In Memoriam: Al Knudsen, a modest, under-recognized founder of cancer genetics (and more)

My first job was a young faculty member was in the Graduate School of Biomedical Sciences, at the University of Texas Health Science Center in Houston.  Our small Center for Demographic and Population Genetics was part of the Graduate School, and it was small enough that we got to know, and interact with, the Dean.  And what a dean he was!

The great, and good Al Knudsen (1922-2016).  Google images.

It was a small graduate school, so Dr Knudsen still was active in research, cancer research. One of the first talks I heard down there in Houston, when I still didn't have my first pair of cowboy boots, y'all, was an interesting idea about the causes of cancer.

Radiation was a known carcinogen, as were some chemicals, and there were various ideas about how carcinogenesis worked at the gene level. The basic idea was that these agents caused genetic mutations that led cells to misbehave, and though abnormal, escape detection by the immune system. More mutations meant more cancer risk, and this was consistent with 'multi-hit' ideas of cancer. More mutations took longer to accumulate, which was consistent with the increasing risk of cancer with age.  But genetics was still very rudimentary then, compared to now, direct testing primitive at best. And there were some curious exceptions.  An interesting fact was that some cancers seemed familial, arising in close relatives, and typically at earlier ages than the sporadic versions of what seemed to be the same type of tumor.  Why?

One example was the eye cancer retinoblastoma which arose in children or young adults, mostly in isolated cases; but there were affected families in which Rb was often present at birth.  Knudsen's idea was that in affected families one harmful allele was being transmitted, but the disease did not arise until a second mutation occurred.  Al published a quantitative mutational model of the onset age pattern in a PNAS paper in 1971, just before I myself had arrived in Houston, but by chance I had heard him present his work at the time of my job interview.

The basic idea was a 2-hit hypothesis, in which you could inherit one Rb mutation, and then only had to 'wait' for some one of your embryonic retinal cells to suffer the bad luck of a hit in the normal copy in order for a cancer to develop.  That waiting time accounted for the earlier onset of familial cases, because they only had to 'wait' for one mutation, whereas sporadic cases needed to experience two Rb hits in the same cell lineage.

This was a profound insight.  It allowed for cancer genetic findings, in which some forms of cancer clustered in families (e.g., some breast and colorectal cancers). Yet most cases were sporadic.  It was shown roughly at that time, by clever work in those crude days of human genetics, that tumors were clonal--the tumor, even when it had spread, was the descendant of a single aberrant (mutated) cell.

It did not take long for this sort of thinking, along with various methods for detection, to find the Rb gene....and other genes related to cancer.  This eventually included genomewide tests for loss of detectable variation based on microsatellite sites, continued to confirm the idea, far beyond those types of cancer that seem to be caused largely by changes in a single gene. The idea of somatic mutation caused by environmental factors, was complemented by the idea that it is common to inherit genotypes that are partially altered but insufficient by themselves to cause cancer, so that the tumor only arises later in life, after environmentally-caused (or stochastic) further mutations occur.

Knudsen's basically 2-hit idea was quickly generalized to 'multi-hit' models of cancer, and the discovery that cancers in a given individual were clonal led to models in which combinations of inherited mutations (present in every cell) and those that occurred somatically, seemed to account for the basic biology of cancer.  Many of the individual genes whose mutation puts a person at very elevated risk of one or more forms of cancer have since been identified, and newer technology has allowed their functional nature (and reason for their role in cancer) to be found.  Some are involved in DNA repair or control of cell division, and it's understandable why their mutational loss is dangerous.

The sources of variation in these genes may vary, but cancer as a combination of inherited and somatically generated mutations is a, if not the, prevailing general model for its biological nature and epidemiology, and shows why tumors are somatic evolutionary phenomena at the gene level.  But his nugget of an idea triggered much broader work in human genetics that, once technology caught up to the challenge, led to our understanding (and, too often, convenient ignoring) of the role of combined inherited and somatically induced variation as a major cause of the common, complex disorders for which genomewide mapping has become a routine approach.

I was still in Houston when Dr Knudsen moved to the Fox Chase Cancer Center in Philadelphia.  We missed him, but over the following decades he continued to contribute to the understanding of cancer.  His inspiring, gentle, and generous nature was an exception in the snake-pit that has become so common in the 'business model' of so many biomedical research circles.

Al's foundational work earned him many honors.  But he didn't get one that I think he richly deserved: his quiet, transformative role in understanding cancer, and the much broader impact on human genetics that followed as a result, deserved a Nobel Prize.

When scientific theory constrains

It's good from time to time to reflect on how we know what we think we know.  And to remember that, as it has been in any time in history, much of what we now think is true will sooner or later be found to be false or, often, only inaccurately or partially correct.  Some of this is because values change -- not so long ago homosexuality was considered to be an illness, e.g.  Some is because of new discoveries -- when archaea were first discovered they were thought to be exotic microbes that inhabited extreme environments but now they're known to live in all environments, even in and on us. And of course these are just two of countless examples.

But what we think we know can be influenced by our assumptions about what we think is true, too. It's all too easy to look at data and interpret it in a way that makes sense to us, even if there are multiple possible interpretations.  This can be a particular problem in social science, when we've got a favorite theory and the data can be seen to confirm it; this is perhaps easiest to notice if you yourself aren't wedded to any of the theories.  But it's also true in biology. It is understandable that we want to assert that we now know something, and are rewarded for insight and discoveries, rather than more humbly hesitating to make claims.

