PubMed

There are at least five problems with this paper:

First, the authors simply assume that the pro-e-cigarette tweets are wrong and need their corrective input. What if users are right to be positive? The authors have not demonstrated any material risk from vapour aerosol. To the extent that there is evidence of exposure the levels so low as to be very unlikely to be a health concern. The presence of a hazardous agent does not in itself imply a risk to health, there has to be sufficient exposure to be toxicologically relevant.

Second, they have also not considered what harmful effect that their potentially misleading 'health education messages' may have. For example, by exaggerating a negligible risk they may be discouraging people from e-cigarette use, and potentially causing relapse to smoking and reducing the incentive to switch - thus doing more harm than had they not intervened. We already know the vast majority of smokers think e-cigarettes are much more dangerous than the toxicological profile of the aerosol suggests - see National Cancer Institute HINTS data. The authors' ideas would aggravate these already highly damaging misperceptions of risk.

Third, as so often happens with tobacco control research, the authors make a policy proposal for which their paper comes nowhere close to providing an adequate justification.

Public health and regulatory agencies could use social media and traditional media to disseminate the message that e-cigarette aerosol contains potentially harmful chemicals and could be perceived as offensive.

They have not even studied the effects of the messages they are recommending on the target audience or tested such messages through social media. If they did, they would discover that users are not passive or compliant recipients of health messages, especially if they suspect they are wrong or ill-intentioned. Social media creates two-way conversations in which often very well-informed users will respond persuasively to what they find to be poorly informed or judgemental health messages. Until the authors have tested a campaign of the type they have in mind, they have no basis for recommending that agencies spend public money in this way.

Fourth, the authors suggest that users should be warned by public health agencies that "e-cigarette aerosol ... could be perceived as offensive". If there were warnings from public health and regulatory agencies about everything that could be perceived as offensive by someone, then we would be inundated with warnings. This is not a reliable basis or priority for public health messaging. Given the absence of any demonstrable material risk from e-cigarette aerosol, the issue is one of etiquette and nuisance. This does not require government intervention of any sort. Vaping policy in any public or private place should be a matter for the owners or managers, who may not find it offensive nor wish to offend their clientele. It is not a matter for legislators, regulators or health agencies.

Fifth (and with thanks to Will Moy's tweet), the work is pointless and wasteful. Who cares what people are saying on twitter about e-cigarettes and secondhand aerosol exposure? Why is this even a subject worthy of study and what difference could it make to any outcomes that are important for health or any other policy? What is the rationale for spending research funds on this form of vaguely creepy social media surveillance?

Updated 21-Jan-17 with fifth point.

The Editorial “Trump’s vaccine-commission idea is biased and dangerous” in Nature 2017 Jan 17;541(7637):259 is debatable. At least one article published by the Nature Publishing Group, in Nature Reviews Disease Primers 2016;2:16086 [1], promoting mass human papillomavirus (HPV) vaccination of girls 9-13 years of age and teenage boys at the cost of >$50 million for every 100,000 adolescents in the name of cervical cancer prevention is equally biased and dangerous. Medical journal censorship of dissenting data and opinions has suppressed the facts that the benefits of mass HPV vaccination are uncertain and the risks are substantial at great cost to society.

References:

1. https://www.ncbi.nlm.nih.gov/pubmed/27905473

Sin Hang Lee shlee01@snet.net Milford Molecular Diagnostics Laboratory Milford,CT

A link to this discussion should be here: http://www.chris-anthony.co.uk/myresearch.html#pqqvit

This paper is missing some highly relevant references from the Kieffer lab, including recent studies that establish the requirement for insulin in the anti-diabetic actions of leptin.

This study has the potential to open a new field to explore like viral communications or signaling in viruses.Someway down the line there is possibility that one will know about the evolution of communication system or the signaling system between two organisms.This study has therapeutic potential also as viruses are responsible for many fatal human diseases like cancer, AIDS etc.If one understand the communication or signaling system between viruses then therapeutics can be developed to block this communication or signaling system which will help in preventing the viral diseases.More understanding of viral communication or signaling system may be used in biotech industries to produce cheaper and useful bioproducts as viruses replicate very fast. Further viruses might use different signaling molecules to communicate with each other for different activities.Signaling system might also help in the classification of viruses. Anyway this study is just the beginning and many more mysteries about viruses might be solved in the near future.

