The evolutionary progression of cancers

It is now generally recognized that cancer is a somatic evolutionary process driven by stepwise selection for single genetic mutations or stable epigenetic changes, resulting from specific DNA methylations. These changes represent potential druggable targets for cancer treatment. Extraordinary developments in DNA sequencing, applied across large numbers of diverse cancer types, have already revealed most of the mutations in protein-coding DNA sequences that drive specific cancer types. The number of protein-coding genes whose changes in expression levels have been convincingly shown to result from stable methylation, rather than mutation, remains much lower than the identified number of mutated protein-coding genes. The poor prognosis associated with undifferentiated adenocarcinomas has long been recognized. This strongly suggests that selection against differentiation is one of the most powerful drivers of cancer progression. Changes in gene expression levels that control differentiation may often involve methylation changes or the effects of DNA-coded functional RNAs. This suggests that the future search for driver genetic changes should focus primarily on studying gene expression control through methylation and DNA-encoded functional RNAs, rather than solely on mutations in protein-coding genes. This research should extend beyond the basic idea that gene expression is primarily regulated by methylation of protein-coding gene promoter regions. The ultimate challenge will be to develop general treatments that can reverse inhibition of expression, for example, by reversing the methylation of specific DNA sequences or by using small inhibitory RNAs.

Authors

Institutions

Publication Details

Journal
Academia oncology.
Published
2024-11-28
DOI
https://doi.org/10.20935/acadonco7415
Citations
1
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
0.29
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The evolutionary progression of cancers

Walter F. Bodmer
1 citations
Academia oncology.
Cancer Genomics and Diagnostics
0.29
article

The evolutionary progression of cancers

Walter F. Bodmer
article en
1 citations

Abstract

It is now generally recognized that cancer is a somatic evolutionary process driven by stepwise selection for single genetic mutations or stable epigenetic changes, resulting from specific DNA methylations. These changes represent potential druggable targets for cancer treatment. Extraordinary developments in DNA sequencing, applied across large numbers of diverse cancer types, have already revealed most of the mutations in protein-coding DNA sequences that drive specific cancer types. The number of protein-coding genes whose changes in expression levels have been convincingly shown to result from stable methylation, rather than mutation, remains much lower than the identified number of mutated protein-coding genes. The poor prognosis associated with undifferentiated adenocarcinomas has long been recognized. This strongly suggests that selection against differentiation is one of the most powerful drivers of cancer progression. Changes in gene expression levels that control differentiation may often involve methylation changes or the effects of DNA-coded functional RNAs. This suggests that the future search for driver genetic changes should focus primarily on studying gene expression control through methylation and DNA-encoded functional RNAs, rather than solely on mutations in protein-coding genes. This research should extend beyond the basic idea that gene expression is primarily regulated by methylation of protein-coding gene promoter regions. The ultimate challenge will be to develop general treatments that can reverse inhibition of expression, for example, by reversing the methylation of specific DNA sequences or by using small inhibitory RNAs.

Academia oncology.Vol. 1(2)
University of Oxford (GB)
Openalex Percentile: Top 37%
Cancer Genomics and Diagnostics
0.29
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.