Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT04823871 |
| Other study ID # |
EK 1612/2018 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
November 1, 2018 |
| Est. completion date |
June 30, 2022 |
Study information
| Verified date |
September 2022 |
| Source |
Medical University of Vienna |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
In Phase I the sponsor will systematically test conditions for lavage filtration that
increase tumor cell fraction without reducing tumor mutation yield. The Sponsor will also
transition all lavages to luteal phase timing, when endometrial shedding is least. In Phase
II the Sponsor will examine our data in context of clinical characteristics, particularly
age, to develop a multivariate model that determines optimal mutant allele frequency (MAF)
diagnostic threshold by patient. Furthermore, the sponsor will explore a highly innovative
idea, entailing empirically determining each individual's background mutation load, agnostic
of the aging or mutagenic exposures responsible, and using this as a personalized calibrator
to determine optimal MAF diagnostic threshold.
Description:
APPROACH PHASE I The University of Washington has demonstrated diagnostic proof of principle;
however, further optimization and validation is needed for industrial scale deployment. In
Phase I the UtL filtration method will be optimized (Aim 1), analytical performance on the
commercial workflow validated (Aim 2) and a sample collection milestone met (Aim 3). Aim 1.
Determine utility of uterine lavage filtration to maximize enrichment for tumor derived DNA.
Preliminary testing with two samples indicated that filtration of UtLs to remove clusters of
endometrial cells could increase sensitivity for tumor mutations several folds. This data set
will be expanded by analysing the filtration effect in 10 UtLs from patients with HGSC with
known TP53 mutations. Each UtL will be divided in half: the first half will be filtered and
the second will remain unfiltered. DNA will be extracted from the filtered, filtrate, and
unfiltered fractions and analysed by TP53 DS (total of 30 samples). For each UtL, the DNA
yield in each of the fractions and the MAF of the tumor mutation will be compared. It is
anticipated that the filtered fraction will contain less DNA then the unfiltered fraction but
the tumor mutation will be present at a higher frequency. If sensitivity is improved (i.e.
more is gained by enrichment than is lost by reduction from less available DNA), filtration
will be used on all future samples collected. Aim 2. Validation of optimized assay: accuracy,
precision, limit of detection, and reproducibility. TwinStrand has developed a streamlined DS
workflow using improved adapters, ligation chemistry and a high throughput 96 well
plate-based format amenable to liquid handling robots and compatible with CLIA laboratory
standards.
TwinStrand has also developed an optimized cloud-based analysis pipeline that supports
automated parallel processing of multiple samples. The optimized process for the detection of
TP53 mutations in UtL samples will be validated. To assess accuracy, technical precision, and
lower limit of detection, DNA samples from two individuals that differ in genotype at >5 SNP
sites in or near TP53 will be mixed, in ratios from 1:100 1:5,000, two UtL. The mixtures will
be sequenced at ~10,000x molecular depth in two independent experiments. SNP allele fraction
(AF) will be compared at different dilutions to determine accuracy (expected AF vs. observed
AF), precision (AF variation among replicates), and lowest limit of detection achievable. To
test assay reproducibility in its intended clinical use, sequencing will be repeated on the
30 samples used Aim 1, which will encompass a wide range of tumor MAFs. The Sponsor will
calculate the coefficient of variation among replicates. Based on pilot studies The Sponsor
anticipate excellent reproducibility (CV<5%). Aim 3. Sample collection. Collection of samples
proposed in Phase II will be started under appropriate IRB/Ethic Committee approval at each
participating institution.
After collection, samples are shipped to the Institute for Cancer Research at the Medical
University of Vienna who will perform UtL filtration and DNA extraction.
Paired DNA from Pap smears and peripheral leukocytes will also be extracted. Isolated DNAs
will be assigned a de identification number and shipped to TwinStrand for Duplex Sequencing.
Phase I Milestones:
1. Confirm utility of UtL filtration on tumor mutation enrichment.
2. Quantify streamlined assay's accuracy, precision, lower limit of detection, and
reproducibility on UtLs.