This article was apparently published twice by mistake. For the non-retracted version, see https://www.ncbi.nlm.nih.gov/pubmed/23507683

There has been a call for peer commentary on the Editorial/Discussion Paper (Slotnick SD, 2017) in the journal Cognitive Neuroscience (due February 13th, 2017). The Editorial/Discussion Paper, Commentaries, and an Author Response will be published in an issue of Cognitive Neuroscience later this year.

(Original comment found at: https://elifesciences.org/content/6/e17044#disqus_thread)

Response to “Replication Study: Discovery and preclinical validation of drug indications using compendia of public gene expression data”

Atul J Butte, Marina Sirota, Joel T Dudley

We represent three of the key authors of the original work.

In October 2013, we were pleased to see that our original 2011 publication (Sirota et al., 2011) was selected as one of the top 50 influential cancer studies selected for reproducibility. Our initial impression, probably like most investigators reading this letter, was that such recognition would be a mixed blessing for us. Most of our work for this paper was conducted in 2009, 4 years prior to us being approached. We can see now that this reproducibility effort is one of the first 10 to be completed, and one of the first 5 to be published, more than 3 years later. The reproducibility team should be commended on their diligence to repeat experimental details as much as possible.

The goal of the original study was to evaluate a prediction from a novel systematic computational technique that used open-access gene-expression data to identify potential off-indication therapeutic effects of several hundred FDA approved drugs. We chose to evaluate cimetidine based on the biological novelty of its predicted connection to lung cancer and availability of local collaborators in this disease area.

The key experiment replicated here involved 18 mice treated with three varying doses of cimetidine (ranging from 25 to 100 mg/kg) administered via intraperitoneal injection daily to SCID mice after implantation of A549 human adenocarcinoma cells, along with 6 mice treated with doxorubicin as a positive control, and 6 mice treated only with vehicle as a negative control. The reproducibility team used many more mice in their experiment, but tested only the highest dose of cimetidine.

First, it is very important to clearly note that we are truly impressed with how much Figure 1 in the reproducibility paper matches Figure 4c in our original paper, and this is the key finding that cimetidine has a biological effect between PBS/saline (the negative control) and doxorubicin (the positive control). We commend the authors for even using the same colors as we did, to better highlight the match between their figure and ours.

While several valid analytic methods were used on the new tumor volume data, the analysis most similar to the original was the t-test we conducted on the measurements from day 11, with 100 mg/kg cimetidine compared to vehicle control. The new measurements were evaluated with a Welch t-test yielding t(53) = 2.16, with p=0.035. We are extremely pleased to see this raw p-value come out from their experiment.

However, the reproducibility team then decided to apply a Bonferroni adjustment, resulting in a corrected p=0.105. While this Bonferroni adjustment was decided a priori and documented (Kandela et al., 2015), we fundamentally do not agree with their approach.

The reproducibility team took on this validation effort by starting with our finding that cimetidine demonstrated some efficacy in the pre-clinical experiments. However, our study did not start with that prediction. We started our experiments with open data and a novel computational effort. Readers of our original paper (Sirota et al., 2011) will see that we started our study much earlier in the process, with publicly-available gene expression data on drugs and diseases, and computationally made predictions that certain drugs could be useful to treat certain conditions. We then chose cimetidine and lung adenocarcinoma from among the list of significant drug-disease pairs for validation. This drug-disease pairing was statistically significant in our computational analysis, which included the formal evaluation of multiple-hypothesis testing using random shuffled data and the calculation of q-values and false discovery rates. These are commonly used methods for controlling for the testing of multiple hypotheses. Aside from the statistical significance, local expertise in lung cancer and the availability of reagents and A549 cells and mouse models in our core facilities guided the selection. We then chose an additional pairing that we explicitly predicted (by the computational methodology) would fail. We again used cimetidine and found we had ACHN cells that could represent a model of renal cancer. Scientists will recognize this as a negative control.

