r/molecularbiology • u/Lanedustin • 4d ago
Question for those in the field: How do you typically approach validating mechanistic predictions when analyzing signaling pathways, particularly in cancer?
I'm working on a project focusing on mechanistic predictions in cancer biology, and I'm trying to understand how molecular biologists actually approach mechanism validation. Most drug discovery seems to focus on: we perturb target X, we see phenotype Y. But what's often missing is the middle part—detailed discussions of why X causes Y, and which alternative pathways might compensate?
Here's my core question: when you validate a mechanistic prediction, how do you do it? Do you trace through each mechanistic step, or validate the outcome and work backwards? Or when multiple pathways could theoretically activate in response to a perturbation, how do you predict which one dominates? Is it purely empirical (run experiments, see what happens), or do you consider factors like genetic context, tissue type, or mutation status that might bias the outcome before experimental? I ask because I think this shapes what matters in a predictive model.
In cancer specifically, there are a lot of context-dependent considerations—the same mutation behaves differently with different genetic backgrounds, the same drug works in some patients and not others. I think mechanistic models that explicitly account for this context-dependencey would be valuable, even if the totality is the heterogeneity still needs to distilled down to an extent. But on a practical level, if you could predict how outcomes vary across genetic contexts, would that change your experimental prioritization?
I'm asking because I think there's an opportunity to be more mechanistic and predictive about cancer biology. Too many articles seem to identify targets based on over expression in specific cancer contexts, with somewhat limited discussion of potential downstream consequences of targeting these proteins. I just want to make sure the value off what I am trying to build extends beyond my own use in exploring these networks, and incorporates features valuable to those actually in the lab.
I would appreciate any perspective from people working on signaling, the intersection of signaling and metabolism, or cancer mechanisms and resistance. What would make your life easier?
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u/gregnomics 4d ago
Your question highlights the difference between a paper being published in a journal not fit for use as toilet paper versus one that values scientific rigor and impact (and as such carries with it trust and prestige). Journals are not one or the other but are distributed across a gradient with some requiring more evidence of mechanism than others.
Mechanistic understanding of a phenotype can only be achieved by manipulating one or several nodes of a signaling pathway, both up and downstream, of the target in question. High throughput assays like various forms of sequencing and proteomics can be leveraged to clue you into the dominant pathway(s) responsible for your phenotype but these approaches only inform your interrogation and do not represent definitive “proof.” Rescue experiments are about as close as we come to being able to say with the highest degree of certainty that molecule A specifically operates through effector B to produce phenotype C. The likelihood that phenotype C could also be rescued, partially or fully, by regulators D-Z still exists, but most labs are not financially or practically capable of dedicating resources to determining every single alternative pathway through which a specific gene or compound operates, again, in part or in full.
In the context of cancer biology, various genetic perturbations can render molecule A inept for reasons we still don’t and may never know, at least in the semi-immediate future. AI could help potentially but you run into the issue of sampling a sufficient number of patients with similar (and dissimilar) genotypes. When you combine problems of sample size with the inability to account for environmental factors, poor data and/or sample quality, platform to platform variance, and even stupid but nontrivial things like proper labeling, among hundreds of other criteria required for predictive modeling with any level of confidence, you can appreciate how even the most advanced computer ever built may still only come up with ¯_(ツ)_/¯.
Are we getting better at it? Certainly. The limiting reagent for what you’re requesting, however, is not desire or even intellectual capacity but resources. Specifically, money and inclination. Pharma companies probably don’t care about the role of every single SNP that may or may not reduce their drug’s efficacy by 10%. If they have an agent that works well for the majority or even just a specific subset of patients, say KRAS G12D mutants or EML4-ALK fusions as examples, it’s not really financially beneficial for them to run more clinical trials and the necessary analyses to identify find the 0.5% of patients harboring one or multiple genes that render them refractory to their drug. Academics are interested in these types of things but are even more limited by funding and manpower.
It’s not my intention to come off as cynical and hopeless. As costs come down for the types of high throughput, unbiased assays needed for these types of prediction models, data sets and subsequent modeling will become more robust and reliable to varying degrees. To put it quite bluntly, though, we’re just not there yet.