Population scale transcriptomics for

precision oncology

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Jeff Leek

VP & CDO

Professor Biostatistics Program, PHS

J Orin Edson Foundation Chair

@jtleek

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1

www.jtleek.com

“Talks”

Fred Hutchinson Cancer Center

2

jtleek@fredhutch.org

Seeking new collaborations in

machine learning for clinical translation

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Fred Hutchinson Cancer Center

3

Precision oncology:

A tour of data plumbing challenges

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Jeff Leek

VP & CDO

Professor Biostatistics Program, PHS

J Orin Edson Foundation Chair

@jtleek

​

4

5

Fred Hutchinson Cancer Center

What gene expression patterns are prognostic of colorectal cancer metastasis ?

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Fred Hutchinson Cancer Center

https://pubmed.ncbi.nlm.nih.gov/25049118/

Q: What genes are prognostic of colorectal cancer metastasis?

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Data

Processing

Computing

Metadata

Analysis

A: This gene signature is/isn’t a potential prognostic biomarker.

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8

Fred Hutchinson Cancer Center

Find a researcher with access to patient samples

What genes are prognostic for metastasis?

Find a researcher with access to patient samples

What genes are prognostic for metastasis?

Collect patient samples and information

3~6 months

Find a researcher with access to patient samples

Collect patient samples and information

Extract DNA/RNA from samples

Sequence samples

3~6 months

1-2 wks

2-4 wks

What genes are prognostic for metastasis?

Find a researcher with access to patient samples

Collect patient samples and information

Extract DNA/RNA from samples

Sequence samples

Process sequencing data

33-6 months

1-3 months

1 month - 1+ years

3~6 months

1-2 wks

2-4 wks

Analyze data and answer biological question

Data cleaning

What genes are prognostic for metastasis?

Find a researcher with access to patient samples

Collect patient samples and information

Extract DNA/RNA from samples

Sequence samples

Process sequencing data

33-6 months

1-3 months

1 month - 1+ years

3~6 months

1-2 wks

2-4 wks

Total: 2+ years

Analyze data and answer biological question

Data cleaning

What genes are prognostic for metastasis?

Data

Processing

Computing

Metadata

Analysis

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Fred Hutchinson Cancer Center

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Fred Hutchinson Cancer Center

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Fred Hutchinson Cancer Center

SAMPLE SIZE

N =

18

Fred Hutchinson Cancer Center

N =

($ YOU HAVE)

($ PER SAMPLE)

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Fred Hutchinson Cancer Center

Langmead & Nellore, Nat Rev. Genet. 2018

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Fred Hutchinson Cancer Center

http://www.washingtonpost.com/sf/national/2015/06/27/watsons-next-feat-taking-on-cancer/

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Data sharing is improving over time

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...but data aren’t always easy to use

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Data sharing is improving over time

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Data

Processing

Computing

Metadata

Analysis

25

Fred Hutchinson Cancer Center

Find a researcher with access to patient samples

Collect patient samples and information

Extract DNA/RNA from samples

Sequence samples

Process sequencing data

33-6 months

1-3 months

1 month - 1+ years

~6 months

1-2 wks

2-4 wks

Total: 1.5+ years

Analyze data and answer biological question

Data cleaning

What genes are prognostic for metastasis?