At no point did we feel the comparison of cimetidine against A549 cells had anything to do with the effect of cimetidine in ACHN cells; these were independently run experiments. The ACHN cell test was to test the specificity of the computational process upstream of all of this; it had nothing to do with our belief in cimetidine in A549 cells. Thus, we would not agree with the replication team’s characterization that these were all multiple hypotheses being validated equally, and thus merited a common adjustment of p-values. As described above, we corrected for the multiple hypothesis testing earlier in our process, at the computational stage. We never expected the cimetidine/ACHN experiment to succeed when we ran it. Similarly, our test of doxorubicin in A549 cells was performed as a positive control experiment; we fully expected that experiment to succeed.

In email discussion, we learned the replication team feels these three hypotheses were tested equally, and thus adjusted the p-values by multiplying them by 3. We are going to have to respectfully “agree to disagree” here.

We note some interesting results of their adjustments, such as the reproducibility team also not finding doxorubicin to have a statistically significant effect compared to vehicle treated mice. Again, the Welch’s t-test on this comparison yielded p=0.0325, but with their Bonferroni correction, this would no longer be deemed a significant association. Doxorubicin has been used as a known drug against A549 cells for nearly 30 years (Nishimura et al, 1989), and our use of this drug was only as a positive-control agent.

Figure 3 was also very encouraging, where we do see a significant effect from the original and reproduced studies, and the meta-analysis together.

In the end, we want to applaud replication efforts like this. We do believe it is importance for the public to have trust in scientists, and belief in the veracity of our published findings. However, we do recommend replication teams of the future to choose papers in a more impactful manner. While it is an honor for our paper to be selected, we were never going to run a clinical trial of cimetidine in lung adenocarcinoma, and we cannot see any such protocol being listed in clinicaltrials.gov. Our publication was more towards demonstrating the value of open data, through the validation of a specific computational prediction. We suggest that future replication studies of pre-clinical findings should really be tailored towards those most likely to actually be heading into clinical trials.

References

Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J, Butte AJ. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med. 2011 Aug 17;3(96):96ra77. doi: 10.1126/scitranslmed.3001318.

Kandela I, Zervantonakis I; Reproducibility Project: Cancer Biology. Registered report: Discovery and preclinical validation of drug indications using compendia of public gene expression data. Elife. 2015 May 5;4:e06847. doi: 10.7554/eLife.06847.

Nishimura M, Nakada H, Kawamura I, Mizota T, Shimomura K, Nakahara K, Goto T, Yamaguchi I, Okuhara M. A new antitumor antibiotic, FR900840. III. Antitumor activity against experimental tumors. J Antibiot (Tokyo). 1989 Apr;42(4):553-7.

(Original comment in full found at: https://elifesciences.org/content/6/e18173#disqus_thread)

Response to: “Replication Study: The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors”

Irving Weissman for the authors of "The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors"

Our original paper by Willingham and Volkmer et al in PNAS reported the result of experiments testing the hypothesis that CD47 might be expressed and demonstrate dominant ‘don’t eat me’ functions on human solid cancers, as well as our previously described studies with human leukemias and lymphomas and mouse leukemias. The study included primarily experiments on primary patient solid cancers with minimal passage as xenografts in immune deficient mice tested in vitro and as xenografts in the mice lacking adaptive immune system T, B, and NK cells, but possessing all other bone marrow derived innate immune system cells such as macrophages . We included one experiment on a long passaged mouse breast cancer line transplanted into syngeneic immunocompetent FVB mice. The Replication Study by Horrigan et al in eLife reports the results of efforts to repeat the experiments on the passaged mouse breast cancer line, but none of the experiments on human primary and minimally passaged cancers of several different solid tumor types, either in vitro or as xenografts in mice in which the CD47 ‘don’t eat me’ signal was blocked with monoclonal antibodies.

When we were requested to participate in a replication study of our paper entitled “The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors” we agreed, but were worried that we had spent years developing the infrastructure to obtain human cancers from de-identified patients, found ways to transplant them into immune deficient mice, and limited our studies to human cancers within 1 to less than 10 transplant passages in these mice. Our major objective in the study was to test whether the CD47 molecule was present on these human solid tumors, if it acted as a ‘don’t eat me’ signal for mouse and human macrophages, and whether these tumors in immune deficient mice were susceptible to blocking anti-CD47 antibodies. This was a scientific paper to answer these questions, and not a preclinical study preparatory to human clinical trials.

