Impact of Tumor Progression on Cancer Incidence Curves

Abstract

Cancer arises through a multistage process, but it is not fully clear how this process influences the age-specific incidence curve. Studies of colorectal and pancreatic cancer using the multistage-clonal-expansion (MSCE) model have identified two phases of the incidence curves. One phase is linear beginning about age of 60, suggesting that at least two rare rate-limiting mutations occur prior to clonal expansion of premalignant cells. A second phase is exponential, seen in earlier-onset cancers occurring before the age of 60 that are associated with premalignant clonal expansion. Here we extend the MSCE model to include clonal expansion of malignant cells, an advance that permits study of the effects of tumor growth and extinction on the incidence of colorectal, gastric, pancreatic and esophageal adenocarcinomas in the digestive tract. After adjusting the age-specific incidence for birth-cohort and calendar-year trends, we found that initiating mutations and premalignant cell kinetics can explain the primary features of the incidence curve. However, we also found that the incidence data of these cancers harbored information on the kinetics of malignant clonal expansion prior to clinical detection, including tumor growth rates and extinction probabilities on three characteristic time scales for tumor progression. Additionally, the data harbored information on the mean sojourn times for prema-lignant clones until occurrence of either the first malignant cell or the first persistent (surviving) malignant clone. Lastly, the data also harbored information on the mean sojourn time of persistent malignant clones to the time of diagnosis. In conclusion, cancer incidence curves can harbor significant information about hidden processes of tumor initiation, premalignant clonal expansion and malignant transformation, and even some limited information on tumor growth before clinical detection.

Long Noncoding RNA GAPLINC Regulates CD44-Dependent Cell Invasiveness and Associates with Poor Prognosis of Gastric Cancer

“…Gastric cancer is the second leading cause of cancer-related mortality in the world, and the majority of patients with gastric cancer are diagnosed at an advanced stage and die within 24 months after operation because of recurrence and metastasis (1,2). To improve gastric cancer early diagnosis and targeted therapy, an in-depth understanding of molecular underpinnings of the disease is required (3)(4)(5).…”

Exploring the Recent Trend in Esophageal Adenocarcinoma Incidence and Mortality Using Comparative Simulation Modeling

“…The FHCRC model is a biological model based on the paradigm of initiation, promotion, and progression where carcinogenesis arises from the accumulation of mutations and clonal expansion of partially altered cells on the pathway to malignancy (Appendix Figure A1). The FHCRC model also combines likelihood and multiscale spatial simulation methods to represent health states as observation or detection processes built into a detailed tissue- and cell-level model of carcinogenesis (16, 17). It also includes random transitions from normal esophageal tissue to BE that occur with a rate which reflects the prevalence of GERD in the general population (18, 19).…”

The Role of Gastroesophageal Reflux and Other Factors during Progression to Esophageal Adenocarcinoma

“…Comparisons continued with models of increasing complexity combining cohort and period trends acting on different biological processes, stopping at models with at most six trend parameters. These results were compared with a (non-nested) model with external (multiplicative) cohort and period adjustments similar to age-period-cohort (APC) models (49). …”

“…In this study, multiscale models are calibrated to EAC incidence data from the Surveillance, Epidemiology, and End Results (SEER) registries between 1975 and 2009 (27). Although multiscale models were fit previously to EAC incidence trends in the US (49, 50), this work represents the first systematic multiscale exploration of the mechanistic role of sGERD and OF as drivers of EAC incidence trends, while explicitly incorporating cohort and period trends for sGERD and OF that influence biological processes. The multiscale approach includes a model of sGERD prevalence that depends on age, birth cohort, and period.…”