Tumor dormancy and frailty models: A novel approach

PMV Rancoita, M Valberg, R Demicheli, E Biganzoli, C Di Serio

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Frailty models are here proposed in the tumor dormancy framework, in order to account for possible unobservable dependence mechanisms in cancer studies where a non-negligible proportion of cancer patients relapses years or decades after surgical removal of the primary tumor. Relapses do not seem to follow a memory-less process, since their timing distribution leads to multimodal hazards. From a biomedical perspective, this behavior may be explained by tumor dormancy, i.e., for some patients microscopic tumor foci may remain asymptomatic for a prolonged time interval and, when they escape from dormancy, micrometastatic growth results in a clinical disease appearance. The activation of the growth phase at different metastatic states would explain the occurrence of metastatic recurrences and mortality at different times (multimodal hazard). We propose a new frailty model which includes in the risk function a random source of heterogeneity (frailty variable) affecting the components of the hazard function. Thus, the individual hazard rate results as the product of a random frailty variable and the sum of basic hazard rates. In tumor dormancy, the basic hazard rates correspond to micrometastatic developments starting from different initial states. The frailty variable represents the heterogeneity among patients with respect to relapse, which might be related to unknown mechanisms that regulate tumor dormancy. We use our model to estimate the overall survival in a large breast cancer dataset, showing how this improves the understanding of the underlying biological process. © 2016, The International Biometric Society.
Original languageEnglish
Pages (from-to)260-270
Number of pages11
JournalBiometrics
Volume73
Issue number1
DOIs
Publication statusPublished - 2017

Fingerprint

Dormancy
Frailty Model
dormancy
Tumors
Tumor
Hazards
Frailty
neoplasms
Hazard Rate
Neoplasms
relapse
Recurrence
Hazard
Cancer
Risk Function
Hazard Function
Biometrics
Breast Cancer
Biological Phenomena
Random variables

Cite this

Tumor dormancy and frailty models: A novel approach. / Rancoita, PMV; Valberg, M; Demicheli, R; Biganzoli, E; Di Serio, C.

In: Biometrics, Vol. 73, No. 1, 2017, p. 260-270.

Research output: Contribution to journalArticle

Rancoita, PMV ; Valberg, M ; Demicheli, R ; Biganzoli, E ; Di Serio, C. / Tumor dormancy and frailty models: A novel approach. In: Biometrics. 2017 ; Vol. 73, No. 1. pp. 260-270.
@article{d32ae8f2cea546abba3f97e57d12f2b2,
title = "Tumor dormancy and frailty models: A novel approach",
abstract = "Frailty models are here proposed in the tumor dormancy framework, in order to account for possible unobservable dependence mechanisms in cancer studies where a non-negligible proportion of cancer patients relapses years or decades after surgical removal of the primary tumor. Relapses do not seem to follow a memory-less process, since their timing distribution leads to multimodal hazards. From a biomedical perspective, this behavior may be explained by tumor dormancy, i.e., for some patients microscopic tumor foci may remain asymptomatic for a prolonged time interval and, when they escape from dormancy, micrometastatic growth results in a clinical disease appearance. The activation of the growth phase at different metastatic states would explain the occurrence of metastatic recurrences and mortality at different times (multimodal hazard). We propose a new frailty model which includes in the risk function a random source of heterogeneity (frailty variable) affecting the components of the hazard function. Thus, the individual hazard rate results as the product of a random frailty variable and the sum of basic hazard rates. In tumor dormancy, the basic hazard rates correspond to micrometastatic developments starting from different initial states. The frailty variable represents the heterogeneity among patients with respect to relapse, which might be related to unknown mechanisms that regulate tumor dormancy. We use our model to estimate the overall survival in a large breast cancer dataset, showing how this improves the understanding of the underlying biological process. {\circledC} 2016, The International Biometric Society.",
author = "PMV Rancoita and M Valberg and R Demicheli and E Biganzoli and {Di Serio}, C",
year = "2017",
doi = "10.1111/biom.12559",
language = "English",
volume = "73",
pages = "260--270",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "1",

}

TY - JOUR

T1 - Tumor dormancy and frailty models: A novel approach

AU - Rancoita, PMV

AU - Valberg, M

AU - Demicheli, R

AU - Biganzoli, E

AU - Di Serio, C

PY - 2017

Y1 - 2017

N2 - Frailty models are here proposed in the tumor dormancy framework, in order to account for possible unobservable dependence mechanisms in cancer studies where a non-negligible proportion of cancer patients relapses years or decades after surgical removal of the primary tumor. Relapses do not seem to follow a memory-less process, since their timing distribution leads to multimodal hazards. From a biomedical perspective, this behavior may be explained by tumor dormancy, i.e., for some patients microscopic tumor foci may remain asymptomatic for a prolonged time interval and, when they escape from dormancy, micrometastatic growth results in a clinical disease appearance. The activation of the growth phase at different metastatic states would explain the occurrence of metastatic recurrences and mortality at different times (multimodal hazard). We propose a new frailty model which includes in the risk function a random source of heterogeneity (frailty variable) affecting the components of the hazard function. Thus, the individual hazard rate results as the product of a random frailty variable and the sum of basic hazard rates. In tumor dormancy, the basic hazard rates correspond to micrometastatic developments starting from different initial states. The frailty variable represents the heterogeneity among patients with respect to relapse, which might be related to unknown mechanisms that regulate tumor dormancy. We use our model to estimate the overall survival in a large breast cancer dataset, showing how this improves the understanding of the underlying biological process. © 2016, The International Biometric Society.

AB - Frailty models are here proposed in the tumor dormancy framework, in order to account for possible unobservable dependence mechanisms in cancer studies where a non-negligible proportion of cancer patients relapses years or decades after surgical removal of the primary tumor. Relapses do not seem to follow a memory-less process, since their timing distribution leads to multimodal hazards. From a biomedical perspective, this behavior may be explained by tumor dormancy, i.e., for some patients microscopic tumor foci may remain asymptomatic for a prolonged time interval and, when they escape from dormancy, micrometastatic growth results in a clinical disease appearance. The activation of the growth phase at different metastatic states would explain the occurrence of metastatic recurrences and mortality at different times (multimodal hazard). We propose a new frailty model which includes in the risk function a random source of heterogeneity (frailty variable) affecting the components of the hazard function. Thus, the individual hazard rate results as the product of a random frailty variable and the sum of basic hazard rates. In tumor dormancy, the basic hazard rates correspond to micrometastatic developments starting from different initial states. The frailty variable represents the heterogeneity among patients with respect to relapse, which might be related to unknown mechanisms that regulate tumor dormancy. We use our model to estimate the overall survival in a large breast cancer dataset, showing how this improves the understanding of the underlying biological process. © 2016, The International Biometric Society.

U2 - 10.1111/biom.12559

DO - 10.1111/biom.12559

M3 - Article

VL - 73

SP - 260

EP - 270

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 1

ER -