Embracing Heterogeneity:
The Freenome, Information, and Early
Disease Detection
Imran S. Haque, PhD
Cancer Crosslinks
25 Oct 2017

@ImranSHaque@freenome

The liquid in “liquid biopsy”

The “freenome”: small molecules, macromolecules:
proteins, RNAs, circulating cell-free DNA (cfDNA).
cfDNA arises from many different tissues in thebody. Have known for >20 years that some amountof cfDNA in cancer patients comes from the tumor:
circulating tumor DNA (ctDNA).
Today’s great hope: using ctDNA to predict
treatment, response, and existence of cancer.
Image credit: KnuteKnudsen, Wikipedia

My claim:
Measurements of circulating tumor DNA(ctDNA) will not solve the clinicalproblem of detecting early stage cancers.
but integrating multi-analyte signals beyond ctDNA
using modern machine learning will.

About Me
Chief Scientific Officer at Freenome

Formerly VP scientific affairs atCounsyl: early tech dev and researchin medical and population genetics,
cancer genetics, assay development.

PhD CS Stanford: large-scale machinelearning for drug discovery.

About Freenome
Early-stage startup based in South San
Francisco, working on early diagnosis andearly intervention in cancer.
Our technical vision:
The cell-free “Freenome” has significant
information beyond sequence variation.
Decoding this information will require ajoint effort in assay development,
computational biology, and machinelearning, with significant advances to bemade in all three.

35+ People at Freenome$77M20+6Total financing: Andreessen
Horowitz, Google Ventures, Polaris,
Founders Fund, and others

Clinical partners: Multi-cancergroups sharing cohorts for sample
collection + collabs for publication

Paid pharma partners:
Including Fortune 500 biopharmaclients

OutlineI
How do tumor fraction, cfDNA concentration, and heterogeneity come together todefine the limitations and costs of mutation-detection liquid biopsy?
II
Where can we find biological opportunities to go beyond variant
calling in the development of cancer early detection?
III
What are the statistical opportunities that may enable us to avoid
the historical challenges of biomarker discovery?

I: Statistical Limitations of Liquid Biopsy

Detecting cancer by flipping coins
With a fair coin, I expect one
heads if I flip twice.
But if I’m unlucky, I may needmore. With >95% probability, I will
see at least one if I flip 8 times:
If my coin is unfair, I might needto flip many more times! If p=0.10,I need to flip thirty times to see atleast one heads with >95% conf!
Can treat cfDNA molecules as coins: “heads” for molecules w/a tumor mutation.

Mutation detection as a binomial process
Given N unique molecules at agiven site and p probability of any
given molecule being tumor
derived, sensitivity = the binomial
probability of seeing at least one
tumor derived molecule.
As VAF/TF drops, need much more
depth to recover high sensitivity.
0.01% has been claimed as the VAF
upper bound for early detection.
Aravanis AM et al, Cell 2017

Real world VAF is <0.1% in early-stage cancer

30-50% of early-stage patients have max VAF < 0.1%; binomial
bound likely reflects what we see in experiments; the deeper
you sequence, the more you find.

Phallen J et al. Sci Transl Med 2017

[cfDNA]: ~1 ng/mL blood
We’ve so far assumed that we could
sequence arbitrarily deeply, but we needto have unique reads, which imposes
input requirements.
In a production test, we care more about
the tails of the distribution than themedian: can’t fail 50% of your samples
for insufficient input!
95% of healthy indivs have >= 2.3 ng
cfDNA/mL plasma = ~1.2 ng/mL blood.
Phallen J et al Sci Transl Med 2017

ctDNA is too rare in early-stage cancer
95% of patients have >= 2.3 ng cfDNA
per mL plasma = ~1.2 ng/mL blood.
In order to have 95% sensitivity at 0.01%MAF, we need 30,000 unique genomic
equivalents = 90ng.
With 100% process efficiency, we’dneed a 75mL blood draw; with a more
realistic 25-50% efficiency, it’s150-300mL!
Phallen J et al Sci Transl Med 2017

Idea: Aggregate TF over a larger ROI
We assumed in the previous section that there was exactly one mutation weneeded to detect from the tumor, at a particular MAF. But tumors have many
mutations, sporadic and recurrent; what if it were OK to detect any of them?