3. Start samples collection for Phase II.
Phase I Products:
1. DS workflow ready for application to UtL DNA at commercial scale in Phase II and Phase
III.
2. Sample bank for Phase II
APPROACH PHASE II Phase II will validate the use of TP53 DS on UtLs for early ovarian cancer
detection in expanded case control patient cohorts encompassing both average and high risk
populations. Predefined parameters that may affect the sensitivity and specificity of TP53
mutation frequency will be examine, and statistically modeled. Clinical sensitivity and
specificity will be maximized by building personalized diagnostic threshold statistical
models using multivariate clinical characteristics, as well as each individual's unique
background mutation load based on leukocyte sequencing. In a subset of patients, the superior
performance of UtL over Pap smear sampling will be confirmed. In addition, a proof of concept
study will be conducted in a sub-set of included patients to study a set of 96 methylation
markers relevant to HGSC in UtLs and corresponding tumor tissue/STICs.
AIM 1. GENERAL POPULATION OVARIAN CANCER SCREENING This aim will develop a biomarker for HGSC
detection in an average risk population. The Sponsor will conduct a case control study that
integrates TP53 DS mutational data with clinic pathological information in order to identify
women with HGSC with maximum sensitivity and specificity. More than 98% of HGSCs carry
mutations in TP53, which means that ultra-deep DS can be cost effectively focused on just a
small genomic region. The product of this aim will be a 200 patient biomarker data set that
demonstrates the cost effective, commercially robust performance of this critically needed
ovarian cancer diagnostic.
Aim 1A: To assess the test performance of TP53 DS on UtLs for HGSC detection in average risk
patients.
Methods: UtL samples from 200 subjects from average risk population will be analyzed via DS:
100 subjects with HGSC (cases) and 100 subjects with lesions that were ultimately found to be
benign after resection, i.e., without cancer (controls). In all patients UtL will be
collected prior surgical intervention for an ovarian mass. Lavage will be carried out during
the luteal phase of the menstrual cycle if pre-menopausal. Surgical specimens will be
pathologically assessed, and a patient will be counted a HGSC positive or a control depending
on the histological results. For cancer patients, the primary tumor will be sequenced via
conventional methods by the Medical University of Vienna for a panel of genes that includes
TP53, BRCA1 and BRCA2. For all de-identified patient samples, collected clinical information
will include: age, smoking history, prior chemotherapy exposure, parity, age-of-menopause,
age-of-menarche, history of oral contraceptive use, cancer family history, and seven-gene
mutation status of the primary tumor. Cases and controls will be aged matched and as wide an
age range as possible will be included to assess the effect of age on sensitivity and
specificity.
Statistical Analysis: A detailed mutation profile for each sample will be generated from the
duplex sequencing output files including: mutant allele frequency (MAF) for all mutated
positions, mutation spectrum, predicted pathogenicity to protein function, relationship to
known hotspots, and overall mutation load (number of mutant nucleotides divided by the total
number of nucleotides sequenced). Following this, samples will be unblinded for
cases-controls status. TP53 MAF from DNA collected by UtLs will be used as predictor for
differentiating between average risk patients (AIM I) with and without HGSC by logistic
regression modelling. Age, smoking history, prior chemotherapy exposure, parity,
age-of-menopause, age-of-menarche, history of oral contraceptive use, cancer family history
and germline mutation status will be considered as potential confounders. Model prediction
will be assessed by cross-validation. An analogous analysis will be applied to the group of
high risk patients (AIM II) where presence and absence of STIC is defining the outcome
variable. Cut off values for mutant allele frequency will be suggested in both cases and
specificity and sensitivity will be estimated including appropriate confidence intervals.
Aim 1B: Improving diagnostic performance with personalized calibration by background mutation
load.
