Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

Abstract

Phenotypic (i.e. non-genetic) heterogeneity in melanoma drives dedifferentiation, recalcitrance to targeted therapy and immunotherapy, and consequent tumor relapse and metastasis. Various markers or regulators associated with distinct phenotypes in melanoma have been identified, but, how does a network of interactions among these regulators give rise to multiple 'attractor' states and phenotypic switching remains elusive. Here, we inferred a network of transcription factors (TFs) that act as master regulators for gene signatures of diverse cell-states in melanoma. Dynamical simulations of this network predicted how this network can settle into different 'attractors' (TF expression patterns), suggesting that TF network dynamics drives the emergence of phenotypic heterogeneity. These simulations can recapitulate major phenotypes observed in melanoma and explain de-differentiation trajectory observed upon BRAF inhibition. Our systems-level modeling framework offers a platform to understand trajectories of phenotypic transitions in the landscape of a regulatory TF network and identify novel therapeutic strategies targeting melanoma plasticity.

Computational analysis reveals similarities and differences between SCLC subtypes

“…Such "teams" may comprise of drivers of NE/non-NE phenotype as well as additional players enabling heterogeneity within this broader classification. Such 'teams' have been observed between EMT-inducers and EMT-inhibitors (Jia et al, 2020) and between drivers of proliferative and invasive phenotypes in melanoma (Pillai and Jolly, 2021), indicating a potential common design principle for the networks involved in cancer cell plasticity. We also demonstrated a high degree of transcriptional similarity between the subtypes negatively correlated with high NE scoring: SCLC-Y and SCLC-I*, reminiscent of recent observations that YAP1 expression in SCLC describes a T-cell inflamed subtype (Owonikoko et al, 2021).…”

“…Deciphering the regulatory networks driving phenotypic plasticity and heterogeneity and simulating their emergent dynamics has been helpful in mapping distinct phenotypes and corresponding molecular signatures in breast cancer and melanoma (Deshmukh et al, 2021;Pillai and Jolly, 2021). A similar approach applied to SCLC networks has identified stabilizers and destabilizers of various phenotypes: SCLC-A, SCLC-A2, and SCLC-N (Udyavar et al, 2017;Wooten et al, 2019), and revealed the underlying design principles of these networks (Chauhan et al, 2021).…”

Cancer: More Than a Geneticist’s Pandora’s Box

“…Similarly, in melanoma, identification and simulation of an underlying GRN enabled four different attractors which mapped to four distinct phenotypes reported experimentally -proliferative, neural crest-like, intermediate/transitory and undifferentiated (Pillai and Jolly, 2021;Rambow et al, 2018). This computational analysis could also recapitulate the cell-state transition trajectory observed experimentally upon treatment with vemurafenib through single-cell analysis (Su et al, 2019), offering a platform to identify novel perturbations that can enrich or deplete certain phenotypes.…”

“…This study provides mechanistic detail of role of non-genetic heterogeneity in emergence of drug resistance in a genetically identical population (Gerosa et al, 2020). Additional analysis of drug-tolerant 'persisters' in melanoma has indicated how vemurafenib treatment can trigger cell-state transitions into a more undifferentiated phenotype which is therapeutically resilient (Pillai and Jolly, 2021;Su et al, 2019Su et al, , 2017. Such transitions are often reversible, as seen for EMT (Tripathi et al, 2020), thus enabling resumption of growth upon drug removal.…”

Interconnected High-Dimensional Landscapes of Epithelial-Mesenchymal Plasticity and Stemness

“…The more the self-stabilizing feedback loops, usually, the deeper the valley corresponding to that state is. Whether hybrid E/M states are stabilized by a balance of epithelial and mesenchymal 'teams' of players (similar to 'teams' seen in small cell lung cancer and melanoma [96,97]), or there exist a bonafide 'team' of stabilizers of hybrid E/M phenotypes remains to be identified. In other words, it is possible that hybrid cell states are stabilized through molecules which may not be either EMTinducers or MET-inducers, but bona fide inducers for hybrid E/M phenotype(s).…”