Fungal infections are clearly associated with cancer. There’s some research into its possible carcinogenic role in at least some cancers. There’s a strong consensus that certain viruses can, but usually don’t, cause cancer. Personally, it seems like a perfectly reasonable hypothesis that fungal infections can play an interactive causal role in driving some cancers. In general, the consensus is you typically need at least two breakdowns of the numerous mechanisms that regulate the cell cycle and cell death for cancer to occur.
I’m a PhD student in the cancer space, focusing on epigenetics and cancer. Basically, this is the field where we try to explain both normal cellular diversity (where DNA mutations are definitely not the cause except in very specialized contexts like V(D)J recombination) and cancers apparently not driven by somatic mutations in protein-coding genes.
Mutations not in protein-coding genes are not necessarily inert. RNA can be biologically active. Noncoding DNA serves as docking sites for proteins, which can then go on to affect transcription of genes into mRNA. The proteome can also be affected by alternative splicing of mRNA. Non-coding mutations can potentially affect any of these processes and thereby affect the RNA and protein landscape within a cell.
In 2024, our ability to detect mutations varies widely across the genome, due both to the way we obtain sequencing data in the first place and the way we attempt to make sense of it. NGS sequencing involves breaking the genome into short fragments and reading around 150 base pairs on either end of the fragments, then trying to map it back to a reference genome. Mapping quality will suffer or completely degrade both if the patient has substantial genetic difference from the reference genome or in regions that are highly repetitive within the genome, such as centromeres. When I work with genetic data, there are regions spanning multiple megabasis that are completely blank, and a large percentage of our reads have to be thrown out because we can’t unambiguously map them to a particular location on the genome. This will be partially overcome in the future as we start to use more long-read sequencing, but this technology is still in its early stages and I’m not sure it will completely replace NGS for the foreseeable future.
In the epigenetics space, we focus on several aspects of cell biochemistry apart from DNA mutations. The classic example is DNA methylation, which is a methyl group (basically a carbon atom) present on about 60% of cytosines (C) that are immediately followed by guanine (G). The CpG dinucleotide is heavily underrepresented relative to what you’d expect by chance, and its heavily clustered in gene promoters. Methylated CpG islands in promoters are associated with “off genes”. The methylation mark is preserved across mitosis. It’s thought to be a key mechanism by which cell differentiation is controlled. We also study things like chromatin accessibility (whether DNA is tightly packaged up and relatively inaccessible to protein interactions or loose and open) and chromatin conformation (the 3D structure of DNA, which can control things like subregion localization into a particular biochemical gradient or adjacency of protein-docking DNA regions to gene promoters).
These epigenetic alterations are also thought to be potentially oncogenic. Epigenetic alterations could potentially occur entirely due to random events localized to the cell in which the alterations occur, or could be influenced by intercellular signaling, physical forces, or, yes, infection. If fungal infections control cells like puppets and somehow cause cancer, my guess is that it would be through some sort of epigenetic mechanism (I don’t know if there are any known fungi that can transmit their DNA to human cells).
Epigenetics research is mainstream, but the technology and data analysis is comparatively immature. One of the reasons it’s not more common is that it’s much harder to gather data on and interpret than it is to study DNA mutations. Most of our epigenetics methods involve sequencing DNA that has undergone some extra-fancy processing of one kind or another, so it’s bound to be strictly more expensive and difficult to execute than plain ol’ DNA sequencing alone. Compounding this, the epigenetic effects we’re interested in are typically different from cell to cell, meaning that not only do you have these extra-challenging assays, you also need to aim for single-cell resolution, which is also either extremely expensive (like $30/cell, isolating individual nuclei using a cell sorter and running reactions on each individually, leading to assays that can cost millions of dollars to produce) or difficult (like using a hyperactive transposase to insert DNA barcodes into intact nuclei that give a cell-specific label the genetic fragments originating from each cell, bringing assay costs down to a mere $50,000-$100,000 driven mainly by DNA sequencing rather than cell processing costs). This data is then very sparse (because there’s a finite amount of genetic information in each cell), very large, and very difficult to interpret. We also have extremely limited technologies to cause specific epigenetic changes, whereas we have a wide variety of tools for precisely editing DNA.