Kandoth C et al, Nature 2013
Ciriello G et al. Nat Genet 2013Somatic heterogeneity in cancer drivers is pervasive
Deep sequencing reveals significant somatic heterogeneity present in normal
tissue and in plasma of healthy individuals. There is significant overlap between
driver gene mutations in cancer and somatic variants found in healthy individuals:
AgeVAF, Clonal
Hematopoiesis

Fraction of
population

>65yr

> 10%

10%

Avg 44yr

0.16-5.28%

16%

<50yr

>=0.1%

10%>70yr>=0.1%~40%

This poses a filtering/PPV problem for mutation detection.

Clonal landscape of 1 sq cm ofsun-exposed eyelid skin.

Genovese G et al. NEJM 2014
Phallen J et al. Sci Transl Med 2017
Razavi P et al. ASCO 2017
Martincorena I et al. Science 2015

Aggregating VAF: ↓ input, ↑ sequencing
Impact: can treat vars as indistinguishable inthe binomial sampling model, soeffective VAF <= Σ VAFi
→ Reduced input requirement(we use more of each genome)

But: seq bandwidth increases; reads fromless-altered regions are less powerful.
→ Increased sequencing requirement

Newman AM et al Nat Med 2014

NGS error correction imposes a depth penalty
Another hitch: NGS reads have an error ratearound 0.1%-0.5%, which is >= the mutation
rate we want to detect.
A variety of methods (SSCS, DCS, iDES,TEC-Seq) can correct these errors throughincorporation of molecular barcodes andoversampling: trading depth for error rate.
In real-world use, error correction requires
5-10x fold increase in read depth:
30,000x req’d depth = 150-300Kx raw depth

Schmitt MW et al. PNAS 2012
Newman AM et al. Nat Biotech 2016
Phallen J et al. Sci Transl Med 2017

ctDNA-based detection is clinically insufficient
Cancer types

Staging

SamplesPanel

Avg Raw Depth

Avg Eff Depth

Clin. Sens.

Breast, Lung,
Colorectal,
OvarianI / II

N=13881kb

38,589x

6,182x59-71%

Breast, Lung,
Prostate

metastatic

N=124

2100kb60,000x3,000-4,000x89%

Phallen J et al, Sci Transl Med 2017
Razavi P et al. ASCO 2017

ctDNA-based detection is clinically insufficient
Cancer types

Staging

SamplesPanel

Avg Raw Depth

Avg Eff Depth

Clin. Sens.

Breast, Lung,
Colorectal,
OvarianI / II

N=138

81kb

38,589x

6,182x59-71%

Breast, Lung,
Prostate

metastatic

N=124

2100kb60,000x3,000-4,000x89%

Non-cfDNA

Staging

TP+FN / Total

Modality

FN SetClin. Sens.

Ovarian

48% I/II

42+5 / 50,078

CA-125 + ultrasound1yr followup

89.4%

Colorectal

93% I/II/III

60+5 / 9,989

FIT + DNA

Colonoscopy

92.3%

Even large panels, with moderately high depth, on
metastatic patients struggle to exceed the >90%
sensitivity that existing screens achieve in early stage.

Phallen J et al, Sci Transl Med 2017
Razavi P et al. ASCO 2017
Menon U et al. Lancet Oncol 2009
Imperiale TF et al. NEJM 2014

Summary: early detection by mutation detection
Desiderata and assumptions:● <=5% of samples fail test for insufficient input● 95% sensitivity to detect a cancer-derived allele● 50% process efficiency tube->sequencer; 5x oversampling for error corr.
● 100% on-target rate for capture● “$1000 genome” sequencing costs: $1000 / 30x3Gbp● Other reagents, labor, etc. cost $0.
VAF
95%sens

CorrecteddepthRaw
depth

Input vol.
(blood)TEC-Seq58 genes
81kb

GRAIL508 genes
2,000Kb

WES~20k genes
50,000Kb

Tumor LB 0.1%

3,000x15,000x15mL$14$340$8,300

Early detection 0.01%

30,000x150,000x 150mL

$140

$3400

$83,000

II: ...now what?
(Biological opportunities)

Concentration vs quantity
The fundamental limitation affecting ctDNA for early detection is quantity: at 0.01%
concentration, there aren’t enough copies of the genome present per mL to bedetectable.
Will highlight two options today:
1. Find tumor-derived material with count > 0 even at concentration <0.01%
2. Find informative material in the non-tumor-derived 99.99%

Tumor-derived macromolecules
DNA only has CN~=2 in cells. Other macromolecules have much higher copy
number, so they may have >0 count even in small, low-concentration samples.

