1) OncoRep is an RNA-Seq based reporting tool that analyzes breast cancer patient sequencing data and presents results to clinicians to guide treatment decisions. 2) It performs molecular classification, identifies altered genes and pathways, detects gene fusions, and reports clinically actionable mutations and potential drug treatments. 3) Results are presented in an interactive HTML report that provides visualization of omics data to help clinicians and tumor boards make informed treatment choices for individual patients.

1. LATEX TikZposter
OncoRep: A n-of-1 reporting tool to support genome-guided treatment
for breast cancer patients using RNA-sequencing
Tobias Meissner1∗
, Kathleen M. Fisch1∗
, Louis Gioia1
& Andrew I. Su1
Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA, USA
OncoRep: A n-of-1 reporting tool to support genome-guided treatment
for breast cancer patients using RNA-sequencing
Tobias Meissner1∗
, Kathleen M. Fisch1∗
, Louis Gioia1
& Andrew I. Su1
Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA, USA
Abstract
Introduction Breast cancer is the leading cause of cancer among females. Being a
heterogeneous disease, it comprises multiple tumor entities associated with distinctive
histological patterns, different biological features and clinical behaviors. Next genera-
tion sequencing allows us to study this heterogeneous disease in multiple dimensions.
This provides in depth insight into tumor pathogenesis on the individual patient level
paving the path to individualized medicine. The advent of individualized medicine
introduces a shift in how cancer patients will be treated in the future, away from the
one drug-one disease paradigm towards the idea of bringing the right drug to the right
patient.
Probem Challenges that arise with this paradigm shift are i) preprocessing and
analyzing sequencing data in the n-of-1 setting ii) extracting relevant information
from various layers of omics data iii) integrating omics data with drug databases,
(iv) presenting the information to the clinician in an understandable manner, and (v)
completing these steps in a timely manner to provide clinically relevant and actionable
targets to a tumor board or the treating physician.
Results To address these challenges, we present OncoRep, an RNA-Seq based n-
of-1 reporting tool for patients with breast cancer. It includes prospective molecular
classification, detection of altered genes, and pathways, identification of gene fusion
events, clinically actionable mutations and it reports suitable drugs based on identified
actionable targets. It presents visualization of these omics data in an approachable html
based interactive report, providing the clinician and tumor board with a tool to guide
the treatment decision making process.
Aims
The aim has been to create a RNA-Seq based prospective patient report for patients
with breast cancer in the n-of-1 setting. The following challenges were to be addressed:
• Automated preprocessing and analysis of RNA-Seq data in the n-of-1 setting
• Integration with drug databases
• Provide the clinician / tumor board with a tool to interactively browse results
• Provide a PDF based patient report
Reference Cohort
The reference cohort consistis of 947 breast
cancer, 106 matched tumor normal & 4 nor-
mal breast tissue samples from TCGA, Illu-
mina body map & GEO dataset GSE52194.
It is used to:
• Train classifiers (receptor status, molecu-
lar classification)
• Identify differentially expressed genes &
altered pathways in a new patient sample Figure 1: Heatmap of the 26 genes constituting the
ER classifier. Prediction error: 4%.
Fully Automated Reporting Pipeline
Figure 2: Flowchart illustrating tools used and their interaction within OncoRep. The four main branches are (left to
right) variant calling, fusion gene detection, quality control and gene expression quantification and analysis. Results from
each branch are analysed, annotated and integrated and a .html report is created at the final stage of the pipeline.
N-of-1 Preprocessing
Problem Scale counts from new patient relative to
reference, to be able to transfer clasifiers generated on
the reference to a new patient sample.
Solution Store the denominator from the size factor
estimation of the reference samples.
New Sample Scale: ratio counts from new sample
and stored denominator from reference cohort.
ˆSj = median
kij
m
v=1
kiv
1/m
i = 1...n genes
j = 1...m samples
kij counts
Gene Expression Reliability
Problem Which level ob abundance de-
scribes biologically meaningful expression?
• High dynamic range of RNA-Seq enables
detection of transcripts with low expression
• Random variation is high in regions with
low expression
Solution Distribution of 156 genes that are
not expressed in any sample of the reference
help to define a reliable expression cutoff value
for each sample. Figure 3: Defining a expression value cutoff to esti-
mate reliable gene expression in the N-of-1 setting.
BRCA Sample - Selected Results
1. Gene Expression
Figure 4: In the sample, 17,049 genes are reliable expressed;
1,489 genes are up- and 1,064 downregulated. The wordcloud
displays 89 altered genes out of 411 BRCA relevant genes.
2. Pathways
DB Pathway Pvalue
KEGG Cell cycle 1.1e-6
KEGG p53 signaling pathway 4.3e-4
NCI Aurora B signaling 1.6e-7
Reactome Translation 2.5e-9
... ... ...
Table 1: In total 49 Pathways are significantly al-
tered within the sample across the four analyzed pathway
databases (KEGG, Biocarta, NCI, Reactome). Relevant
altered pathways include p53 signaling and Aurora b sig-
naling.
3. Variants
CHROM POS rsID Gene Effect
2 33585796 rs4422143 LTBP1 MODERATE, MISSENSE, NON SYNONYMOUS CODING, V999A
3 47163169 . SETD2 MODERATE, MISSENSE, NON SYNONYMOUS CODING, E986G
6 12122645 rs6900196 HIVEP1 MODERATE, MISSENSE, NON SYNONYMOUS CODING, T873A
7 91714911 rs1063242 AKAP9 MODERATE, MISSENSE, NON SYNONYMOUS CODING, P2979S
14 23451430 . AJUBA MODERATE, MISSENSE, NON SYNONYMOUS CODING, G16C
15 64067092 . HERC1 MODERATE, MISSENSE, NON SYNONYMOUS CODING, R244H
15 90633828 . IDH2 MODERATE, MISSENSE, NON SYNONYMOUS CODING, L86I
19 58118480 . ZNF530 MODERATE, MISSENSE, NON SYNONYMOUS CODING, S529R
Table 2: Using the SNPirR variant calling pipeline, from 906.884 identified raw variants, 34.842 varaiants pass the SNPiR
filtering steps. Using additional filtering steps, we are able to identify 161 candiate variants, of which eight are predicted to
be drivers (displayed above) and six have been identified in cancer before as reported by COSMIC.
4. Fusion Genes
5’ 3’ Description pDriver Exp.gain
HAS2 TMEM66 NA 0 -0.54
ZNF525 ZNF845 readthrough NA NA
Table 3: Two gene fusion candidates have been detected in
the sample.
5. Matched Drugs
Target Drugs
MMP12 Marimastat
TOP2A Etoposide Doxorubicin
TYMS Pemetrexed Capecitabine
Table 4: Drug matching identified 49 FDA approved an-
tineoplastic drugs that target altered genes in the sample.
HTML-Report
Figure 5: Screenshots of the OncoRep HTML-Report showing results from differential expression calling, prediction of
receptor status and molecular classification (left), and drug matching based on differentially expressed genes (right).
Info
Source Code
https://bitbucket.org/sulab/oncorep
Contact
Tobias Meissner meissto@scripps.edu @meissner t
Andrew Su asu@scripps.edu @andrewsu