Charitable giving
The other day I was listening to the BBC Radio 4 program Analysis on the charitable impulse.  Why do people give to charity?  It turns out that a lot of psychological research has been done on this, to the point that charities are now able to manipulate us into giving.  If you call your favorite NPR station to donate during a fund drive, e.g., if you're told that the caller just before you gave a lot of money, you're more likely to make a larger donation than if you're told the previous caller pledged a small amount.

A 1931 advertisement for the British charity, Barnardo's Homes; Wikipedia

Or, if an advertisement pictures one child, and tells us the story of that one child, we're more likely to donate than if we're told about 30,000 needy children.  This works even if we're told the story of two children, one after the other.  But, according to one of the researchers, if we're shown two children at once, and told that if we give, the money will randomly go to just one of the children, we're less likely to give.  This researcher interpreted this to mean that two is too many.

But there seem to me to be other possible interpretations given that the experiment changes more than one variable.  Perhaps it's that we don't like the idea that someone else will choose who gets our money.  Or that we feel uncomfortable knowing that we've helped only one child when two are needy.  But surely something other than that two is too many, given that in 2004 so many people around the world donated so much money to organizations helping tsunami victims that many had to start turning down donations.  These were anonymous victims, in great numbers.  Though, as the program noted, people weren't nearly as generous to the great number of victims of the earthquake in Nepal in 2015, with no obvious explanation.

The researcher did seem to be wedded to his one vs too many interpretation, despite the contradictory data.  In fact, I would suggest that the methods, given what were presented, don't allow him to legitimately draw any conclusion.  Yet he readily did.

Thinness microbes?
The Food Programme on BBC Radio 4 is on to the microbiome in a big way.  Two recent episodes (here and here) explore the connection between gut microbes, food, and health and the program promises to update us as new understanding develops.  As we all know by now, the microbiome, the bug intimates that accompany us through life, in and on our body, may affect our health, our weight, our behavior, and perhaps much more.  Or not.

Pseudomonas aeruginosa, Enterococcus faecalis and Staphylococcus aureus on Tryptic Soy Agar.  Wikipedia

Obesity, asthma, atopy, periodontal health, rheumatoid arthritis, Parkinson's, Alzheimer's, autism, and many many more conditions have been linked with, or are suggested to be linked with, in one way or another, our microbiome.  Perhaps we're hosting the wrong microbes, or not a diverse enough set of microbes, or we wipe the good ones out with antibiotics along with the bad, or with alcohol, and what we eat may have a lot to do with this.

One of the researchers interviewed for the program was experimenting with a set of identical twins in Scotland.  He varied their diets having them eat, for example, lots of junk food and alcohol, or a very fibrous diet, and documented changes in their gut microbiomes which apparently can change pretty quickly with changes in diet.  The most diverse microbiome was associated with the high fiber diet. Researchers seem to feel that diversity is good.

Along with a lot of enthusiasm and hype, though, mostly what we've got in microbiome research so far is correlations.  Thin people tend to have a different set of microbes than obese people, and people with a given neurological disease might statistically share a specific subset of microbes.  But this tells us nothing about cause and effect -- which came first, the microbiome or the condition?  And because the microbiome can change quickly and often, how long and how consistently would an organism have to reside in our gut before it causes a disease?

There was some discussion of probiotics in the second program, the assumption being that controlling our microbiome affects our health.  Perhaps we'll soon have probiotic yogurt or kefir or even a pill that keeps us thin, or prevents Alzheimer's disease.  Indeed, this was the logical conclusion from all the preceding discussion.

But one of the researchers, inadvertently I think, suggested that perhaps this reductionist conclusion was unwarranted.  He cautioned that thinking about probiotic pills rather than lifestyle might be counterproductive.  But except for factors with large effects such as smoking, the effect of "lifestyle" on health is rarely obvious.  We know that poverty, for example, is associated with ill health, but it's not so easy to tease out how and why.  And, if the microbiome really does directly influence our health, as so many are promising, the only interesting relevant thing about lifestyle would be how it changes our microbiomic makeup.  Otherwise, we're talking about complexity, multiple factors with small effects -- genes, environmental factors, diet, and so on, and all bets about probiotics and "the thinness microbiome" are off.  But, the caution was, to my mind, an important warning about the problem of assuming we know what we think we know; in this case, that the microbiome is the ultimate cause of disease.

The problem of theory
These are just two examples of the problem of assumption-driven science. They are fairly trivial, but if you are primed to notice, you'll see it all around you. Social science research is essentially the interpretation of observational data from within a theoretical framework. Psychologists might interpret observations from the perspective of behavioral, or cognitive, or biological psychology, e.g., and anthropologists, at least historically, from, say, a functionalist or materialist or biological or post-modernist perspective. Even physicists interpret data based on whether they are string theorists or particle physicists.

And biologists' theoretical framework? I would suggest that two big assumptions that biologists make are reductionism and let's call it biological uniformitarianism. We believe we can reduce causation to a single factor, and we assume that we can extrapolate our findings from the mouse or zebrafish we're working on to other mice, fish and species, or from one or some people to all people. That is, we assume invariance rather than that what we can expect is variation. There is plenty of evidence to show that by now we should know better.

True, most biologists would probably say that evolutionary theory is their theoretical framework, and many would add that traits are here because they're adaptive, because of natural selection. Evolution does connect people to each other and people to other species, it has done so by working on differences, not replicated identity, and there is no rule for the nature or number of those differences or for extrapolating from one species or individual to another. We know nothing to contradict evolutionary theory, but that every trait is adaptive is an assumption, and a pervasive one.

Theory and assumption can guide us, but they can also improperly constrain how we think about our data, which is why it's good to remind ourselves from time to time to think about how we know what we think we know. As scientists we should always be challenging and testing our assumptions and theories, not depending on them to tell us that we're right.