What Short-Term MAPK Perturbation Transcriptomics Measures: A Quantitative Framework for Resistance-State Inference
Short-term perturbation transcriptomics is increasingly used to nominate drug-response mechanisms, adaptive tolerance states, and resistance-associated programs. The interpretability problem is not that acute response is literally equated with final resistance; it is that several biologically distinct regimes can produce overlapping RNA signatures. Here I define an operational framework for MAPK-driven cancer models that separates acute pharmacodynamic response, cytostatic response, adaptive stress-tolerance/drug-tolerant-persister (DTP)-like rewiring, pre-existing resistant-like state, and stable acquired resistance. I then apply that framework without claiming clone identity from RNA alone. In GSE108383, a melanoma single-cell dataset comparing parental and six-week BRAF-inhibitor-resistant A375 and 451Lu cells, pre-existing resistant-like cells were rare: 0.6% of parental cells crossed a lenient resistant-state threshold and 0% crossed a stringent resistant-median threshold. In Tahoe-100M pseudobulk differential expression, I quantified 2,135 24-hour MAPK-pathway perturbation conditions across 39 MAPK-driver lines and 12 RAF, MEK, or RAS inhibitors. The observed regime distribution was 42.5% acute pharmacodynamic/cytostatic response, 7.0% adaptive stress-tolerance/DTP-like rewiring, 50.4% no detected program under the tested panels, and 0% stable acquired-resistance-like activation. These are transcriptomic mode fractions, not clone fractions. The central result is therefore methodological: short-term MAPK perturbation transcriptomics predominantly measures acute pathway suppression and cytostasis, with a smaller stress-tolerance tail, and should not be assumed to approximate stable acquired resistance-state transcription without longitudinal, lineage, or genotype evidence.
Reviews
This manuscript proposes an operational, regime-based interpretation framework for short-term MAPK-pathway perturbation transcriptomics, explicitly separating acute pharmacodynamic/cytostatic responses from adaptive rewiring/DTP-like programs and from stable acquired resistance (which the author states cannot be inferred from RNA alone). The analysis applies the framework to (i) GSE108383 single-cell melanoma (parental vs 6-week BRAFi-resistant A375 and 451Lu) to estimate how often untreated parental cells look “resistant-like” under percentile-based thresholds, and (ii) a large Tahoe-100M pseudobulk compendium of 24h MAPK inhibitor perturbations (2,135 conditions) to quantify how frequently acute vs adaptive vs “stable-resistance-like” signatures appear. The key quantitative takeaways are that parental resistant-like cells are rare under the chosen thresholds (mean 0.6% lenient; 0% stringent), and that 24h perturbations most often show acute suppression/cytostasis (reported ~42–47%), sometimes adaptive rewiring (~7%), often “no detectable program” (~50%), and essentially never a 6-week-resistance-like signature. The main strength is conceptual and epistemic discipline: the paper repeatedly draws the correct boundary that short-term expression signatures are “modes of transcriptional response” rather than clone fractions, and it avoids claiming genetic resistance from RNA-only readouts. The main weakness is that the quantitative results hinge on several under-specified analytic choices (how signatures/modules are defined; scoring method; statistical testing and multiple-testing control for “significant activation”; handling of batch effects and gene coverage; definition of “no detectable program”; and sensitivity to thresholding). Additionally, the cross-line signature transfer appears weak (AUC 0.38 in one direction), raising concern that “resistant-like” as defined may be line-specific and that reported rarity could partly reflect signature non-portability rather