Combining ctDNA (KRAS 2-codon) assay
with quantification of 1-4 proteins boosted
sensitivity in early stage and small
pancreatic tumors, while maintaining
specificity (1/182 FPR).

Cohen JD et al. PNAS AOP Sep 2017

Non-tumor-derived material
Immune surveillance is involved in carcinogenesis and early clearing of cancer.
Macromolecules: differential cytokine andantibody production are observed in cancer
vs healthy individuals.

Cytology: Different immune cell populations areobserved in different tumor types, with an effecton cancer prognosis.
Hanash SM, Pitteri SJ, Faca VM. Nature 2008.
Gentles AJ et al. Nat Med 2015.

Integrative analysis for early detection
ctDNA alone provides neither the sensitivity
nor specificity to make a sufficient early
detection assay.

But there is good literature evidence to suggestthat combining approaches can boost theperformance of a detection protocol to usefullevels.

xkcd #1831

Biomarker discovery has not been easy
This is highly multiparametric and relies on statistical recovery of signals from ahigh dimensional space. The field is littered with failures from confounders: stressresponse, genotype, circadian rhythms, collection conditions (anesthesia), etc.

Example: Genotype strongly affects
levels of circulating plasma proteins,
including those used as biomarkers:

Cohen JD et al. PNAS AOP Sep 2017
Suhre K et al. Nat Comm 2017.

III: ...so really, now what?
(Statistical opportunities)High dimensional modeling: beyond n vs p
The fundamental limitation of statistical modeling is one of information flow:
1. How many bits of (conditional) information are present in our source data?
2. How many bits needed to discover the label-conditional structure in our data?
3. How many bits needed to classify instances in our target classes?
Older biomarker discovery techniques constrained by (1) and (2): enough apparent
information in the samples to classify them, but not enough to factor out the hidden
confounders.

Mo’ data, mo’ problems
New methods offer the potential to secure much larger amounts of information persample, but simply acquiring more data is not a panacea:- Early GWAS: failure to account for ancestry confounding- Early proteomics biomarkers: failure to account for sample acquisition biases
The great challenge is (2): building models with the appropriate factorization toseparate relevant confounders. Structure learning requires much more data thanjust predicting labels directly!
Typical practice: specify confounders up front, and train filters or linear models toremove biases explicitly.

A simple example: variant calling
Variant callers traditionally built with a variety of custom-trained filters: sequence
context, allele balance, strand bias, etc.
DeepVariant: automatically trained convolutional neural net on NA12878 platinum
genome outperformed hand-tuned GATK HaplotypeCaller

Modern methods have potential for
automatic discovery of conditional
structure in complicated genomics
data.
Poplin R et al. bioRxiv 092890 (2016).

When adding even mo’ problems can help
Variant calling is unique: enormous amounts of training/validation data, relatively
simple conditional dependence structure.
Biomarker discovery involves complex confounders, small N, large data per sample,and relatively few labels. Fundamental challenge: we are constrained by theinformation content in our label set, not our feature set.

Idea: use external data sets and problems to provide the information for learning
structure; save label bits for our own problem: multi-task and transfer learning.

Vision: transfer learning for biomarker discovery
Prior limitation was a lack of total label data to train structure in statistical models.
Newer methods allow us to integrate large-scale external data sets withhigh-dimensional, multi-parametric data extracted from newer assay technology.

Example: expression and genotype data from cfDNA.

Ulz P et al. Nat Genet 2016
Snyder MW et al. Cell 2016

Conclusions

Summary
1. Variant-calling based early detection has serious limitations on sensitivity:
a.
b.
c.

Possibly insufficient cfDNA for it to work at all.
Multiple OOM in sequencing cost away from commercial viability.
Significant specificity challenges from somatic heterogeneity.

2. Markers beyond ctDNA have potential but pose daunting statisticalchallenges.
3. Advances in multi-task and transfer learning may offer a route tostatistically robust biomarker development.

A vision for the next 5 years
1. Large, publicly-available reference compendia of cell-, tissue-, andpopulation-specific genomic information enable large-scale machinelearning of the causal structure behind cancer genesis, maintenance, andclearance.
2. ML-derived methods enable point-in-time population screening withgreater accuracy and adherence for conditions with existing screens, andmeet the unmet need in unsolved conditions.
3. Repeat longitudinal testing in the general population enables thecalibration of tests to individual variability, minimizing overtreatment andinforming discovery of fundamental biology.

xkcd #1838</talk>ihaque@freenome.com@ImranSHaque@freenome
We’re hiring: freenome.com/careers