To our surprise, our study verified on all tested human cancers that they express CD47, perhaps the first cancer gene commonly expressed on all cancers; and it is a molecule which provides a ‘don’t eat me’ function; and we showed that blocking that function led to tumor attack by macrophages.

Unfortunately, the independent group who accepted the task of replicating our studies did not do a single study with human cancers, or to study the effect of our blocking antibodies to the CD47 tumor cell surface molecules on the phagocytic removal of human cancers.

Horrigan et al did begin, with our help, to replicate the one study we did as a pilot to see if anti-CD47 antibodies that also bind to mouse CD47 would have an effect on a long-transplanted mouse breast cancer line. We and others have found that the exact way you transplant these mouse cancers is critical to achieve engraftment of the cancers in appropriate immune competent mice. As we learned from Dr Sean Morrison, UT Southwestern Childrens Hospital, many cancers won’t grow in mice unless a special type of matrigel is used to support the cells in vitro and in transplant. Without it, transplantation may be sporadic and/or absent. The replication team found their own matrigel, and for reasons unknown to us, could not get reproducible transplantation in their testing. This was picked up in reviews of the paper by eLife referees, including a request for repeating the studies a number of ways, but that did not happen.

There is therefore no study that addresses the title of the paper and its major conclusions: human cancers express CD47 and our studies show that it is a target for therapeutic studies.

Several independent papers since ours have replicated not only our findings, but have extended them to many other human cancers (see below). So replication of our major points have occurred with independent groups.

But we agree that everything we publish, major or minor, central or peripheral, must be replicable. Even in our human tumor studies there were a few outlier cancers that did not diminish growth in the presence of blocking anti-CD47 antibodies.

The beginning of replication is to show experience and competence in the transplantability of the cancer. There are many possible reasons that replication of the basic transplantation of MT1A2 breast cancer cells in syngeneic FVB mice was not replicated in the experiments carried out by Horrigan et al, who got only a fraction of the mice transplanted. These could include the particular matrigel used, a problem with using long passaged cell lines[which may be heterogeneous and altered by the passaging in vitro and in vivo], rather than primary or recent mouse or human cancers. It could be inherent in how Horrigan et al did the experiments. Oddly, the control antibodies did diminish the growth of the MT1A2 cancers in their single experiment. Amongst the reasons concerning the heterogeneity of long passaged cell lines we might cite is that we have discovered two more ‘don’t et me’ molecules on cancers that interact with other receptors on macrophages. Although those papers are submitted, but not yet published, we cannot specify the details lest we endanger their publishability. (Readers who send us a request will receive copies of the papers when published.) Laboratories that study tumors at different transplant passages have often found that variant subsets of cells within the cancer can rapidly outgrow the major population of cells transplanted, and it is common that the successive transplants grow more aggressively in the same strain of mice, even though the name of the tumor is retained. For that reason it is clear that studies on long-passaged tumors may be studying some properties of the passaged cell rather than the original cancer in the individual. There are other possibilities. When the replication study lab interacted with us early on, we offered to do the experiments side by side with them to facilitate technology transfer. Horrigan et al declined. The offer still stands.

Before this paper was published we published other papers demonstrating that CD47 was expressed on all samples of human AML and human NHL tested, usually at a higher level than on the same stage or type of human normal cell. Further, we showed both by genetic manipulation of the expression of CD47 on human cells or the treatment of those cells with blocking antibodies to CD47 that interrupt its interaction with macrophage receptor Sirpα lead to mouse or human macrophages to phagocytose and kill the tumor target cells. We used anti-human antibodies that did not trigger phagocytosis by ‘opsonization’, as the isotype of the antibodies used for blocking were not the isotype that is highly efficient at triggering complement activation or ADCC (activation via Fc receptor of NK lineage killer cells), and we demonstrated that on human lymphomas. [...]

