Feedback analysis identifies a combination target for overcoming adaptive resistance to targeted cancer therapy

Park, Sang-Min; Hwang, Chae Young; Choi, Ji-Hye; Joung, Chang Young; Cho, Kwang-Hyun

doi:10.1038/s41388-020-1255-y

Cited by 17 publications

(31 citation statements)

References 66 publications

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“…In addition, Src oncogenic role in RTK signalling may explain why Srci sensitises CRC to RTK inhibitors in experimental CRC models [ 136 ]. Similarly, the effect of Src activity on MAPK/PI3K signalling is consistent with findings showing the potential clinical utility of combining Srci with KRAS effector inhibitors (MAPK kinase, and PI3K inhibitors) in KRAS mutant CRC [ 137 , 138 ]. This observation is clinically relevant because these tumours are refractory to the upstream EGFR antibody currently used in the clinic, cetuximab [ 11 ].…”

Section: Therapeutic Strategies To Target Sfks Signalling In Crcsupporting

confidence: 82%

Src Family Tyrosine Kinases in Intestinal Homeostasis, Regeneration and Tumorigenesis

Sirvent

Mevizou

Naim

et al. 2020

Cancers

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Src, originally identified as an oncogene, is a membrane-anchored tyrosine kinase and the Src family kinase (SFK) prototype. SFKs regulate the signalling induced by a wide range of cell surface receptors leading to epithelial cell growth and adhesion. In the intestine, the SFK members Src, Fyn and Yes regulate epithelial cell proliferation and migration during tissue regeneration and transformation, thus implicating conserved and specific functions. In patients with colon cancer, SFK activity is a marker of poor clinical prognosis and a potent driver of metastasis formation. These tumorigenic activities are linked to SFK capacity to promote the dissemination and tumour-initiating capacities of epithelial tumour cells. However, it is unclear how SFKs promote colon tumour formation and metastatic progression because SFK-encoding genes are unfrequently mutated in human cancer. Here, we review recent findings on SFK signalling during intestinal homeostasis, regeneration and tumorigenesis. We also describe the key nongenetic mechanisms underlying SFK tumour activities in colorectal cancer, and discuss how these mechanisms could be exploited in therapeutic strategies to target SFK signalling in metastatic colon cancer.

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Section: Therapeutic Strategies To Target Sfks Signalling In Crcsupporting

confidence: 82%

Src Family Tyrosine Kinases in Intestinal Homeostasis, Regeneration and Tumorigenesis

Sirvent

Mevizou

Naim

et al. 2020

Cancers

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“…Park et al . were interested in identifying a gene that, when perturbed in combination with BRAFi, could prevent the development of adaptive resistance to BRAFi 56 . However, instead of inhibiting upstream molecules like in the BRAFi + EGRFi treatment, they searched for a target within the MAPK signaling pathway that could sensitize HT29 CRC cells to MAPKi therapy.…”

Section: Method’s Validationmentioning

confidence: 99%

NETISCE: A Network-Based Tool for Cell Fate Reprogramming

Marazzi

Shah

Balakrishnan

et al. 2022

Preprint

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The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. NETISCE identifies reprogramming targets through the innovative use of control theory within a dynamical systems framework. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system that are relevant for the desired reprogramming task.

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“…In such cases, it is imperative to identify the aspects of the signaling network responsible for such dynamic reprogramming and arrest the same. For instance, employing novel probabilistic BD modeling on a colorectal cancer network showed that blocking a feedback regulation involving Src may overcome the adaptive resistance during targeted therapy involving inhibition of MAPK pathway [164,165]. Probabilistic BD modeling permits including variability in selecting the Boolean logic employed in the Boolean function of a node based on the weights of the interactions input it [166,167].…”

Section: Therapeutic Outlook: Drug Response and Resistancementioning

confidence: 99%

Boolean dynamic modeling of cancer signaling networks: Prognosis, progression, and therapeutics

Sherekar

Viswanathan

2021

Comp Sys Onco

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Cancer is a multifactorial disease. Aberrant functioning of the underlying complex signaling network that orchestrates cellular response to external or internal cues governs incidence, progression, and recurrence of cancer. Detailed understanding of cancer's etiology can offer useful insights into arriving at novel therapeutic and disease management strategies. Such an understanding for most cancers is currently limited due to unavailability of a predictive large‐scale, integrated signaling model accounting for all tumor orchestrating factors. We suggest that the potential of Boolean dynamic (BD) modeling approaches, though qualitative, can be harnessed for developing holistic models capturing multi‐scale, multi‐cellular signaling processes involved in cancer incidence and progression. We believe that constraining such an integrated BD model with variety of omics data at different scales from laboratory and clinical settings could offer deeper insights into causal mechanisms governing the disease leading to better prognosis. We review the recent literature employing different BD modeling strategies to model variety of cancer signaling programs leading to identification of cancer‐specific prognostic markers such as SMAD proteins, which may also serve as early predictors of tumor cells hijacking the epithelial‐mesenchymal plasticity program. In silico simulations of BD models of different cancer signaling networks combined with attractor landscape analysis and validated with experimental data predicted the nature of short‐ and long‐term response of standard targeted therapeutic agents such as Nutlin‐3, a small molecule inhibitor for p53‐MDM2 interaction. BD simulations also offered a mechanistic view of emerging resistance to drugs such as Trastuzumab for HER+ breast cancer, analysis of which suggested new combination therapies to circumvent them. We believe future improvements in BD modeling techniques, and tools can lead to development of a comprehensive platform that can drive holistic approaches toward better decision‐making in the clinical settings, and thereby help identify novel therapeutic strategies for improved cancer treatment at personalised levels.

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Feedback analysis identifies a combination target for overcoming adaptive resistance to targeted cancer therapy

Cited by 17 publications

References 66 publications

Src Family Tyrosine Kinases in Intestinal Homeostasis, Regeneration and Tumorigenesis

Src Family Tyrosine Kinases in Intestinal Homeostasis, Regeneration and Tumorigenesis

NETISCE: A Network-Based Tool for Cell Fate Reprogramming

Boolean dynamic modeling of cancer signaling networks: Prognosis, progression, and therapeutics

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