Microbiome and Metagenomics in Cancer

Sean Davis

Part 01 Establishing causation

How do you prove a microbe causes a disease? The answer has been rewritten twice already — and the microbiome forces a third rewrite.

Koch’s postulates

In 1876, Robert Koch traced anthrax to Bacillus anthracis by following the organism through its entire life cycle — and in doing so gave medicine its first rigorous standard for causation, not mere association.

First page of Robert Koch's 1876 paper on the etiology of anthrax, in German.

Koch’s postulates

The microorganism is found in abundance in diseased but not in healthy individuals
The microorganism can be isolated from the diseased host and grown in pure culture
The cultured microorganism causes disease when inoculated into a healthy host
The microorganism can be re-isolated from the inoculated host and is the same as the original
Elimination of the microbe from the host alleviates disease

The load-bearing assumption

Every postulate assumes one organism, growable in pure culture, sufficient on its own. All three assumptions fail for the microbiome.

The callout is the substance. Each assumption fails in a specific way.

One organism. Microbiome effects are typically community properties. Remove or add any single taxon and the effect usually does not reproduce, so the unit of causation is not an organism.

Pure culture. Most gut organisms historically resisted culture. Postulate 2 therefore excluded the majority of the interesting biology for a century, and is precisely why sequencing-based approaches took over.

Sufficient on its own. This fails even for the clean cases. Roughly half the world carries Helicobacter pylori; a small minority develop gastric cancer. The microbe is necessary but not sufficient — host genetics, bacterial strain genotype, and diet all condition the outcome.

Koch knew the framework leaked. He was aware of asymptomatic carriers of cholera and typhoid, which violate the first postulate outright. These criteria were a standard of evidence, never a law of nature.

Koch’s postulates: the molecular age

Falkow recast causation in terms of genes rather than organisms — by the 1980s the interesting variation was between strains of the same species (Falkow, 1988).

The virulence gene is found in pathogenic but not nonpathogenic strains
Deletion or inactivation of the gene leads to loss of pathogenicity
Reactivation or allelic replacement of the gene restores pathogenicity

The motivating problem: E. coli is a harmless commensal in most people and lethal in others. “Is E. coli a pathogen?” is unanswerable, because the species is the wrong unit. Pathogenicity lives in genes that some strains carry and others do not.

The structure is knockout and complementation — the standard of proof from molecular genetics, imported into infectious disease. Find the gene, remove it, lose virulence; put it back, recover virulence.

Falkow’s own retrospective fifteen years later is a useful counterweight: he observed that the criteria bend constantly in practice, and that applying them rigidly would have blocked genuine discoveries. Some virulence genes are essential for viability and cannot be deleted; some phenotypes require animal models that do not exist. Criteria of this kind are frameworks for argument, not checklists to be satisfied mechanically.

Koch’s postulates: the sequencing age

Fredricks and Relman rewrote the postulates again once sequence, not culture, became the primary evidence — most pathogens of interest had never been cultured at all (Fredericks & Relman, 1996).

Focus shifts to nucleic acid sequences rather than culture or whole genes
Individuals or communities, as identified by sequencing, differ in abundance, organization, and/or function in diseased vs. healthy hosts
Community virulence may or may not depend on specific, well-defined virulence factors
Modifying the community — or removing specific members — alleviates disease

The cases driving this in 1996 were organisms nobody could grow: Tropheryma whipplei in Whipple’s disease, hepatitis C, the hantaviruses, and the herpesvirus behind Kaposi sarcoma. All were identified by sequence before anyone cultured them, so a framework requiring pure culture could not accommodate them.

The last criterion is the one that survives as the field’s working standard. If modifying a community changes the disease, you have something causal. That is the logic behind fecal microbiota transplant for recurrent Clostridioides difficile infection, which remains the field’s most convincing intervention.

There is a cost to this move that becomes important later. Once sequence alone is the evidence, a new failure mode appears: sequence can originate from a contaminated reagent, a mislabeled entry in a reference database, or a batch effect. Koch could see his bacillus under a microscope and grow it on a plate. Sequence-based inference has no equivalent independent check unless one is built in deliberately.

Why study the microbiome?

Humans are full of microorganisms — skin, gut, oral cavity, nasal cavity, eyes — and they affect health, drug metabolism, and treatment response.