The unprecedented sensitivity of DS led us then others to the novel discovery that cancer
like mutations accumulate at very low levels with age in multiple human tissues. In a pilot
study the sponsor discovered that the average BB mutation load is somewhat higher in UtLs
than other tissues, potentially because of DNA contribution from endometrial tissue, which
replicates extensively prior to menopause. While this did not compromise specificity in the
pilot study, the sponsor recognize that BB could pose an obstacle with some very early tumors
where MAF signal is low, or with very elderly women where background is high. The Sponsor
expect that lavage filtration and luteal phase collection in premenopausal women will reduce
BB signal, but as an added measure the sponsor will examine whether performance can be
further improved by normalizing for an individual's background mutation load. The sponsor
hypothesize that the level of TP53 mutations in circulating leukocytes can serve as an
empirically measured personal calibrator that captures, not only known factors that increase
BB, such as age, but also unknown mutagenic exposures or other factors occurring during a
person's life. Although DNA from leukocytes probably only contributes a minimal amount to the
total pool of BB mutations in UtLs, the sponsor posit that they may serve as a proverbial
"canary in a coal mine" that will be proportionally representative of BB mutations elsewhere
in the body. The BB mutation load in a lavage, itself, cannot be directly measured in a real
world setting when the mutation(s) contributed by a tumor are unknown. The plausibility of
this concept was shown by our initial study of TP53 mutations in peritoneal fluid, which
included a subset of matching blood samples and indicated a strong association between BB and
age.
Methods: DNA from the peripheral blood mononuclear cells (PBMC) component - collected
immediately prior to a surgery - of randomly chosen 50 from 100 HGSC cases and 50 from 100
controls from Aim 1A will be extracted and subjected to TP53 DS at ~10,000x molecular depth.
TP53 mutations from PBMCs will be subtracted from TP53 mutations found in the UtL.
Statistical Analysis: The Sponsor will examine the association between TP53 mutation
frequencies in UtL and leukocytes and will determine whether false negatives in Aim 1A
correspond to cases with increased BB in leukocytes. In addition, the sponsor will analyze
TP53 mutation frequencies in leukocytes as a predictor of case control status, again using
the leave out 10% procedure. This innovative calibration method will be compared with the
simple ROC metrics achieved with the univariate model using a fixed mutation fraction as well
as the adjusted multivariate model developed based on other patient characteristics such as
age. As a scientific question unrelated to the present aims, the sponsor are eager to see
whether leukocyte TP53 mutation load will serve as an independent predictor of patient age,
overall health or history of mutagenic exposures.
Aim 1C. Comparison of diagnostic performance of uterine lavage vs. Pap smear collected DNA.
The first report of using NGS for ovarian cancer detection from a trans vaginal liquid biopsy
relied on Pap smear collection and SafeSeqS as the sequencing technology. The cited
sensitivity of 41% provided an important proof of principle, but also ample room for
improvement. The sponsor have definitively demonstrated the superior accuracy of DS over
SafeSeqS like methods and ongoing studies in our lab indicate limited sensitivity of Pap
smears compared to UtL. However, a direct comparison of the two collection methods on the
same patients remains to be performed. Formally demonstrating the degree of superiority of
UtL over Pap smears will help with commercial market entry (the sponsor note that a related
NCI SBIR has been awarded to PapGene Inc, a company founded by the above-mentioned study's
authors). The superiority of UtL is expected based on the fact that the lavages reach the
fallopian tubes and ovarian surfaces whereas Pap smears rely on disseminated cancer cells
reaching the cervical canal. Nevertheless, in the event that TP53 DS on Pap smears does not
drastically underperform UtL, the sponsor would further explore this complementary collection
method. Because Pap smears are routinely performed by primary care providers, the ability to
use these could help accelerate market adoption. However, current evidence points to the
probable inferiority of Pap smears.
Methods: The sponsor will carry out TP53 DS as above but using DNA from Pap smears collected
prior to surgery of randomly selected, age matched subset of 25 cancer cases and 25 controls
from Aim 1A.
Statistical Analysis: Data on TP53 MAF from DNA collected by conventional pap smear tests
from a subsample of average risk patients will be considered as additional predictor in
models developed within aim 1A and 1B.The sponsor will investigate if any additional power to
discriminate cases from controls can be gained by combining information from Pap smears and
UtLs.
