Month: August 2017

MeTIL

[PMID: 28714863] [The Journal of Clinical Investigation]

DNA methylation-based immune response signature improves patient diagnosis in multiple cancers

“the evaluation of tumor-infiltrating lymphocytes (TILs) relies on histopathological measurements with limited accuracy and reproducibility. Here, we profiled DNA methylation markers to identify a methylation of TIL (MeTIL) signature that recapitulates TIL evaluations” Glad to see the methylation becomes a good surrogate of pathological TIL measurement.

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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. “