY, Henson M, Stroynowski I. J Immunol. 2003 May 1;170(9):4515-23. []
  7. For example, Braaten, D.C., J.S. McClellan, I. Messaoudi, S.A. Tibbetts, K.B. McClellan, J. Nikolich-Zugich, and H.W. Virgin. 2006. Effective control of chronic {gamma}-herpesvirus infection by unconventional MHC Class Ia-independent CD8 T cells. PLoS Pathog. 2:e37.[]
  8. Promiscuous antigen presentation by the nonclassical MHC Ib Qa-2 is enabled by a shallow, hydrophobic groove and self-stabilized peptide conformation.  He X, Tabaczewski P, Ho J, Stroynowski I, Garcia KC.  Structure. 2001 Dec;9(12):1213-24.[]
July 10th, 2008

Viral immune evasion: A theme continues to emerge

T cells & herpes simplex
CTL (green) and HSV-infected cells (red)
(from Akiko Iwasaki)

Last time I talked about herpesvirus immune evasion of cytotoxic T lymphocytes, I cautiously wondered if there might be a theme emerging: Do these genes mainly help the virus with latent infection?

Immune evasion of CTL seems to be pretty much universal among the millions of different herpesvirus species — at least, as far as I know, in every case where people have looked for it, the virus has some way of blocking antigen presentation. Although other virus families also block antigen presentation (HIV, some poxviruses, and human adenoviruses are probably the best known instances), immune evasion isn’t as universal among those other families.

For example, although human adenoviruses mostly have immune evasion function, adenoviruses of other species do not (as far as we can tell); and for that matter not even all human adenoviruses have the ability to block antigen presentation. What’s more, there is a trend for those non-herpesvirus viruses that do evade CTL, to also establish long-term latent or persistent infection.

A recent paper from Frank Carbone’s lab1 offers a little more, indirect, support for this theme. They asked what CTL actually do to herpes simplex virus in the initial infection.

HSV-infected ganglionWe usually blithely call CTL “antiviral lymphocytes”, but what exactly does that mean for specific virus infections? For example, I’ve previously pointed out experiments that show that CTL have more than one way of clearing away virus infections — they can use cytokine secretion as well as, or instead of, cell lysis, as their weapon, which opens up the opportunity for activity over a broad range rather than one cell at a time. In another example, Luis Sigal’s lab has shown that CTL can protect against extromelia (mousepox) infection at a very early stage, by blocking the virus’s spread from the skin to the liver, cutting them off at the bottleneck of the lymph through which the virus intially spreads.

On the other hand, herpes simplex virus often seems to do just fine even when CTL are present. The virus sets up an initial infection in the skin, and rapidly tracks up through neurons to ganglia, where it sets up a latent infection. By the time CTL are up and running, the virus is comfortably snuggled down in the neuron, shutting down all its protein expression to the point where CTL don’t see it very well. Even if you have an active CTL response already, the virus seems to be able to get in to the neurons and set up latency anyway.

So what do CTL do to herpes simplex? Carbone’s lab set up mice with and without specific anti-HSV CTL, and infected them with the virus. As you’d expect (and as has been shown lots of times in the past) the CTL markedly reduced the amount of virus replication and shedding, but did not prevent the virus from setting up a latent infection.

Though the presence of herpes-specific effector CD8+ T cells early during viral challenge attenuated the primary infection and prevented the development of disease, these cells failed to block the skin to nervous system transmission of the virus, and hence substantial latent infections were established in the face of this CTL immunity.

(My emphasis.) How come? Part of the answer seems to be that the CTL didn’t respond quite fast enough. 2 Virus infects the skin, replicates, and moves up into neurons in about 24 hours. (If they surgically removed the infected skin prior to 24 hours after infection, neurons weren’t infected; if they removed the infected region more than 24 hours after infection, neurons were infected.) CTL, on the other hand, move into the infected region of skin within about 15 hours of infection. At this point the CTL start to shut down virus replication; but the window of opportunity, as you can see, is very narrow. The CTL need to shut down the virus in the skin very rapidly, and to very low levels, within just a few hours of entering the site of infection.

In fact, if you start off with a relatively small amount of virus, then CTL can shut the replication down enough to make a difference.  It’s mainly when there’s a lot of virus to start with that the CTL can’t get the virus down under some threshold level that allows efficient latent infection:

Thus, virus-specific CTL can reduce the average viral copy number of the residual latent infection, but this is only achievable when a substantial attenuation of the skin infection is observed.

Trigeminal ganglionThere are a bunch of fascinating points that arise from this work. First, it helps account for the fact that superinfection with herpes simplex is actually quite rare — that is, if you’re infected with HSV already, then you’re unlikely to get re-infected with a second virus. Normal exposure to HSV probably is at a very low level, with only a handful of virions entering the skin; it’s more like the low-range infection in Carbone’s experiments than the high-range, where they put in some 10 million virions, and at the low range, CTL can move in and check the initial infection fast enough to make a difference.

Second, a critical point about these experiments is that they were done in the absence of CTL evasion. That’s because there experiments were done in mice, and the HSV immune evasion molecule ICP47 doesn’t work in mice, as opposed to in humans.

One of the puzzling things about immune evasion genes, to me, has been how ineffectual they seem to be in authentic infections. But these experiments suggest if your interest is in establishing latency, then immune evasion doesn’t need to be hugely effective: It just needs to keep the window open a crack for a few more hours, letting the virus replicate through the first wave of CTL invasion. If ICP47 can hold off the CTL for an extra 8 hours, then it’s probably done its job, allowing HSV to set up a latent infection and thus reactivate and infect new hosts on and off over the next 60 or 70 years.

So, even though this paper really didn’t look at immune evasion per se, I think it does offer some support for the concept that (for herpesviruses, anyway) immune evasion really isn’t for the acute infection at all.  Instead, it’s a mechanism to help the virus establish latent infection.

  1. Wakim, L.M., Jones, C.M., Gebhardt, T., Preston, C.M., Carbone, F.R. (2008). CD8+ T-cell attenuation of cutaneous herpes simplex virus infection reduces the average viral copy number of the ensuing latent infection. Immunology and Cell Biology DOI: 10.1038/icb.2008.47[]
  2. I wonder whether they miigh have seen a faster response if they had started with skin-specific T cells, though.[]