(The comment in full can be found at: https://elifesciences.org/content/6/e18173#disqus_thread)

(Original comment found at: https://elifesciences.org/content/6/e21634#disqus_thread)

Response to: “Replication Study: Melanoma genome sequencing reveals frequent PREX2 mutations"

Lynda Chin and Levi Garraway

We applaud the Reproducibility Project and support its goal to reproduce published scientific results. We also thank Horrigan et al for a carefully executed study, for which we provided reagents and extensive consultation throughout. Their work illustrates the inherent challenges in attempting to reproduce scientific results.

We summarize below the results of Horrigan et al., first in lay terms and then in more scientific detail.

Description for Lay Readers

Briefly, our 2012 paper reported that human melanoma patients often carry mutations in the PREX2 gene. To study the effect of mutations in PREX2, we made modified versions of a commonly used immortalized human melanocyte cell line (called p’mels) and injected them into mice. When mice were injected with cells carrying an irrelevant gene or a normal copy of PREX2 (“control mice”), tumors started to form in about 9 weeks. When mice were injected with cells carried the mutated PREX2 genes (“experimental mice”), tumors began to form after around 4-5 weeks—indicating that mutated PREX2 accelerated tumor formation.

When Horrigan et al. tried to reproduce our experiment, they found that tumors began to form in their control mice after about 1 week—not 9-10 weeks. Because their control developed tumors so rapidly, Horrigan et al. recognized that they could not meaningfully test our finding that mutant PREX2 accelerated the tumor formation.

Why did the human melanocyte cells grow tumors in the control mice so much faster in Horrigan et al.’s experiment? The likely explanation is that human cells engineered in this way are known to undergo dramatic changes when they are grown for extended periods in culture. Therefore, Horrigan et al.’s study underscores how important it is to have appropriate control cells, before attempting to reproduce experimental findings.

Finally, we emphasize that Horrigan et al.’s results do not call into question our results about PREX2 because their experiment was not informative. Moreover, we have recently validated the findings about PREX2 in an independent way—by creating genetically engineered mice that carry mutated PREX2 in their own genomes. These PREX2 mutant mice showed accelerated tumor growth compared to controls.

Description for Scientific Readers

The authors repeated a xenograft experiment (Figure 3b) in our 2012 report. In our experiment, we overexpressed GFP (negative control), wild type PREX2 (normal control) and two PREX2 mutants (G844D and Q1430*) (experimental arm) in a TERT-immortalized human melanocyte line engineered with RB and p53 inactivation (p’mel). To further sensitize these melanocytes for tumorigenicity, they were also engineered to overexpress oncogenic NRASG12D. We showed that the mutant PREX2 expression in p’mel cells significantly accelerated tumor formation in vivo. However, Horrigan et al found that the control and PREX WT or mutant expressing p’mels all behaved identically, forming tumors rapidly in vivo (within 1 week of implantation). This finding differed from our study, in which the control cells (both GFP and PREX2) did not form tumors until >10 weeks after implantation.

The fact that Horrigan et al observed rapid tumor formation in all settings means that their findings are uninformative with regard to the reproducibility of a central conclusion of our 2012 report, namely that mutant PREX2 can accelerate tumor formation in vivo. Testing this hypothesis requires a control arm in which tumor formation is sufficiently latent so that a discernible effect on the rate of tumorigenesis by the mutants can be observed. In the Horrigan et al study, tumorigenesis in the control arms was so rapid that it essentially became impossible to detect any additional effect of mutant PREX2.

Why were the controls so much more tumorigenic in the hands of Horrigan et al.? We note that although the investigators were provided with clones from the same p’mels used in the 2012 study, by the time Horrigan et al received the cells, more than two years had passed since the original p’mel cells were engineered. This is a crucial point, because as with many other cell lines, these “primed” human primary melanocytes are known to readily undergo adaptation during extended cultivation in vitro. In particular, these p’mels can spontaneously acquire a more transformed phenotype over time (we have seen this happen on multiple occasions). Thus, although a clone from the same engineered cells were provided to Horrigan et al, the fact that that clone of p’mel cells exhibited very different phenotype suggests that the additional passages, a major geographic relocation, and subsequent freeze-thaw manipulations have rendered them unsuitable as an experimental frame of reference.