AIM 2. HIGH RISK POPULATION OVARIAN CANCER SCREENING This aim will take a similar approach to
that of Aim 1 but will focus on women at high risk for ovarian cancer due to hereditary
breast and ovarian cancer (HBOC) mutations. HBOC women will have been identified by a strong
family history of breast or ovarian cancer and found to carry mutations in cancer
susceptibility genes, usually BRCA1 and BRCA2, which confer a lifetime HGSC risk of 35%, 46%
and 13%, 23%, respectively. Standard of care is risk reducing salpingo oophorectomy (RRSO) at
an early age, typically after completion of child bearing. Although this approach does
decrease mortality, it is imperfect: approximately 10% of ovarian cancers in this population
develop before age 40. In the Vienna case series, cancers have been seen at as young an age
as 20, which is well before a prophylactic surgery would normally have been undertaken. Thus,
even with standard of care, HBOC women in their reproductive years are at a significantly
elevated risk of ovarian cancer relative to other women their need for an effective screening
tool is even greater. At present, affected women must make the decision to either accept the
increased risk of a highly lethal cancer or elect to forgot (or prematurely end)
childbearing. This is an excruciatingly challenging decision faced by hundreds of thousands
of women in the US alone. In certain ethnic groups, particularly Ashkenazi Jews, the risk of
being a carrier is as high as 1 in 40. Yet the majority of BRCA carriers, even those who do
not have surgery, will never develop HGSC. There is an urgent need for early detection
diagnostic tools in women at high risk of HGCS in order to save lives and preserve the choice
of fertility. The product of this aim will be a 115 patient biomarker data set that
demonstrates commercially robust performance of such an assay.
Aim 2A. To assess the test performance of TP53 DS on UtLs for HGSC detection in high risk
patients.
The sponsor will approach this study per the general methods of Aim 1A. One significant
difference is that UtL will be collected from HBOC women undergoing RRSO, rather than from
women with known ovarian masses. After RRSO, the ovaries and fallopian tubes undergo a
meticulous sectioning protocol known as SEE-FIM to carefully examine the tissues,
particularly the fimbriae, for STICs and occult cancers. Among this high risk population,
such lesions are found in roughly 5% of resections. The cancer group will encompass women
where STICs or occult tumors were found during SEE-FIM the control group will include women
with no lesions. This study design is innovative in that it focuses, not only on high risk
women, but specifically on the ability of our assay to detect very early stage, potentially
even microscopic, cancers when they remain surgically curable.
Methods: UtL samples from up to 115 women with hereditary high risk ovarian cancer syndromes
will be analyzed via DS: 15 with STICs or occult cancer (cases) and up to 100 cancer free
(controls). Case and control will be matched by age. UtLs will be performed immediately prior
to RRSO. Surgical specimens will be pathologically assessed by a centrally standardized
SEE-FIM protocol. After RRSO, the ovaries and fallopian tubes undergo a meticulous sectioning
protocol known as SEE-FIM to carefully examine the tissues, particularly the fimbriae, for
STICs and occult cancers. The sponsor expects to have identified 5 to 15 women with STIC or
occult cancers in this cohort, 100 age matched women will have no detected lesions. The
smaller number of cancer cases than controls reflects the low percentage of lesion positive
surgeries and the sample number the sponsor project realistically being able to achieve.
Although a larger number would be optimal, such samples are extremely limited. The cancer
group will encompass women where STICs or occult HGSC were found during SEE-FIM the control
group will include women with no lesions Statistical analysis: This will be as per Aim 1A.
Aim 2B. Diagnostic threshold calibration in high risk women by background mutation load.
This sub-aim will use the same protocol and statistical methods of aim 1B and apply them to
the high- r i s k population of Aim 2.
Methods:Leukocyte DNA from all available cancer cases and a randomly chosen 30-woman subset
of the control group will be evaluated. It is well-established that the risk of malignancy in
HBOC women is elevated relative to others by virtue of defects in DNA repair that increase
the probability of oncogenic mutations arising. As such, the sponsor anticipate that the
non-cancer-derived background mutation load measured in UtLs as well as in peripheral blood
may be proportionally elevated as well. Adjusting for the high background mutation load
measured in blood may be particularly important to increase specificity in this group.
