A step-by-step guide to the new Mutect2 Panel of Normals
Workflow
The three steps to create a panel of normals are:
Step 1: Run Mutect2 in tumor-only mode for each normal sample:
gatk Mutect2 -R reference.fasta -I normal1.bam --max-mnp-distance 0
-O normal1.vcf.gz
gatk Mutect2 -R reference.fasta -I normal2.bam --max-mnp-distance 0
-O normal2.vcf.gz
... etc.
Step 2: Create a GenomicsDB from the normal Mutect2 calls:
gatk GenomicsDBImport -R reference.fasta -L intervals.interval_list
\
--genomicsdb-workspace-path pon_db \
-V normal1.vcf.gz \
-V normal2.vcf.gz \
-V normal3.vcf.gz
Step 3: Combine the normal calls using CreateSomaticPanelOfNormals:
gatk CreateSomaticPanelOfNormals -R reference.fasta \
--germline-resource af-only-gnomad.vcf.gz \
-V gendb://pon_db \
-O pon.vcf.gz
NOTE: get the af-only-gnomad.vcf.gz file from the following link:
https://console.cloud.google.com/storage/browser/gatk-best-practices/somatichg38;tab=objects?prefix=&forceOnObjectsSortingFiltering=false
Longitudenal cancer analysis
3 tumor samples: initial, remission, relapse
Analysis 1
first identify the somatic variants in the diagnosis sample (relative to germline).
Next identify the somatic variants in the relapse sample (relative to germline).
Then plot the somatic variant VAFs in the diagnosis sample on one axis, and the somatic variant
VAFs in the relapse sample on the other axis. This is called a 2d comparison plot
here: https://github.com/genome/sciclone
Analysis 2
Done using SuperFreq analysis
Oncothreads
https://academic.oup.com/bioinformatics/article/37/Supplement_1/i59/6319672
https://www.nature.com/articles/nmeth.3088
https://www.bio-itworldexpo.com/19/data-visualization
10:40 Longitudinal and Context Visualization for Precision Oncology
Jeremy Goecks, PhD, Assistant Professor of Biomedical Engineering and Computational Biology,
Oregon Health and Science University
The goal of precision oncology is to find effective treatments for each patient’s cancer based its
molecular profile. Visualization plays a key role in precision oncology, helping to understand and
integrate longitudinal and complex data analyses and then communicate results to physicians,
patients, and other stakeholders. We will discuss our work applying visualization for precision
oncology and identify opportunities and challenges for visualization in precision oncology going
forward.
SuperFreq
Tracking clonal evolution within a cancer:

Monitoring the expansion and contraction of distinct clones helps to reveal the mutations that
initiate the disease and those that drive progression

it can help to identify recurrent patterns associated with therapy resistance



it can help detect the cause of relapse or drug resistance
identify early driver mutations, or track the course of metastasis
SuperFreq is a cancer exome sequencing analysis pipeline that integrates
 identification of somatic single nucleotide variants (SNVs) and copy number alterations
(CNAs)
and
 clonal tracking for both