When we notice such “drifts” in engineered cell culture models, we often have to re-derive the relevant lines starting from even earlier stages in order to have controls with suitable tumorigenic latency. For example, in this case, we would have re-introduced NRASG12D into a clone of non-transformed melanocytes harboring TERT immortalization and RB/P53 inactivation to re-engineer a p’mel cell line. Had Horrigan et al used less tumorigenic controls, they would have a much better chance to reproduce an accelerating effect of mutant PREX2.

To validate our initial observations regarding the oncogenic role of mutant PREX2, we have since taken an orthogonal approach: we created a genetically engineered mouse (GEM) model targeting both a truncating PREX2 mutation (E824*) and oncogenic NRASG12D expression to melanocytes under a tet-regulated promoter. In this GEM model, we observed significantly increased penetrance and decreased latency of melanoma formation (Lissanu Deribe et al PNAS, 2016, E1296-305; see Figure 3b in Lissanu Deribe et al PNAS, 2016), thus confirming the xenograft findings of our 2012 report showing that mutant PREX2 is oncogenic.

In summary, we support rigorous assessments of reproducibility such as this. Equally, we consider it crucial to recognize and account for salient underlying properties of the model systems and experimental controls in order to minimize the risk of misleading conclusions regarding the reproducibility of any given experiment. Indeed, Horrigan et al. nicely articulated the importance of these considerations when discussing their results.

IUPHAR-DB is now subsumed into a new resource. For the latest description see "The IUPHAR/BPS Guide to PHARMACOLOGY in 2016" https://www.ncbi.nlm.nih.gov/pubmed/26464438

IUPHAR-DB is now subsumed into a new resource. For the latest description see "The IUPHAR/BPS Guide to PHARMACOLOGY in 2016" https://www.ncbi.nlm.nih.gov/pubmed/26464438

I know nothing about cow genetics, but I have done some work on the genetics of metabolites in humans, so I was interested to see how the authors derived biological insights from this genetic study. In particular, I was intrigued by the suggestion in the abstract that they found evidence that genes involved in the synthesis of “milk components” are important for lactation persistence.

Unfortunately, the more I studied the paper the more problems I found that call this claim into question.

First off, the Q-Q plot is currently unavailable, but the text mentions there’s only a “slight deviation in the upper right tail”, which could mean there are no true significant signals.

To account for multiple testing, the authors decided to use a genome-wide association p-value cutoff of 0.95/44100 = 2.15e-5 instead of a more defensible 0.05/44100 = 1.1e-6.

Since their initial p-value cutoff yielded a relatively small number of significant SNPs, the authors used a much more lenient p-value cutoff of 5e-4 which presumably is well within the linear portion of the Q-Q plot.

The biggest problem with the enrichment analysis, however, is that they’ve neglected to account for genes drawn from a common locus. Often, paralogs of similar function are proximal in the genome. But typically we assume that a single SNP is affecting the function of only a single gene at a locus. So, for example, a SNP near the APOA4/APOA1/APOC3/APOA5 locus can tag all 4 genes, but it’s unfair to consider that 4 independent indications that “phospholipid efflux”, “reverse cholesterol transport”, “triglyceride homeostasis” and other pathways are “enriched” in this GWAS.

This issue, of overcounting pathways due to gene duplication, affects all their top findings, presumably rendering them non-significant. Besides lipid pathways, this issue also pertains to the “lactation” GO term, which was selected based on the genes GC, HK2, CSN2 and CSN3. GC, CSN2 and CSN3 are all co-located on Chromosome 6.

A perplexing claim in the paper is for the enrichment of the term “lipid metabolic process” (GO:0006629). According to the Ensembl Biomart, 912 Bos taurus genes fall into this category, or about 4% of the bovine protein coding genes (24616 according to Ensembl). So out of their set of 536 genes (flanking SNPs with P < 5e-4) we’d expect about 20 “lipid metabolic process” genes. And yet, this paper reports only 7. This might be significant, but for depletion, not enrichment.

Sample size is of course a huge issue in GWAS. While 3,800 cows is a large number, it appears this trait may require a substantially larger number of animals before it can yield biologically meaningful results.