AUCAGUCGAUCACCGAU

transcription

RNA

translation

protein

ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT

DNA

M

M

M

slide adapted from alyssa frazee

AUCAGUCGAUCACCGAU

transcription

RNA

translation

protein

ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT

DNA

M

M

M

RNA

slide adapted from alyssa frazee

Genome

Transcripts

Reads

@22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2

CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA

+

GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD

@22:16362385-16362561W:ENST00000440999:3:177:-56:294:S/2

GCGTGAGCCACAGGGCCCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCT

+

@=ABBBBIIIIIIIIHHGGGGIIDBDIIIIIIGIIIIHIIIIHFDD@BBDBGGFIDEE8DCC/29>BGFCGHHHGF

@22:16362385-16362561W:ENST00000440999:4:177:137:254:S/1

TCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCAAGGCCTGAACTACCTGCaGTGGGGAGCACCTCAGGGTTT

+

DDGBBCGGGIGGGBDDDHIIGGDGD77=BDIIIIIIIIFHHHHIIIHEFFHGGDD8A>DEGHHIFDDHH8@BEDDI

@22:16362385-16362561W:ENST00000440999:5:177:68:251:S/2

AGGGTTTGCCCAGGCAACCAGCCAGCCCTGGTCCAAGGCATCCTGGAGCGAGTTGTGGATGGCAAAAAGACNCGCC

+

HIGHIHFHEGE4111:.;8@?@HDIIIIIIIEGGIHHHIIGA?=:FIIIDD8.02506A8=AC#############

@22:16362385-16362561W:ENST00000440999:6:177:348:453:S/1

AAGGCCTGAACTACCTGCGGTGGGGAGCACCTCAGGGTTTGCCCAGGCAACCAGCCAGCCCTGGTCCAAGGCATCC

+

B9?@8=42:E@GDEDIIIIIGGHIIIFBEEAGIIDIIDHHGGHIIEGEIIIIIHIHFHFFEEFGGGGGB88>:DGH

@22:51205934-51222090C:ENST00000464740:132:612:223:359:S/2

GGAAGTATGATGCTGATGACAACGTGAAGATCATCTGCCTGGGAGACAGCGCAGTGGGCAAATCCAAACTCATGGA

+

IIEHHHHHIIIIIIIHGGDGHHEDDG8=;?==19;<<>D@@GGGIIHIIHGGDDHGBA=ABEG@@DFCCAA<:=>8

@22:51205934-51222090C:ENST00000464740:125:612:-1:185:S/1

TGGAGTGCGCTGCGGCGCGAGCTGGGCCGGCGGGCGTGGTTCGAGAGCGCGCAGAGTCCAGACTGGCGGCAGGGCC

+

HHIIIHIDGG@;=@GIIIIIDDGBBBEDB@8>5554,/':9B@@C?==@1:2@?=GG=;<HHHHGIIHHEC-;;3?

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3 gb

Carefully!!

coverage vector

2

6

0

11

6

Genome

(DNA)

junction

counts

3

3

Genome

(DNA)

~71.7 mb

Data

Processing

Computing

Metadata

Analysis

37

Fred Hutchinson Cancer Center

SRA

Human

RNA-seq

Illumina

≈22,000 samples

≈50,000 samples

≈60,000 samples

≈247,000 samples

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http://rail.bio/

Slide courtesy Ben Langmead

http://blogs.citrix.com/2012/10/17/announcing-general-availability-of-sharefile-with-storagezones/

slide adapted from andrew jaffe

Obstacle: our research moves (spot) markets

Spike in market price due to preprocessing job flows

�expression data for ~70,000 human samples

GTEx

N=9,962

TCGA

N=11,284

SRA

N=49,848

samples

expression estimates

gene

exon

junctions

ERs

< 1,000 junctions

> 20,000 junctions

=

A global view of transcript variability

Nellore et al. Genome Biology 2016

46

Fred Hutchinson Cancer Center

Re-annotating the transcriptome

https://www.nature.com/articles/s41586-022-04558-8

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Fred Hutchinson Cancer Center

Data

Processing

Computing

Metadata

Analysis

48

Fred Hutchinson Cancer Center

Find a researcher with access to patient samples

Collect patient samples and information

Extract DNA/RNA from samples

Sequence samples

Process sequencing data

33-6 months

1-3 months

1 month - 1+ years

~6 months

1-2 wks

2-4 wks

Total: 1.5+ years

Analyze data and answer biological question

Data cleaning

What genes are prognostic for metastasis?

What % expressed?

New genes?

Important outliers?

Prognostic signatures

Sequence data

Process/quantify

ACTACTTT

Metadata

Clean/predict

�expression data for ~70,000 human samples

GTEx

N=9,962

TCGA

N=11,284

SRA

N=49,848

samples

expression estimates

gene

exon

junctions

ERs

Answer meaningful questions about human biology and expression

�expression data for ~70,000 human samples

samples

phenotypes

?