Statistical analysis: This will be as per Aim 1B.
AIM III To define a methylation signature for HGSC detection in UtLs. Over the last 15 years,
the value of DNA methylation analysis for the detection of epigenetic, tumour-specific
changes has been demonstrated, particularly in cancer diagnostics. Studies of DNA-methylation
in ovarian cancer (OC) focused on detecting DNA fragments shed by OC cells into the
bloodstream (i.e. cell-free DNA - cfDNA) suggest that DNA methylation patterns in cfDNA have
the potential to detect a proportion of OCs up to two years in advance of diagnosis. This
study also clearly shows the limitation of this approach because only 50% of all patients who
finally developed high-grade serous ovarian cancer (HGSC) within two years could be detected.
The underlying problem is that due to the low concentration of cfDNA in the blood stream a
very weak signal needs to be detected. With the aim of potentially increasing the sensitivity
of HGSC detection the sponsor will in cooperation with Prof. Andreas Weinhäusel, Austrian
Institute of Technology (AIT), Competence Unit Molecular Diagnostics, perform a proof of
concept study in a sub-set of patients included in this project. Prof. Andreas Weinhäusel,
identified 96 methylation markers relevant to HGSC. Only a very small amount of DNA is
required for DNA methylation. It is therefore advisable to use the already available DNA from
the lavages and the corresponding tumor tissue, which has been extracted in the course of
this study, for the further development of the specificity and sensitivity of the test
procedure. The product of AIM3 will be a 140 patient methylation markers data set that
demonstrates high sensitive performance of such an assay.
Methods: Genomic DNA from UtLs and corresponding tumor tissue from 30 HGSC patients and 10
STICs/occult cancers will get enriched for methylated DNA with the aid of methylation
sensitive restriction enzymes. This DNA will be analyzed by performing a highthrough put-qPCR
in which 5 x 96 marker x 96 DNA will be analyzed in parallel. The most informative markers
will be combined to a methylation signature. To proof the specificity of the signature DNA
from UtLs from 60 controls will be analyzed (30 average risk and 30 high risk cases).
Assessment of DNA methylation pattern will be carried out on the pre-resection lavages and on
up to 10 STIC lesions/occult cancers. When local pathology results identify STICs or occult
cancer cells (FFPE tissue), laser microdissection and DNA isolation will be performed.
Subsequently applying standard NGS TP53 sequencing will be performed as described in AIM 1
and AIM 2. In this pilot sub-study, high-through put-qPCR will be applied to remaining DNA
material for assessment of methylation status.
Statistical analysis: Aiming to evaluate the potential for defining a DNA-Methylation
signature for simple PCR testing, dCT-PCR values from 96-plexed high-throughput MSREqPCR
analysis will be analysed by bioinformatics & biostatistics to conduct 1) class comparison
between the relevant clinical subtypes and classes for defining significantly differentially
methylated genes; 2) multivariate class prediction analysis using different feature selection
approaches based on single-marker p-values. Different classification algorithms (e.g.
k-nearest neighbor, support vector machines, linear discrimination analyses etc.) will be
applied, using 10-fold cross validation and/or leave-one-out cross validation for selecting
best candidate methylation markers - and algorithms; in addition, the top-AUC values of
single candidate methylation-marker's will be defined in ROC analysis and considered for
marker selection.
A subset of 48 candidate markers will be selected for confirmation in a separate sample set.
The methylation data derived thereof will be analyzed using class comparison and class
prediction analysis in a similar manner as the first set of data derived from 480plexed
analysis. Best performing single markers and combinations of markers from multivariate
analysis will be defined. Classification results and methylation data will be compared with
p53 mutation test-results to evaluate potential of methylation-based diagnostic
classification, as a sole and in combination with p53 mutation analysis. This will be
conducted applying different class-prediction approaches integrating both p53 and methylation
data in multivariate models.