For potentially oncogenic infections, fungal or otherwise, you’d want to show things like:
We can give organisms cancer by transferring the pathogen to them
We can slow or prevent cancer by suppressing the putatively oncogenic pathogen.
The pathogen is found in cancer biosamples at an elevated rate
There are differences between the cancer-associated pathogens and non-cancer-associated pathogens, or cellular changes that make them more susceptible to oncogenesis through their interactions with the pathogen
All of this seems like a perfectly respectable research project, just difficult. I can’t imagine anybody I work with having a problem with it. Where they probably would have a problem would be if the argument was that “fungal infections are the sole cause of cancer, and DNA mutations or epigenetic alterations are completely irrelevant to oncogenesis.”
There’s an angle I’ve neglected in this post until now, which is the perspective from evolutionary theory. it’s more common to refer to this in explaining how cancer evolves within an individual. But it’s also relevant to consider how it bears on the Peto paradox. Loosely, species tend to evolve such that causes of reproductive unfitness (including death) tend to balance out in terms of when they occur in the life cycle. Imagine a species under evolutionary pressure to grow larger, perhaps because it will allow it to escape predation or access a new food source. If the larger number of cells put it at increased risk of cancer, then at some point there would be an equilibrium where the benefit of increased size was cancelled by the cost of increased oncogenesis risk. This also increases adaptive pressure to stabilize new oncopreventative mechanisms in the population that weren’t present before. This may facilitate additional growth to a new equilibrium.
This helps explain why cancer isn’t associated with larger size: adaptive pressure to develop new oncopreventative mechanisms increases in proportion to the risk to reproductive fitness posed by cancer.
Fungal infections are clearly associated with cancer. There’s some research into its possible carcinogenic role in at least some cancers. There’s a strong consensus that certain viruses can, but usually don’t, cause cancer. Personally, it seems like a perfectly reasonable hypothesis that fungal infections can play an interactive causal role in driving some cancers. In general, the consensus is you typically need at least two breakdowns of the numerous mechanisms that regulate the cell cycle and cell death for cancer to occur.
I’m a PhD student in the cancer space, focusing on epigenetics and cancer. Basically, this is the field where we try to explain both normal cellular diversity (where DNA mutations are definitely not the cause except in very specialized contexts like V(D)J recombination) and cancers apparently not driven by somatic mutations in protein-coding genes.
Mutations not in protein-coding genes are not necessarily inert. RNA can be biologically active. Noncoding DNA serves as docking sites for proteins, which can then go on to affect transcription of genes into mRNA. The proteome can also be affected by alternative splicing of mRNA. Non-coding mutations can potentially affect any of these processes and thereby affect the RNA and protein landscape within a cell.
In 2024, our ability to detect mutations varies widely across the genome, due both to the way we obtain sequencing data in the first place and the way we attempt to make sense of it. NGS sequencing involves breaking the genome into short fragments and reading around 150 base pairs on either end of the fragments, then trying to map it back to a reference genome. Mapping quality will suffer or completely degrade both if the patient has substantial genetic difference from the reference genome or in regions that are highly repetitive within the genome, such as centromeres. When I work with genetic data, there are regions spanning multiple megabasis that are completely blank, and a large percentage of our reads have to be thrown out because we can’t unambiguously map them to a particular location on the genome. This will be partially overcome in the future as we start to use more long-read sequencing, but this technology is still in its early stages and I’m not sure it will completely replace NGS for the foreseeable future.
