Figure 1 shows the prevalence of aberrations in key driver genes and pathways in

Question

Figure 1 shows the prevalence of aberrations in key driver genes and pathways in PDAC; implicating structural variation as an important mutational mechanism in pancreatic carcinogenesis.

Mutations in key genes and pathways in pancreatic cancer

The upper panel shows non-silent single nucleotide variants and small insertions or deletions. The central matrix shows: non-silent mutations (blue), copy number changes (amplification (>5 copies) represented in red and loss represented in green) and genes affected by structural variants (SV, yellow). Pathogenic germline variants are highlighted with asterisk (*) symbols. The histogram on the left shows the number of each alteration in each gene.

Question: Explain what specific data is shown in the figure above in regards to genetic heterogeneity. Also, please explain the primary challenge when identifying driver mutations in high through put sequencing datasets. Thank you!

Explanation / Answer

The Specific data in regards to genetic heterogeneity that is shown in the figure :

It is a challenging task to discover personalized driver genes that provide crucial information on disease risk and drug sensitivity for individual patients. However, few methods have been proposed to identify the personalized-sample driver genes from the cancer omics data due to the lack of samples for each individual. To circumvent this problem, here we present a novel single-sample controller strategy (SCS) to identify personalized driver mutation profiles from network controllability perspective.

The primary challenges:

SCS integrates mutation data and expression data into a reference molecular network for each patient to obtain the driver mutation profiles in a personalized-sample manner. This is the first such a computational framework, to bridge the personalized driver mutation discovery problem and the structural network controllability problem. The key idea of SCS is to detect those mutated genes which can achieve the transition from the normal state to the disease state based on each individual omics data from network controllability perspective. We widely validate the driver mutation profiles of our SCS from three aspects: (i) the improved precision for the predicted driver genes in the population compared with other driver-focus methods; (ii) the effectiveness for discovering the personalized driver genes and (iii) the application to the risk assessment through the integration of the driver mutation signature and expression data, respectively, across the five distinct benchmarks from The Cancer Genome Atlas. In conclusion, our SCS makes efficient and robust personalized driver mutation profiles predictions, opening new avenues in personalized medicine and targeted cancer therapy.

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