3.8 × 10¹³ bacterial cells in a reference 70 kg adult

≈ 1.3 : 1 bacteria-to-human-cell ratio

≈ 150 × more genes than the human complement — 3.3 M microbial genes

Correction to a familiar number

The often-repeated “10× more microbial cells than human cells” traces to a 1972 back-of-envelope estimate. Sender, Fuchs and Milo’s re-derivation puts the ratio near 1:1 — close enough that one defecation event shifts it. Most of the human-cell count is red blood cells, which is where the old estimate went wrong. The gene claim survived; the cell claim did not.

The 10:1 ratio appeared in essentially every microbiome review for forty years. It traces to a 1972 estimate by Luckey that was explicitly an order-of-magnitude calculation, repeated until citation made it fact.

Sender, Fuchs and Milo did the arithmetic properly in 2016: about 3.8 × 10¹³ bacteria, the overwhelming majority in the colon, against about 3.0 × 10¹³ human cells. Roughly 90% of human cells are red blood cells — small, numerous, and easy to underestimate when reasoning from tissue volume, which is where the old figure went wrong. The corrected ratio is near 1:1 and varies enough that a single bowel movement measurably shifts it.

The gene claim held up. Qin and colleagues catalogued 3.3 million non-redundant microbial genes from 124 individuals, against roughly 20,000 human genes. So the functional-potential argument for studying the microbiome survives intact; it was only the headline cell-count statistic that failed.

The transferable point: a number everyone repeats and nobody has re-derived is a liability, not a fact.

Part 02 Measuring the microbiome

Every step between the swab and the count matrix adds bias. Knowing which step added which bias is most of the skill.

Workflow

Pipeline from sampling to extraction to amplification to next-generation sequencing to bioinformatics.

The thing to remember

Every step can add bias or noise — swab site, extraction chemistry, primer choice, PCR cycle number, sequencing platform, and reference database each leave a fingerprint on the final abundance table.

Each step has a characteristic failure.

Sampling. Body site, time of day, recent food, recent antibiotics. Stool is not “the gut” — it samples the lumen’s output, not the mucosa, and the two communities differ substantially.

Extraction. The most underestimated step. Gram-positive cell walls are tough; insufficient bead-beating systematically under-recovers Firmicutes. A reported Bacteroidetes-to-Firmicutes ratio can be largely an artifact of lysis protocol.

Amplification. Primer choice determines which organisms are visible at all. PCR cycle number drives chimera formation and amplification bias.

Sequencing. Platform error profiles differ, which affects how reads are resolved into features.

Bioinformatics. Reference database and version, covered in more detail later.

The practical consequence: when two labs report different abundances for the same organism, protocol differences are a more likely explanation than biology.

Two sequencing approaches

Shotgun

Sequence all DNA in the sample
More information — taxonomy and function
Higher complexity
Higher cost

Amplicon

Sequence one marker gene
No functional information
Less complex to analyze
Cheaper

The 16S rRNA gene

Shannon index of sequence variability across 16S rRNA alignment positions, showing nine variable regions V1 through V9.

Nine variable regions (V1–V9) sit between highly conserved stretches. The conserved parts give you universal primer binding sites; the variable parts give you taxonomic signal.

Which region you amplify determines what you can resolve — and what you systematically miss.

Why this gene: 16S rRNA is present in every bacterium and archaeon, it is functionally constrained so it evolves slowly, and it alternates conserved and variable stretches along its length.

That alternation is the entire trick. Conserved blocks provide primer binding sites that work across the whole domain; the variable blocks between them carry enough sequence difference to distinguish taxa. In the figure, peaks are variable positions and troughs are conserved; the brackets mark commonly amplified windows.

The practical consequence: short-read platforms cover only a few hundred bases, so you must choose a window. V4 is the most common choice, V3–V4 also popular. Different windows give different answers — some genera are indistinguishable across V4 but separable across V1–V2, and the reverse also happens. “We did 16S” is therefore not a method description; “we amplified V4 with the 515F/806R primer pair” is.

Note on the figure itself: it comes from RIM-DB, a methanogen database, so the exact peak heights reflect archaeal sequences. The structure of the argument generalizes; the specific values are not universal.

16S amplicon sequencing

Four-step 16S amplicon workflow: gene-specific primer amplification with adapter tags, barcode addition via index PCR, MiSeq sequencing, and data analysis.

