STK11 Loss: A Novel Mechanism for Melanoma Metastasis with Therapeutic Implications

Azin, Marjan; Demehri, Shadmehr

doi:10.1016/j.jid.2021.10.003

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…These mutations may lead to a different growth of the primary tumor or a different metastatic dissemination. BRAF and STK11 have been described as genes involved in the metastatic process 69,70 . Moreover, one of the squamous cell carcinoma had a PTEN mutation, which has been shown to be associated with BM in breast cancer 71 .…”

Section: Discussionmentioning

confidence: 99%

Mechanistic modeling of brain metastases in NSCLC provides computational markers for personalized prediction of outcome

Benzekry

Schlicke

Tomasini

et al. 2023

Preprint

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Background: Intracranial progression after curative treatment of early-stage non-small cell lung cancer (NSCLC) occurs from 10 to 50% and is difficult to manage, given the heterogeneity of clinical presentations and the variability of treatments available. The objective of this study was to develop a mechanistic model of intracranial progression to predict survival following a first brain metastasis (BM) event. Methods: Data included early-stage NSCLC patients treated with a curative intent who had a BM as the first and single relapse site (N=31). We propose a mechanistic mathematical model to estimate the amount and sizes of (visible and invisible) BMs. The two key parameters of the model are 𝛼, the proliferation rate of a single tumor cell; and 𝜇, the per day, per cell, probability to metastasize. The predictive value of these individual computational biomarkers was evaluated. Findings: The model was able to correctly describe the number and size of metastases at the time of first BM relapse for 20 patients. Parameters 𝛼 and 𝜇 were significantly associated with overall survival (OS) (HR 1.65 (1.07-2.53) p=0.0029 and HR 1.95 (1.31-2.91) p=0.0109, respectively). Adding the computational markers to the clinical ones significantly improved the predictive value of OS (c-index increased from 0.585 (95% CI 0.569-0.602) to 0.713 (95% CI 0.700-0.726), p<0.0001). Interpretation: We demonstrated that our model was applicable to brain oligoprogressive patients in NSCLC and that the resulting computational markers had predictive potential. This may help lung cancer physicians to guide and personalize the management of NSCLC patients with intracranial oligoprogression.

show abstract

Section: Discussionmentioning

confidence: 99%

Mechanistic modeling of brain metastases in NSCLC provides computational markers for personalized prediction of outcome

Benzekry

Schlicke

Tomasini

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…BRAF and STK11 have been described as genes involved in the metastatic process 71,72 . Moreover, one of the squamous cell carcinoma had a PTEN mutation, which has been shown to be associated with BM in breast cancer 73 .…”

Section: Discussionmentioning

confidence: 99%

Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases

Benzekry,

Schlicke,

Mogenet

et al. 2023

Clin Exp Metastasis

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Background:Intracranial progression after curative treatment of early-stage non-small cell lung cancer (NSCLC) occurs from 10 to 50% and is difficult to manage, given the heterogeneity of clinical presentations and the variability of treatments available. The objective of this study was to develop a mechanistic model of intracranial progression to predict survival following a first brain metastasis (BM) event.Methods: Data included early-stage NSCLC patients treated with a curative intent who had a BM as the first and single relapse site (N=31).We propose a mechanistic mathematical model to estimate the amount and sizes of (visible and invisible) BMs. The two key parameters of the model are 𝛼, the proliferation rate of a single tumor cell; and 𝜇, the per day, per cell, probability to metastasize. The predictive value of these individual computational biomarkers was evaluated. Findings:The model was able to correctly describe the number and size of metastases at the time of first BM relapse for 20 patients. Parameters 𝛼 and 𝜇 were significantly associated with overall survival (OS) (HR 1.65 (1.07-2.53) p=0.0029 and HR 1.95 (1.31-2.91) p=0.0109, respectively). Adding the computational markers to the clinical ones significantly improved the predictive value of OS (c-index increased from 0.585 (95% CI 0.569-0.602) to 0.713 (95% CI 0.700-0.726), p<0.0001). Interpretation:We demonstrated that our model was applicable to brain oligoprogressive patients in NSCLC and that the resulting computational markers had predictive potential. This may help lung cancer physicians to guide and personalize the management of NSCLC patients with intracranial oligoprogression. Highlights:-Mechanistic mathematical modeling allows individualized prognosis of survival for lung cancer patients after first brain metastatic relapse -Individual model-derived computational parameters identifies high-risk patients in terms of brain metastasis progression and survival -Prognostic features include quantification of the number and sizes of both clinically visible and invisible brain metastases

show abstract

Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases

Benzekry,

Schlicke,

Mogenet

et al. 2023

Preprint

View full text Add to dashboard Cite

Background: Intracranial progression after curative treatment of early-stage non-small cell lung cancer (NSCLC) occurs from 10 to 50% and is difficult to manage, given the heterogeneity of clinical presentations and the variability of treatments available. The objective of this study was to develop a mechanistic model of intracranial progression to predict survival following a first brain metastasis (BM) event. Methods: Data included early-stage NSCLC patients treated with a curative intent who had a BM as the first and single relapse site (N=31). We propose a mechanistic mathematical model to estimate the amount and sizes of (visible and invisible) BMs. The two key parameters of the model are α, the proliferation rate of a single tumor cell; and μ, the per day, per cell, probability to metastasize. The predictive value of these individual computational biomarkers was evaluated. Findings: The model was able to correctly describe the number and size of metastases at the time of first BM relapse for 20 patients. Parameters α and μ were significantly associated with overall survival (OS) (HR 1.65 (1.07-2.53) p=0.0029 and HR 1.95 (1.31-2.91) p=0.0109, respectively). Adding the computational markers to the clinical ones significantly improved the predictive value of OS (c-index increased from 0.585 (95% CI 0.569-0.602) to 0.713 (95% CI 0.700-0.726), p<0.0001). Interpretation: We demonstrated that our model was applicable to brain oligoprogressive patients in NSCLC and that the resulting computational markers had predictive potential. This may help lung cancer physicians to guide and personalize the management of NSCLC patients with intracranial oligoprogression.

show abstract

STK11 Loss: A Novel Mechanism for Melanoma Metastasis with Therapeutic Implications

Cited by 3 publications

References 19 publications

Mechanistic modeling of brain metastases in NSCLC provides computational markers for personalized prediction of outcome

Mechanistic modeling of brain metastases in NSCLC provides computational markers for personalized prediction of outcome

Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases

Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases

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