Overall, the findings from both the laboratory and the clinic implicate negative feedback as an important and targetable mechanism of drug resistance. At a minimum there are four areas in which this concept needs to be advanced in order to move beyond pioneering examples. First, a more comprehensive view of the feedback program regulating a given oncoprotein-driven network is needed. Studies examining the effects of drugging the PI3K/AKT/mTOR and RAS/RAF/MEK pathways have revealed dense layers of negative feedback rather than singular feedback loops. Some of these pathways are unique to specific cell lineages while others are unique to specific tumor genotypes. The powerful technologies for global assessment of gene expression/modification, protein expression/modification, etc. need to be employed to identify characteristic responses to selective inhibitors of these signaling networks. The ability to mine such data will depend heavily on the use of inhibitors that are highly selective for their target and cancer models that effectively mimic the genetic complexity of the diseases being studied. As common feedback modules are identified, a second key area for research on negative feedback in cancer is to define the relevance of the individual feedback programs for adaptive phenotypes. Based on the findings to date, it is very likely that numerous negative feedback loops will be found for a given oncoprotein. It may not be practical or desirable to block all of the feedback programs induced by the drug. Indeed some of these feedback loops may be utilized by normal cells to survive a drug and avert toxicity. Nevertheless, cancer cells may have an Achilles heel in terms of specific feedback loops that are essential for survival. Apart from inducing cell death, some of the adaptive resistance phenotypes are likely to engender other changes in cell fate. For instance, drug induced feedback may enable a metastatic or quiescence program rather than activating continued proliferation. Careful analysis of the cell biology of adaptive programs will be essential to identifying combination therapeutic strategies that effectively translate to patients. While such rational combination strategies to target the oncoprotein and the negative feedback program are being identified, it will be essential to simultaneously identify biomarkers that predict which tumor types will most benefit. To the degree that mTORC1 inhibition induces AKT and ERK, in which patients should we add a MEK inhibitor and in which patients should we add an AKT inhibitor? The third major area of understanding needed to advance effective translation of this concept will be development of robust predictors of response for such combinations. In this regard, it will be necessary for studies performed on feedback responses to extend beyond 1 or 2 cell lines and identify genotype, cell of origin, chromatin state, or other marks to ultimately guide patient selection. Finally, while much of this work will be most realistically achieved using cancer cell lines, mouse models, and patient derived xenografts, there remain reasons to be concerned that these will not fully capture the clinical feedback response. A critical component of the feedback response relies on upstream components like receptors that are strongly influenced by the microenvironment and ligand milieu in which they are found. Moreover, the pharmacodynamics of drug inhibition in the tumor are not well modeled outside the host due to specific circumstances such drug metabolism, tumor vasculature, etc. Despite the hurdles involved, there is still no effective substitute for the gold standard of the patient tumor biopsy. As drugs are developed in the clinic, carefully planned tumor biopsies prior to therapy and while on therapy will yield vital information towards understanding and validating the feedback responses.
The oncoprotein driven signaling pathway can be more realistically envisioned as a web with multiple interconnected inputs. Pressuring the web at a singular point will move some aspects of it in a specified direction, but other significant regions will run counter to this in part due to negative feedback. Synthetic lethality may be an achievable outcome by pressuring the network at both a key node and the major negative feedback signal that is relieved in response. Exploiting such vulnerabilities may greatly improve the chances of therapeutic success. Avant-garde trial designs incorporating tumor biopsies to identify adaptive responses and drug them in patient specific ways may ultimately represent the idealized way forward but will necessarily involve the commitment of investigators, philanthropists, and pharmaceutical companies to understanding and modulating these networks in all their complexity and heterogeneity.
Negative feedback pathways are ubiquitous features of growth factor signaling networks. As growth factor signaling networks play essential roles in the majority of cancers, their therapeutic targeting has become a major emphasis of clinical oncology. Drugs targeting these networks are predicted to inhibit the pathway but also to relieve the negative feedback. This loss of negative feedback can itself promote oncogenic signals and cancer cell survival. Drug-induced relief of feedback may be viewed as one of the major consequences of targeted therapy and a key contributor to therapeutic resistance.