Jeffrey Townsend
@jeffreytownsend
Elihu Professor of Biostatistics @yalesph.bsky.social--Evolutionary biology of cancer, infectious disease, and fungi; sometimes running.
We increased resolution on selection at the individual-variant level, showing that gene-level averages obscure hotspot-specific biology. PIK3CA H1047R and E545K—and especially BRAF V600E—emerge as substantially more strongly selected in colon than in rectal cancer. 🧪 rdcu.be/fx0fu
We ranked the strongest gene-wide cancer effects in both tumor types. However, the proposed “private” genes show only moderate effects and are not among the strongest drivers. The major selective architecture is therefore broadly shared across colon and rectum. 🧪 rdcu.be/fx0fu
Gene-wide cancer effects are largely concordant between colon and rectal adenocarcinoma. However, six previously proposed “private” drivers lie near parity. Their prevalence differences therefore do not indicate strong tissue-specific selection. 🧪 rdcu.be/fx0fu
We further emphasized the utility of continuous analyses rather than imposition of discrete thresholds. Such analyses showed better how TMB increases with tobacco smoking exposure (SBS4), and that—in contrast—tumors with higher APOBEC mutational process (SBS13) do not exhibit higher TMB.
Extraordinary graduate student Kira Glasmacher dug deeper, comparing survival annotations in the recent publication with the original dataset (www.nature.com/articles/s41...). Bioinformatic forensics revealed discrepancies in survival status at last follow-up—and survival didn't differ as reported.
Further examining a similar result based on a distinct mutational signature (SBS13) and a threshold of 0.25 identified in the original paper, no statistically significant difference in TMB could be detected once reference-based signature attribution was used.
As Rishabh examined the original research, he noticed that it very strangely reported lower tumor mutation burden in smokers than non-smokers. That didn't make sense, and his re-analysis of the data backed up that the smoking signature tracked with more mutations, not fewer.
Mutations are not the same as cancer, so Rishabh used cancereffectsizeR (aacrjournals.org/cancerres/ar...) to quantify oncogenic effect, not simply mutation count. In this case, mutational impact on cancer was not the same, but was also not terribly different from impact on the genome.
@multitarga.com was featured in this Yale School of Public Health piece on community and health innovation. Our Multi-Targeted Primer reagents aim to make high-quality RNA-seq more focused, efficient, and accessible across health, agriculture, and surveillance. ysph.yale.edu/news-article...
New @plos.org-GENETICS: Borne, Taverner & @pandolfatto.bsky.social use MASS-PRF to detect clustered, lineage-specific amino-acid substitutions in Drosophila proteins—then beautifully validate one case in Trio. journals.plos.org/plosgenetics... 🧪 #EvoDevo
Arvin Venkat, emergency medicine physician, scientist, and state representative in the House of Representatives of the State of Pennsylvania speaks on the topic of science and democracy in our times at NO KINGS — Pittsburgh. #Velshi
One of these is the White House @whitehouse-47.bsky.social . The other is Dnipro, Ukraine. What are the common factors?
gave a talk on "Why Cancer Isn’t the Same at Every Age: Lessons for Prevention and Care" at the Joint Retreat on Aging and Cancer Research, sponsored by @yalecancer.bsky.social Program in Genomics, Genetics, and Epigenetics among others including many engaged representatives of the local community.
One last point: don't use tobacco. It causes cancer doi.org/10.1093/molb... (also respiratory and pulmonary diseases, cardiovascular and vascular diseases, diabetes mellitus, and reproductive and sexual impairment).
Together, these findings support an interpretation that enhanced ICI response in KRAS G12C-mutant tumors arises from tobacco-driven mutagenesis and its immunogenic consequences, rather than from variant-specific oncogenic or immunologic properties. authors.elsevier.com/a/1ls3ocYZOi...
By integrating published clinical outcome data, we have shown that increasing exposure to tobacco predicts lower hazard ratios and higher response rates to immune-checkpoint inhibition. authors.elsevier.com/a/1ls3ocYZOi...
Our analyses point to the importance of mutagenic context: KRAS G12C is enriched for the tobacco-associated SBS4 mutational signature, which is strongly correlated with tumor mutation burden across lung adenocarcinomas. authors.elsevier.com/a/1ls3ocYZOi...
To clarify the effect of tobacco-associated mutagenesis, we used data from Litchfield et al. to model the impact of tobacco-driven tumor mutational burden on immune-checkpoint inhibition. Predicted response rates increase sharply at low–moderate exposure to SBS4 (tobacco). doi.org/10.1016/j.ce...
To clarify the effect of tobacco-associated mutagenesis, we used data from Samstein et al. to model the impact of tobacco-driven tumor mutation burden on immune-checkpoint inhibition outcomes. As SBS4 (tobacco exposure) increases, the predicted hazard ratio declines. www.nature.com/articles/s41...
Because tobacco smoke exposure contributes to both KRAS G12C mutations and to increased TMB, it follows that KRAS G12C-mutant tumors exhibit significantly increased TMB compared to KRAS G12D tumors. authors.elsevier.com/a/1ls3ocYZOi...
Smoking is a major contributor to increased tumor mutational burden in non-small-cell lung cancer. Tobacco smoke-related SBS4-attributed mutation fraction and TMB were significantly correlated across LUAD tumors. authors.elsevier.com/a/1ls3ocYZOi...
We examined the tobacco mutational signature and KRAS mutation frequency in tumors. KRAS G12C-mutant tumors showed a significantly higher proportion of mutations attributable to the SBS4 tobacco-associated mutational signature compared to KRAS G12D. authors.elsevier.com/a/1ls3ocYZOi...
We analyzed 9,230 lung adenocarcinoma (LUAD) tumors to assess the mutational context and oncogenic potential of KRAS G12A and G12V and G12C and G12D variants. Their cancer effect sizes were similar—large compared to variants at other sites of KRAS. aacrjournals.org/cancerres/ar...
🔍 Measuring how fast expression evolves opens a path to understanding how organisms evolve—not just what evolves. This is just the beginning: from single genes to networks, expression clocks can help decode the tempo of evolution. doi.org/10.1093/molb... #MolecularEvolution #GeneExpression #MBE
Some functions are flexible, others are locked down. Metabolic pathways—like carbon and sulfur metabolism—evolve fast in gene expression. But core cellular processes like splicing and proteasome function? They evolve slowly, constrained by essential roles in development. doi.org/10.1093/molb...