GTEx

N=9,962

TCGA

N=11,284

SRA

N=49,848

samples

expression estimates

gene

exon

junctions

ERs

Answer meaningful questions about human biology and expression

SRA phenotype information is far from complete

z

z

z

Even when information is provided, it’s not always clear…

Sex across the SRA:

Even when information is provided, it’s not always clear…

Sex across the SRA:

“1 Male, 2 Female”, “2 Male, 1 Female”, “3 Female”, “DK”, “male and female” “Male (note: ….)”, “missing”, “mixed”, “mixture”, “N/A”, “Not available”, “not applicable”, “not collected”, “not determined”, “pooled male and female”, “U”, “unknown”, “Unknown”

Goal :��to accurately predict critical phenotype information for all samples in recount2

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gene, exon, exon-exon junction and expressed region RNA-Seq data

SRA

Sequence Read Archive

N=49,848

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TCGA

The Cancer Genome Atlas

N=11,284

GTEx

Genotype Tissue Expression Project

N=9,662

Missingness limited in GTEx phenotype data

GTEx

Sex across GTEx:

Goal :��to accurately predict critical phenotype information for all samples in recount2

Ellis et al. Nuc. Acids Res. 2018

​

gene, exon, exon-exon junction and expressed region RNA-Seq data

SRA

Sequence Read Archive

N=49,848

​

divide samples

build and optimize phenotype predictor

predict phenotypes across SRA samples

test accuracy of predictor

TCGA

The Cancer Genome Atlas

N=11,284

GTEx

Genotype Tissue Expression Project

N=9,662

predict phenotypes across samples in TCGA

Training Data

Validation

Data

Test

Data

Validation

Data

99.9%

Sex prediction is accurate across data sets

99.8%

99.0%

86.3%

To assess misreporting of sex in the SRA, we can use Y-chromosome expression

XX

XY

reported female

reported male

​

predicted female

predicted male

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Fred Hutchinson Cancer Center

Expression from the Y chromosome suggests misreporting of sex in the SRA

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Fred Hutchinson Cancer Center

Ellis et al. Nuc. Acids Res. 2018

�expression data for ~70,000 human samples

samples

phenotypes

GTEx

N=9,962

TCGA

N=11,284

SRA

N=49,848

samples

expression estimates

gene

exon

junctions

ERs

Answer meaningful questions about human biology and expression

What % expressed?

New genes?

Important outliers?

Sequence data

Process/quantify

Metadata

Clean/predict

ACTACTTT

Primary vs.

Metastatic

Data

Processing

Computing

Metadata

Analysis

67

Fred Hutchinson Cancer Center

Find a researcher with access to patient samples

Collect patient samples and information

Extract DNA/RNA from samples

Sequence samples

Process sequencing data

3-6 months

1-3 months

1 month - 1+ years

~6 months

1-2 wks

2-4 wks

Total: 1mo - 1+years

Analyze data and answer biological question

Data cleaning

What genes are prognostic for metastasis?

What gene expression patterns are prognostic of colorectal cancer metastasis ?

69

Fred Hutchinson Cancer Center

https://pubmed.ncbi.nlm.nih.gov/25049118/

Kim et al. analysis looked to identify genes that contribute to metastasis in colon cancer.

N=18

3. Liver Metastasis (MC)

1. Healthy Colon (NC)

2. Primary Cancer (PC)

Predictions can be used to:��(1) Identify studies of interest��(2) appropriately analyze data

NC: non-cancerous

PC: primary cancer

MC: metastatic cancer

NC: non-cancerous

PC: primary cancer

MC: metastatic cancer

Are the same genes found when sex is included in the analysis?

Predictions can be used to:��(1) Identify studies of interest��(2) appropriately analyze data

NC: non-cancerous

PC: primary cancer

MC: metastatic cancer

Concordance at the Top (CAT) Plots

How similar are the results from Analysis A and Analysis B ?

