Evidence map›Paper›PMID 40909815›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Estimating the carcinogenesis timelines in early-onset versus late-onset cancers and changes across birth cohorts.

Navid Mohammad Mirzaei, Wan Yang

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Navid Mohammad MirzaeiDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA.ORCID 0000-0002-0971-0514
Wan YangDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA.

Funding

UNCOVER: underlying novel causes of onset of very early cancer researchR01CA257971 · NCI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI YANG, WAN · 2021 to 2025
$2.0M
NCI NIH HHS R01 CA257971
6 · The paper itself

Abstract

Understanding the timing of key mutational events in cancer development is critical for informing cancer prevention and detection strategies, particularly for early-onset cases that have increased in recent years. Yet intermediate mutational events are challenging to observe in humans. Here, we extend a tumor kinetic model we recently developed and long-term cancer registries data to estimate the expected timing of intermediate mutational events for breast, colorectal, and thyroid cancers. We formulate three distinct systems of ordinary differential equations, each describing cell evolution sequentially through three stages of carcinogenesis, up to the first occurrence of malignant transition. We further apply a convolution-based method to derive probability distributions and compute the expected age for each transition, based on parameters fit to incidence and tumor size data for each cancer. The models estimate the initial mutation occurs early in life for all three cancer types. For breast and colorectal cancers, estimated malignant transformation occurs more than a decade faster and hence earlier in early-onset than late-onset cases (in late 30s vs. late 40s to early 50s). In contrast, early- and late-onset thyroid cancers show similar early timelines (malignant transformation in late 20s), consistent with known early-life thyroid clonal activity. We also quantify early-onset carcinogenesis timelines for three key birth cohorts (born during 1950-1954, 1965-1969, and 1980-1984) and identify a shift toward earlier malignant transformation in more recent cohorts, largely due to faster progressions of later stage transitions, for all three cancer types. These findings can inform early-onset cancer etiologic studies and intervention strategies.

Indexed as

Breast cancerColorectal cancerEarly-onset cancerLate-onset cancerMultistage clonal expansion modelMutation timelineThyroid cancer

Identifiers

PMID40909815
PMCPMC12407671

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.