Hey Bioinformatics lovers,

Tommy here. Everyone once was a beginner, and I made mistakes too.

However, the experience is something you gained by making those mistakes.

TTN shows up near the top of every cancer exome waterfall plot. If you're new to somatic variant analysis, you'll see it and think you found a driver.

You didn't.

TTN encodes titin, the largest human protein at ~34,000 amino acids. The gene has 363 exons.

A coding sequence that massive collects somatic mutations the way a large target collects stray arrows — by sheer size, not by biological selection.

In cancer genomics, TTN is a passenger.

TTN mutation counts track with tumor mutational burden (TMB). In TCGA's pan-cancer cohort of 10,000+ samples, TTN mutation frequency correlated with TMB at rho = 0.917.

Tumors with high microsatellite instability (MSI-H) light up TTN because they accumulate mutations genome-wide, and TTN's coding region is the biggest target.

Some groups now use TTN mutation count as a single-gene proxy for TMB. That's a legitimate use — and about the only one TTN earns in tumor profiling.

Don't build a biological narrative around TTN mutations in your cancer exome. There's no driver story there.

In cardiology, TTN mutations are real — but context is everything.

Truncating variants in A-band exons with high cardiac expression cause dilated cardiomyopathy (DCM). These are well-validated, clinically actionable findings.

But many TTN truncations land in alternatively spliced exons with low cardiac PSI (proportion spliced in).

Those exons produce transcripts that the heart barely uses. A truncation in an exon with PSI < 0.5 is a different beast than one in a constitutively expressed A-band exon.

The variant type alone — missense, truncating, frameshift — doesn't tell you enough. You need to check which exon got hit and whether the heart actually expresses it.

Two rules for TTN:

Correct for gene length before you call TTN significant in a burden test.

And check exon-level cardiac expression (PSI) before you call a TTN variant pathogenic.

The biggest gene in the genome, the biggest interpretation headache in your results.

If you want to dig deeper: Roberts et al. 2015 laid out the PSI framework for interpreting TTN truncations.

Oh et al. 2020 (npj Genomic Medicine) formalized the TTN-as-TMB-proxy approach with TCGA data.

Happy Learning!

Tommy aka crazyhottommy

PS:

If you want to learn Bioinformatics, there are four ways that I can help:

1. My free YouTube Chatomics channel, make sure you subscribe to it.
2. I have many resources collected on my github here.
3. I have been writing blog posts for over 10 years https://divingintogeneticsandgenomics.com/​
4. Lastly, I am posting daily AI Claude code tips on my Linkedin https://www.linkedin.com/in/%F0%9F%8E%AF-ming-tommy-tang-40650014/recent-activity/all/​

Stay awesome!
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PPS