Nope, not necessarily. The two concepts of biological and statistical interactions can overlap but are not the same. We elaborate on this here, focusing on HER2 as a prototypical example. See in particular Section 3.4 and Figure 7. More tailored to a general oncology audience would also be Section 17. “Prognostic and Predictive Effects” here. For data science nerds, here is a cool related paper and datamethods thread on broader concepts this is all embedded to.
Note also that many of the statistically “predictive” biomarkers oncologists are seeing in clinical datasets are actually ultra-prognostic biomarkers. A great example of this is the beautiful story of ctDNA in muscle-invasive bladder cancer culminating with IMvigor011. Luckily, the prognostic effects of ctDNA monitoring were so powerful that just about everyone with ctDNA+ would recur and practically all with ctDNA- would remain disease-free post-surgery. Thus, the prognostic signal persisted even after using information-destroying methods such as looking for “predictive” 2-way interactions at the HR scale in forest plots of subgroup analyses. That may indeed be the indirect value of looking for these interactions: it can be a quick practical filter for “ultra-prognostic” biomarkers such as adjuvant ctDNA in muscle-invasive bladder cancer.