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Analysis of Advanced, Metastatic Tumors Indicates How Treatment Influences Mutational Profiles

NEW YORK – A new analysis of advanced tumors from patients underscores how cancer treatments affect tumors' mutational profiles.

As part of the Personalized OncoGenomics Program at BC Cancer, researchers led by Marco Marra profiled 570 advanced and metastatic cancers using whole-genome and transcriptome sequencing. Most previous large-scale tumor sequencing efforts have focused on primary tumors.

As they reported today in Nature Cancer, the researchers' pan-cancer analysis uncovered links between exposure to DNA-damaging treatments and particular mutational signatures as well as gene alterations associated with drug resistance or sensitivity.

"Our analyses reveal potential mechanisms of resistance and impacts of therapy and illustrate the value of performing whole-genome and transcriptome profiling on advanced cancers in a clinical context," Marra and his colleagues wrote in their paper.

This analysis encompassed biopsies from advanced and metastatic tumors from patients from the POG570 cohort. About 80 percent of patients in the cohort received systemic therapies prior to their biopsies.

By analyzing tumor and normal sequences, the researchers uncovered 7.4 million somatic substitutions and more than 701,000 small indels. The most frequently altered oncogenes and tumor-suppressor genes included TP53, NF1, RB1, and KRAS, among others. 

As compared to primary cancers from the Pan-Cancer Analysis of Whole Genomes cohort, the POG570 cohort had higher frequencies of mutations — particularly in breast and ovarian cancer patients — that have previously been implicated in endocrine therapy resistance. This, the researchers noted, was in line with the cohort's treatment history, suggesting these alterations influence treatment resistance.

The researchers also uncovered 35 small coding mutations and gene copy-number mutations that were more frequently altered among treated patients. Eight of the 13 of those mutations that were clustered or truncating represented drug-mutation alterations. Three in particular — one affecting ESR1 and two affecting EGFR — were known resistance mutations that crop up in aromatase-treated breast cancers. Expression-level data likewise indicated elevated ESR1 expression among those given hormonal therapy.

The researchers further identified mutational signatures within their cohort. One signature — SBS31 — was more prevalent among patients who had received platinum therapies. This linked additional tumor types, including cholangiocarcinoma, sarcoma, and breast, lung, and ovarian cancer, with the SBS31 signature. 

Changes in gene expression were also associated with receiving certain therapies. For instance, VEGFA, which is targeted by bevacizumab, is expressed at higher levels among patients who received that therapy, suggesting that this change in VEGFA expression may be a compensatory resistance mechanism. Meanwhile, DPYD expression was reduced in colorectal cancer patients treated with 5-FU, especially those treated with it for more than 90 days. DPYD, the researchers noted, encodes an enzyme that degrades 5-FU.

Within their cohort, the researchers also noticed a number of recurrent events affecting noncoding but regulatory regions of genes like TERT, PLEKHS1, AP2A1, and ADGRG6. TERT promoter mutations, they noted, have a known oncogenic role, which they noted was consistent with its increased expression here.

The researchers also examined the immune microenvironment of these advanced cancers. They noted that the overall survival of patients varied by the immune cluster in which they fell, with cluster five patients exhibiting the highest overall survival. Additionally, these immune signatures in combination with overall tumor burden could predict immunotherapy response. 

"In addition to contributing to fundamental research insights, the results from this study have informed clinical patient management, including selection of patients for immunotherapy, use of genome signatures for predicting drug sensitivity, transcriptome-based changes in diagnosis, and identification of drug targets, including fusions and overexpressed genes," the researchers added.