How did the author manage to publish a paper with the title "E-cigarettes: Are they as safe as the public thinks?", without citing any data on what the public actually does think? There is data in the National Cancer Institute's HINTS survey 2015. This is what it says:

Compared to smoking cigarettes, would you say that electronic cigarettes are…

• 5.3% say much less harmful
• 20.6% say less harmful
• 32.8% say just as harmful
• 2.7% say more harmful
• 2.0% say much more harmful
• 1.2% have never heard of e-cigarettes
• 33.9% don’t know enough about these products

Which brings me to the main issue with the paper. The author claims that there is insufficient knowledge to determine if these products are safer than cigarettes. This is an extraordinary and dangerous claim given what is known about e-cigarettes and cigarettes. It is known with certainty that there are no products of combustion of organic material (i.e tobacco leaf) in e-cigarette vapour - this is a function of the physical and chemical processes involved. We also know that products of combustion cause almost all of the harm associated with smoking. There is also extensive measurement of harmful and potentially harmful constituent of cigarette smoke and e-cigarette aerosol showing many are not detectable or present at levels two orders of magnitude lower in the vapour aerosol (e.g. see Farsalinos KE, 2014, Burstyn I, 2014). So the emissions are dramatically less toxic and exposures much lower.

The author provides a familiar non-sequitur: "There are no current studies that prove that e-cigarettes are safe". There never will be. Firstly because it is impossible to prove something to be completely safe, and almost nothing is. Secondly, no serious commentators claim they are completely safe, just very much safer than smoking. Hence the term 'harm reduction' to describe the benefits of switching to these products.

This view commands support in the expert medical profession. The Royal College of Physicians (London) assessed the toxicology evidence in its 2016 report Nicotine without smoke: tobacco harm reduction and concluded:

Although it is not possible to precisely quantify the long-term health risks associated with e-cigarettes, the available data suggest that they are unlikely to exceed 5% of those associated with smoked tobacco products, and may well be substantially lower than this figure. (Section 5.5 page 87)

This is a carefully measured statement that aims to provide useful information to both users of the products and health and medical professionals while reflecting residual uncertainty. It contrasts with the author's information leaflet for patients, which even suggests there is no basis for believing e-cigarettes to be safer than smoking:

If you are smoking and not planning to quit, we don't know if e-cigarettes are safer. Talk to your health care provider.

But we do know beyond any reasonable doubt that e-cigarettes are very much safer - the debate is whether they are 90% safer or 99.9% safer than smoking. Regrettably, only 5.3% of American adults correctly believe that e-cigarettes are very much less harmful than smoking, while 37% incorrectly think they are as harmful or more harmful (see above). The danger with these misperceptions of risk is that they affect behaviour, causing people to continue to smoke when they might otherwise switch to much safer vaping. The danger with a paper like this and its patient-facing leaflet is that it nurtures these harmful risk misperceptions and becomes, therefore, a vector for harm.

To return to the author's title question: E-Cigarettes: Are They as Safe as the Public Thinks?. The answer is: "No, they are very much safer than the public thinks".

This appears to be a classic example of the Hawthorne Effect, i.e. what gets examined tends to improve (http://www.economist.com/node/12510632). The conclusion of this research seems to be that focusing on a problem by providing feedback tends to improve that problem, compared to doing nothing.

A subset of experimental results from this study were the focus of a replication attempt as part of the Reproducibility Project: Cancer Biology (https://osf.io/e81xl/wiki/home/). The experimental designs and protocols were reviewed and approved in a Registered Report (http://dx.doi.org/10.7554/eLife.04180) and the results of the experiments were published in a Replication Study (http://dx.doi.org/10.7554/eLife.21634).

A subset of experimental results from this study were the focus of a replication attempt as part of the Reproducibility Project: Cancer Biology (https://osf.io/e81xl/wiki/home/). The experimental designs and protocols were reviewed and approved in a Registered Report (http://dx.doi.org/10.7554/eLife.04586) and the results of the experiments were published in a Replication Study (http://dx.doi.org/10.7554/eLife.18173).

A subset of experimental results from this study were the focus of a replication attempt as part of the Reproducibility Project: Cancer Biology (https://osf.io/e81xl/wiki/home/). The experimental designs and protocols were reviewed and approved in a Registered Report (http://dx.doi.org/10.7554/eLife.06847) and the results of the experiments were published in a Replication Study (http://dx.doi.org/10.7554/eLife.17044).