Tag: Cancer Evolution

Field Cancerization

[PMID: 29217838] [Nature Reviews Cancer]

An evolutionary perspective on field cancerization

“Field cancerization, which is the replacement of the normal cell population by a cancer-primed cell population that may show no morphological change, is now recognized to underlie the development of many types of cancer, including the common carcinomas of the lung, colon, skin, prostate and bladder. Field cancerization is the consequence of the evolution of somatic cells in the body that results in cells that carry some but not all phenotypes required for malignancy.”

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Subclonal Evolution of Resistant Cancer Phenotypes

[PMID: 29093439] [Nature Communications]

Combating subclonal evolution of resistant cancer phenotypes

“track the genetic and phenotypic subclonal evolution of four breast cancers through years of treatment to better understand how breast cancers become drug-resistant. Recurrently appearing post-chemotherapy mutations are rare. However, bulk and single-cell RNA sequencing reveal acquisition of malignant phenotypes after treatment” “These findings highlight cancer’s ability to evolve phenotypically and suggest a phenotype-targeted treatment strategy that adapts to cancer as it evolves.”

Spatial-omic data in Early Breast Tumor

[PMID: 29093438] [Nature Communications]

Mapping genomic and transcriptomic alterations spatially in epithelial cells adjacent to human breast carcinoma

“To address this we created a unique dataset of epithelial samples ductoscopically obtained from ducts leading to breast carcinomas and matched samples from ducts on the opposite side of the nipple. Here, we demonstrate that perturbations in mRNA abundance, with increasing proximity to tumour, cannot be explained by copy number aberrations. “

Hypermutation in Cancer

[PMID: 29056344] [Cell]

Comprehensive Analysis of Hypermutation in Human Cancer

“an extensive assessment of mutation burden through sequencing analysis of >81,000 tumors from pediatric and adult patients” “Mutation burden analysis reveals new drivers of hypermutation in POLE and POLD1.” “Replication repair deficiency was a major contributing factor. ” “Unbiased clustering, based on mutational context, revealed clinically relevant subgroups regardless of the tumors’ tissue of origin, highlighting similarities in evolutionary dynamics leading to hypermutation.” “The order of mutational signatures identified previous treatment and germline replication repair deficiency”

Evolution of Breast Cancer Mets

[PMID: 28810143] [Cell]

Genomic Evolution of Breast Cancer Metastasis and Relapse

“We sequenced whole genomes or a panel of 365 genes on 299 samples from 170 patients with locally relapsed or metastatic breast cancer. Several lines of analysis indicate that clones seeding metastasis or relapse disseminate late from primary tumors, but continue to acquire mutations, mostly accessing the same mutational processes active in the primary tumor. Most distant metastases acquired driver mutations not seen in the primary tumor, drawing from a wider repertoire of cancer genes than early drivers. ” “In primary breast cancer, ER-positive and triple-negative tumors show rather distinct combinations of driver mutations, with PIK3CA, GATA3, and MAPK-pathway mutations characterizing the former and TP53 and copy number alterations the latter. When studying relapse and metastasis samples, however, we found that the genomic differences between triple-negative and ER-positive cancers became more blurred: TP53 mutations were seen in 40%–50% of relapsed ER-positive cases; and PIK3CA, GATA3, CDH1, and MAP3K1 all increased several-fold in relapsed ER-negative cancers.” Work from Peter Campbell.

 

 

SELECT

[PMID: 28756993] [Cell]

Conditional Selection of Genomic Alterations Dictates Cancer Evolution and Oncogenic Dependencies

“Cancer genome profiling has revealed that specific events are more or less likely to be co-selected, suggesting that the selection of one event depends on the others. However, the nature of these evolutionary dependencies and their impact remain unclear. Here, we designed SELECT, an algorithmic approach to systematically identify evolutionary dependencies from alteration patterns. By analyzing 6,456 genomes from multiple tumor types, we constructed a map of oncogenic dependencies associated with cellular pathways, transcriptional readouts, and therapeutic response. Finally, modeling of cancer evolution shows that alteration dependencies emerge only under conditional selection. “