Four steps. First, amplify the chosen variable region with primers carrying adapter tails. Second, a short index PCR adds a sample-specific barcode, which is what allows hundreds of samples to be pooled in one sequencing run. Third, sequence the pool. Fourth, demultiplex by barcode and process.

Two artifacts to know. Barcode hopping — index misassignment between samples, particularly on patterned flow cells — introduces low-level cross-contamination into every sample on a run. This is tolerable for high-biomass samples and serious for low-biomass ones.

Second, the processing step in panel 4 has changed substantially. The field formerly clustered reads into operational taxonomic units at 97% similarity; current practice resolves exact amplicon sequence variants using DADA2 or Deblur. The difference matters when reading older literature: an OTU is defined relative to the dataset that produced it, so OTUs are not comparable across studies, whereas ASVs are exact sequences and therefore are.

Shotgun metagenomics

No marker gene is amplified. Total DNA is extracted, fragmented, and sequenced, then reads are handled along two paths.

Reads matching entries in a reference database are assigned taxonomically and functionally. Reads that match nothing can still be assembled de novo into contigs and binned into metagenome-assembled genomes — MAGs — which is how the field discovers organisms that have never been cultured. That second path is the main scientific advantage of shotgun over amplicon.

The dominant practical problem is host DNA. In a human sample, the great majority of DNA is human. For stool this is manageable. For tumor tissue, blood, or bronchoalveolar lavage, 99% or more of sequencing effort can go to host DNA to recover a small number of microbial reads. This is the low-biomass regime, and it is where the contamination problems discussed later become severe.

Choosing between them

Shotgun

Pros

Not biased by amplicon primer set
Not limited by conservation of one gene
Provides functional information

Cons

Environmental and host contamination
Expensive ($1000+/sample)
Complex analysis; needs HPC, high memory

Amplicon

Pros

Well established, large body of prior data
Inexpensive ($50–$100/sample)

Cons

V-region choice biases results
Built on a very well-conserved gene — hard to resolve species and strains

A workable decision rule.

If the question is whether community composition differs between groups, and statistical power matters, 16S is appropriate and the cost difference is the decisive argument — the same budget buys ten to twenty times as many samples.

If the question involves function, specific pathways, or strain identity, 16S cannot answer it. Strain resolution matters more often than expected: pathogenic and commensal strains of the same species are frequently identical across the amplified 16S window, so a 16S study cannot distinguish them.

Framed generally, this is a trade between statistical power and biological resolution. Neither option is more modern or more correct; the right answer depends on the question.

One caveat on the cost figures: they are order-of-magnitude and have fallen steadily. Analysis cost — compute and expert time — has fallen much more slowly than sequencing cost, and is now frequently the binding constraint on shotgun studies.

16S reference databases

These snapshots are stale

Greengenes 13_8 and SILVA 128 are the versions many published pipelines still pin. Taxonomy has moved substantially since — results are not comparable across database versions, and “which database” is a real methods choice, not a detail (DeSantis et al., 2006; Quast et al., 2012).

The dates are the point. Greengenes 13_8 dates from 2013 and was effectively unmaintained for roughly a decade, yet remained the default in widely used tutorials and therefore in a large body of published analysis.

Bacterial taxonomy has been substantially revised in that interval, much of it under the Genome Taxonomy Database, which reassigns groups on the basis of whole-genome phylogeny rather than historical convention. Firmicutes became Bacillota; Bacteroidetes became Bacteroidota. The renaming is superficial; the regrouping underneath it is not. Two studies reporting “Firmicutes abundance” against different database versions are not measuring the same quantity.

Practical rules that follow. Report the database and its version in methods. Do not compare abundances across studies that used different references without reprocessing from raw reads. This is among the most common silent errors in microbiome meta-analysis, and it is the direct motivation for the uniformly processed compendium resources at the end of the talk.

Microbiome and Metagenomics in Cancer

Part 01 Establishing causation

Koch’s postulates

Koch’s postulates

Koch’s postulates: the molecular age

Koch’s postulates: the sequencing age

Why study the microbiome?

Part 02 Measuring the microbiome

Workflow

Two sequencing approaches

The 16S rRNA gene

16S amplicon sequencing

Shotgun metagenomics

Choosing between them

16S reference databases

Greengenes2 unifies the two worlds