In the epigenetics space, we focus on several aspects of cell biochemistry apart from DNA mutations. The classic example is DNA methylation, which is a methyl group (basically a carbon atom) present on about 60% of cytosines (C) that are immediately followed by guanine (G). The CpG dinucleotide is heavily underrepresented relative to what you’d expect by chance, and its heavily clustered in gene promoters. Methylated CpG islands in promoters are associated with “off genes”. The methylation mark is preserved across mitosis. It’s thought to be a key mechanism by which cell differentiation is controlled. We also study things like chromatin accessibility (whether DNA is tightly packaged up and relatively inaccessible to protein interactions or loose and open) and chromatin conformation (the 3D structure of DNA, which can control things like subregion localization into a particular biochemical gradient or adjacency of protein-docking DNA regions to gene promoters).
These epigenetic alterations are also thought to be potentially oncogenic. Epigenetic alterations could potentially occur entirely due to random events localized to the cell in which the alterations occur, or could be influenced by intercellular signaling, physical forces, or, yes, infection. If fungal infections control cells like puppets and somehow cause cancer, my guess is that it would be through some sort of epigenetic mechanism (I don’t know if there are any known fungi that can transmit their DNA to human cells).
Epigenetics research is mainstream, but the technology and data analysis is comparatively immature. One of the reasons it’s not more common is that it’s much harder to gather data on and interpret than it is to study DNA mutations. Most of our epigenetics methods involve sequencing DNA that has undergone some extra-fancy processing of one kind or another, so it’s bound to be strictly more expensive and difficult to execute than plain ol’ DNA sequencing alone. Compounding this, the epigenetic effects we’re interested in are typically different from cell to cell, meaning that not only do you have these extra-challenging assays, you also need to aim for single-cell resolution, which is also either extremely expensive (like $30/cell, isolating individual nuclei using a cell sorter and running reactions on each individually, leading to assays that can cost millions of dollars to produce) or difficult (like using a hyperactive transposase to insert DNA barcodes into intact nuclei that give a cell-specific label the genetic fragments originating from each cell, bringing assay costs down to a mere $50,000-$100,000 driven mainly by DNA sequencing rather than cell processing costs). This data is then very sparse (because there’s a finite amount of genetic information in each cell), very large, and very difficult to interpret. We also have extremely limited technologies to cause specific epigenetic changes, whereas we have a wide variety of tools for precisely editing DNA.
For potentially oncogenic infections, fungal or otherwise, you’d want to show things like:
We can give organisms cancer by transferring the pathogen to them
We can slow or prevent cancer by suppressing the putatively oncogenic pathogen.
The pathogen is found in cancer biosamples at an elevated rate
There are differences between the cancer-associated pathogens and non-cancer-associated pathogens, or cellular changes that make them more susceptible to oncogenesis through their interactions with the pathogen
All of this seems like a perfectly respectable research project, just difficult. I can’t imagine anybody I work with having a problem with it. Where they probably would have a problem would be if the argument was that “fungal infections are the sole cause of cancer, and DNA mutations or epigenetic alterations are completely irrelevant to oncogenesis.”
There’s an angle I’ve neglected in this post until now, which is the perspective from evolutionary theory. it’s more common to refer to this in explaining how cancer evolves within an individual. But it’s also relevant to consider how it bears on the Peto paradox. Loosely, species tend to evolve such that causes of reproductive unfitness (including death) tend to balance out in terms of when they occur in the life cycle. Imagine a species under evolutionary pressure to grow larger, perhaps because it will allow it to escape predation or access a new food source. If the larger number of cells put it at increased risk of cancer, then at some point there would be an equilibrium where the benefit of increased size was cancelled by the cost of increased oncogenesis risk. This also increases adaptive pressure to stabilize new oncopreventative mechanisms in the population that weren’t present before. This may facilitate additional growth to a new equilibrium.
This helps explain why cancer isn’t associated with larger size: adaptive pressure to develop new oncopreventative mechanisms increases in proportion to the risk to reproductive fitness posed by cancer.
Thanks. You’ve convinced me that Lintern overstates the evidence of mutation-free cancer cells.