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This result suggests that these mutations could predict HCC development.
In conclusion, our application of the machine learning technique to somatic mutations could predict some primary tumor sites, such as the large intestine, liver, skin, pancreas, and lung.
Several preclinical studies suggested that PIK3CA mutations could predict response to PI3K and mTOR inhibitors, although mutations in the mitogen-activated protein kinase (MAPK) pathway (KRAS, NRAS, BRAF) might mediate resistance [ 57, 58].
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Sourcing from the global repository of cancer-associated somatic mutations we could predict a large set of putative NAMs leading to downstream rewiring (Experimental Procedures; Table S1).
Follow-up studies showed that assessment of both the loss of PTEN expression and activating mutations in PIK3CA could predict the risk for HER2 amplified tumor progression.
In particular, in lung cancer EGFR mutations or EGFR amplification could predict response to EGFR tyrosine kinases inhibitors (Shigematsu and Gazdar, 2006).
We also evaluated personal and family history characteristics in our data that could predict mutation status.
We found that patient MHC-I genotype-based scores could predict which mutations were more likely to emerge in their tumor.
These findings raised the important question as to whether expression of GRN in plasma could predict GRN mutation status and could be used as a biological marker to identify GRN mutation carriers.
To determine if expression of GRN in plasma could predict GRN mutation status and could be used as a biological marker, we optimized a GRN ELISA and studied plasma samples of a consecutive clinical FTLD series of 219 patients, 70 control individuals, 72 early-onset probable Alzheimer's disease patients and nine symptomatic and 18 asymptomatic relatives of GRN mutation families.
The combined mutations in NRAS, BRAF, and PIK3CA could predict resistance to anti-EGFR moAbs with a statistically significant value, as recently also highlighted by Ciardiello and colleagues [ 17].
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