Analysis A

Analysis B

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Fred Hutchinson Cancer Center

Concordance at the Top (CAT) Plots

How similar are the results from Analysis A and Analysis B ?

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Analysis A

Analysis B

10

10

75

Fred Hutchinson Cancer Center

Concordance at the Top (CAT) Plots

How similar are the results from Analysis A and Analysis B ?

​

Analysis A

Analysis B

10

10

76

Fred Hutchinson Cancer Center

Concordance at the Top (CAT) Plots

How similar are the results from Analysis A and Analysis B ?

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Analysis A

Analysis B

The top results from A and B are the same

Some differences at less significant genes

77

Fred Hutchinson Cancer Center

Concordance at the Top (CAT) Plots

How similar are the results from Analysis A and Analysis B ?

Analysis A

Analysis B

The results of the orange condition are less similar between Analysis A and B than the green condition

78

Fred Hutchinson Cancer Center

Predictions can be used to:��(1) Identify studies of interest��(2) appropriately analyze data

NC: non-cancerous

PC: primary cancer

MC: metastatic cancer

Predictions can be used to:��(1) Identify studies of interest��(2) appropriately analyze data

NC: non-cancerous

PC: primary cancer

MC: metastatic cancer

Predictions can be used to:��(1) Identify studies of interest��(2) appropriately analyze data

NC: non-cancerous

PC: primary cancer

MC: metastatic cancer

Predictions can be used to:��(1) Identify studies of interest��(2) appropriately analyze data

Loss of concordance suggests that differential expression is detecting tissue differences, not cancer-related changes.

We have expression data from both healthy liver and colon samples (GTEx)...

84

Fred Hutchinson Cancer Center

So…what if we compared the MC:PC results with differential expression between colon and liver?

Hypothesis: MC:PC results should be most similar to GTEx colon vs. liver

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Fred Hutchinson Cancer Center

Comparison of results with GTEx colon vs. liver suggests differential expression results detecting tissue differences

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Fred Hutchinson Cancer Center

Data

Processing

Computing

Metadata

Analysis

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Fred Hutchinson Cancer Center

Data

Processing

Computing

Metadata

Analysis

Software

88

Fred Hutchinson Cancer Center

Study explorer

https://jhubiostatistics.shinyapps.io/recount3-study-explorer/

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Fred Hutchinson Cancer Center

Bioconductor packages

recount3

snaptron

dasper

90

Fred Hutchinson Cancer Center

Data

Processing

Computing

Metadata

Analysis

Software

Training

91

Fred Hutchinson Cancer Center

General purpose training

https://www.coursera.org/specializations/genomic-data-science#instructors

92

Fred Hutchinson Cancer Center

recount workshops

http://research.libd.org/recountWorkshop/

93

Fred Hutchinson Cancer Center

Cell specific regulation of gene expression

https://www.nature.com/articles/s41467-022-33523-2

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Fred Hutchinson Cancer Center

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Data Science Lab (DaSL)

Pronounced “Dazzle”

www.hutchdatascience.org

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Data

Processing

Computing

Metadata

Analysis

Software

Training

96

Fred Hutchinson Cancer Center

DaSL Mission

The mission of the DaSL is to coordinate data activities, build community, make data easier to use, and create value for Hutch clinical and research teams with data resources, training, support, partnerships, philanthropy and infrastructure.

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97

Fred Hutchinson Cancer Center

First Year DaSL Activities

Data Needs Study

Data Sharing Support

Training/Community

98

Fred Hutchinson Cancer Center

FH Data Needs Study

Comprehensive data needs study

Lead by HCI Expert

Resulting in a report of data needs

Driving resources to support needs

Sean Kross

data@fredhutch.org

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Fred Hutchinson Cancer Center

Data Sharing Plumbing

Amy Paguirigan

data@fredhutch.org

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Fred Hutchinson Cancer Center

fhdata.slack.com

virtual data community!

Fred Hutchinson Cancer Center

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Thank you