2021
DOI: 10.1016/j.cell.2021.03.009
|View full text |Cite
|
Sign up to set email alerts
|

Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes

Abstract: Summary Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

12
243
0
1

Year Published

2021
2021
2024
2024

Publication Types

Select...
3
3
3

Relationship

0
9

Authors

Journals

citations
Cited by 340 publications
(256 citation statements)
references
References 112 publications
12
243
0
1
Order By: Relevance
“…However, this finding requires further evaluation via DNA analysis of white blood cells to differentiate cancer from germline mutations or potential clonal hematopoiesis. 51 Additionally, on the basis of WGS on over 2,500 cancer samples, the PCAWG initiative identified actionable mutations in 60% of tumors, 30 while in our integrated analysis of both multi-regional tumor and serial liquid biopsies, all patients with detectable mutations harbored actionable events, supporting the translational framework of detailed tumor and plasma analysis. Nevertheless, as demonstrated by our results, isolated analysis of primary CRC, metastatic tissue or ctDNA is unable to uncover the complete mutational landscape for each individual patient, highlighting the translational importance or our work.…”
Section: Discussionsupporting
confidence: 52%
See 1 more Smart Citation
“…However, this finding requires further evaluation via DNA analysis of white blood cells to differentiate cancer from germline mutations or potential clonal hematopoiesis. 51 Additionally, on the basis of WGS on over 2,500 cancer samples, the PCAWG initiative identified actionable mutations in 60% of tumors, 30 while in our integrated analysis of both multi-regional tumor and serial liquid biopsies, all patients with detectable mutations harbored actionable events, supporting the translational framework of detailed tumor and plasma analysis. Nevertheless, as demonstrated by our results, isolated analysis of primary CRC, metastatic tissue or ctDNA is unable to uncover the complete mutational landscape for each individual patient, highlighting the translational importance or our work.…”
Section: Discussionsupporting
confidence: 52%
“…Multiple genomic studies have identified ITH as an integral part of cancer evolution in solid tumors. 28,30 This multi-regional variability has been strongly correlated with intrinsic and acquired drug resistance and relapse. 27,31,32 Indeed, consistent with our work, several studies have uncovered extensive ITH of primary CRC as a prognostic factor for metastasis, as well as a predictor of drug resistance.…”
Section: Discussionmentioning
confidence: 99%
“…Here we use colorectal carcinogenesis as a model system to evaluate the selective advantage of putative driver mutations. We delineate the evolutionary dynamics of primary colorectal malignancies using a multi-region approach based on single-gland sequencing of multiple distant tumour regions, making use of a novel phylogenetic approach that, unlike single bulk sample strategies (Dentro et al, 2021), has good power to detect positively-selected subclonal expansions. Marrying this sensitive detection of subclonal selection together with an assessment of the genetic makeup of subclones, enables us to assess the functional impact of putative cancer driver gene mutations -measured in terms of the fitness advantage bestowed to the mutant clone -in vivo in patients.…”
Section: Introductionmentioning
confidence: 99%
“…Relatedly, assuming that the subset consisting of K active signatures in the tumor was known and each mutational distribution of the k -th signature was denoted by φ k ∈ ℝ V for 1 ≤ k ≤ K , we easily estimated the subset by applying various fitting methods [17, 18] to the mutation set in advance. For the genomic locus in which each mutation existed, we assumed that the copy number in a cancer cell , the copy number of a major allele in a cancer cell , the copy number in a normal cell (these are collectively denoted as C n in Figure 1), and the sample purity P were also known, and we must set these values in some way to predict CNA considering structural variants [19, 20].…”
Section: Methodsmentioning
confidence: 99%