Abstract
Mutated genes are rarely common even in the same pathological type between cancer patients and as such, it has been very challenging to interpret genome sequencing data and difficult to predict clinical outcomes. PIK3CA is one of a few genes whose mutations are relatively popular in tumors. For example, more than 46.6% of luminal-A breast cancer samples have PIK3CA mutated, whereas only 35.5% of all breast cancer samples contain PIK3CA mutations. To understand the function of PIK3CA mutations in luminal A breast cancer, we applied our recently-proposed Cancer Hallmark Network Framework to investigate the network motifs in the PIK3CA-mutated luminal A tumors. We found that more than 70% of the PIK3CA-mutated luminal A tumors contain a positive regulatory loop where a master regulator (PDGF-D), a second regulator (FLT1) and an output node (SHC1) work together. Importantly, we found the luminal A breast cancer patients harboring the PIK3CA mutation and this positive regulatory loop in their tumors have significantly longer survival than those harboring PIK3CA mutation only in their tumors. These findings suggest that the underlying molecular mechanism of PIK3CA mutations in luminal A patients can participate in a positive regulatory loop, and furthermore the positive regulatory loop (PDGF-D/FLT1/SHC1) has a predictive power for the survival of the PIK3CA-mutated luminal A patients.
Highlights
Whole-genome and whole-exome sequencing from tumor samples have identified mutations that are enriched in tumor samples in comparison to germline cells
A comprehensive analysis of the mutational landscape across 12 major cancer types revealed that PIK3CA mutations occurred in 35.5% of all breast cancer tumors, but luminal A subtype tumors had the highest frequency at 46.6% of all the samples harboring the mutation [2]
There was no significant difference in survival between these two groups (P = 0.233, log-rank test) (Figure 1), suggesting that PIK3CA mutation alone is not associated with clinical outcomes
Summary
Whole-genome and whole-exome sequencing from tumor samples have identified mutations that are enriched in tumor samples in comparison to germline cells. Genetic mutations in tumors are very heterogeneous, meaning that the type and frequency of mutated genes in patients are different between cancer types, and within a same cancer type. The extent of such heterogeneity has recently been demonstrated in genome sequencing of breast cancer samples by The Cancer Genome Atlas (TCGA) [2]. This feature of the cancer mutations makes it challenging to interpret the data and very hard to conduct clinical predictions using genome sequencing data. The luminal subtypes A/B are often characterized by the expression of estrogen receptor (ER+) and represent $70% of breast cancer samples [3]
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