HiPerDART Targets and Objectives

agerasiaetherealΤεχνίτη Νοημοσύνη και Ρομποτική

24 Νοε 2013 (πριν από 3 χρόνια και 4 μήνες)

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HiPerDART Targets and Objectives


In applications related to clinical diagnostics, DNA microarrays have enormous potential for multiplex
analysis, yet issues related to technical challenges (low reproducibility, poor signal
-
to
-
noise, and lengthy
workflows) have limited their practicality a
nd slowed regulatory approval. We believe that the SuNS
technology offers significant advantages in microarray manufacturing, such that these challenges may be
overcome.


By making a large up
-
front investment in the template and then replicating it many

times, we can effectively
dilute our production costs while maintaining high performance features in the array. From a manufacturing
standpoint, we will address three fundamental features of microarrays, which have so far limited their utility.


Probe De
nsity
: Signal
-
to
-
noise and assay sensitivity, key features of microarray utility, are both determined
by probe density within a feature. The higher the target
-
capture capacity of the feature per unit area is, the
better the performance of the microarray. T
here is a fundamental limit, however, to the oligo density which
can be achieved on a surface, due to the radii of gyration of the DNA strands. The approach here proposed is
to maximize probe density by concatamerizing oligos in the “z
-
direction” with resp
ect to the array surface.
The HiPerDART consortium envisions accomplishing this by utilizing rolling
-
circle amplification (RCA)
technology. Extension from a common primer sequence will take place within the micro
-
wells, each with a
different circularized D
NA molecule. The process is illustrated below:





F
IGURE
5:

E
XTENSION FROM A COMM
ON PRIMER SEQUENCE


Utilizing the common primer sequence, the resulting long
-
mer strands are themselves enzymatically
replicated prior to being printed onto a replica surfac
e. The HiPerDART consortium believes that by
increasing probe density using RCA technology, we can dramatically increase signal
-
to
-
noise and assay
sensitivity to an unprecedented degree.


HydroSuNS
: A compelling feature of the SuNS technology is the flexib
ility of substrate material onto which
DNA molecules may be printed. A good substrate shall have properties such that it simultaneously (a)
provides ideal conditions for SuNS printing, and (b) optimizes microarray assay performance. Scientists in
our conso
rtium recently have discovered that indeed such a surface exists, and that it is the surface of a
hydrogel polymer.


Hydrogel as ideal substrate for SuNS printing:

There are essentially two technical challenges to consider
with any SuNS approach. The fi
rst challenge is to achieve nanometric conformal contact between two
surfaces over a macroscopic area
, while the second

is to minimize damage to the template DNA which may
result from repeated cycles of surface
-
to
-
surface contact.
A
lthough literature
exists for related systems
4, 5
,
Molecular Stamping is the first to overcome these two challenges of SuNS by printing

onto the surface of a
hydrogel
.




F
IGURE
6:

TEMPLATE AND REPLICA

ARRAY



Array Hybridization

Conventional microscope slides (75 x 25 mm²)

are most commonly used as microarray substrates. The
following methods for hybridizing microarrays are commonly used today:


1. The coverslip method is the standard experimental procedure. A thin glass or plastic coverslip is placed on
top of the array sl
ide and the sample solution is drawn in the gap between cover slip and slide by capillary
action. Sample volumes are limited to a few microliters per slide.


2. Gasket
-
type hybridization chambers are formed by a cover and a gasket which is usually attached

to the
slide with an adhesive. Hybridization in these gasket
-
type chambers may take place with or without
agitation. Typically those hybridization chambers hold 50
-
500 µL of sample.


3. If volumes of more than a few mL are used, the entire slide is placed

into containers. The containers may
be agitated or left static during the hybridization reaction.


Within the HiPerDART project, a microarray cartridge / microfluidics hybridization device will be
developed which will enable very low hybridization volume
s, while simultaneously reducing the time
required for hybridization and improving the reproducibility of assay results. Our preliminary cartridge
concept is depicted below.




F
IGURE
7:

MICROARRAY CARTRIDGE


In close collaboration with the SME partner
thinXXS, the HiPerDART consortium will utilize the latest
microfluidic device prototyping technology to develop the microinjection moulding required for this project.
One key element of the design will be to incorporate one
-
way channels so that each array
is guaranteed to
remain isolated from the others during the assay implementation.


Molecular profiling in colorectal cancer

Colorectal cancer is the third most common cancer worldwide after lung

and breast. Cumulative risk in European countries is near 6%,

both in

men and women. Five year survival estimates range from 90% in Stage I to < 5% in stage IV, and is less
accurate (45
-
80%) in stages II & III (Ries et al, 2005; Howlander et al, 2006). Adjuvant chemotherapy is
standard for stage III (Coleman et al,
2001; van Cutsem et al, 2005) but not for stage II (Benson et al, 2004;
Sobrero and Koehne, 2006; Andre et al, 2006), where the challenge is to identify the 25% of patients not
cured by surgery alone. Clinical and pathological risk factors (T4, G3, number
of assessed lymph nodes,
perforation or vascular invasion) have been identified but lack standardisation (Ries et al, 2005; Coleman et
al, 2001). We need better prognostic biomarkers that would allow selecting high risk stage II patients for
adjuvant thera
py and possibly to spare it in the patients with low risk stage III. Despite the large amount of
literature on molecular biomarker candidates, none is routinely used in the clinic due to lack of proper
standardisation and validation (Anwar et al, 2004).


C
umulative genetic alterations can lead to abnormal transcription of a large number of genes, which can be
measured by mRNA expression levels. The MAQC1 and MAQC2 initiatives (coordinated by FDA) and
EMERALD
-
QC (EU project) aim at deriving consensus protoco
ls to warrant predictive and stable genomic
signatures from gene expression and other genomic levels. Inherent to this approach is the hypothesis that
every tumor contains informative gene expression signatures that, at the time of diagnosis, can direct th
e
biologic behaviour over time. Recently, several microarray gene expression profiles have shown promise in
predicting the prognosis of stage II and III colon cancer (Eschrich et al, 2005; Barrier et al, 2006; Lin et al,
2007) but need to be validated befo
re being routinely used, which is jeopardized by the current cost of
assessing the large number of genes in these profiles.


In this
project

we aim to develop a prognostic predictor that has clinical usefulness in the management of
colorectal cancer patients. We will use already existing data on genomic expression analyzed with
Affymetrix microarrays in 150 tumors from the MECC study (Israel)
and 26 tumors from the Bellvitge study
(Barcelona). The patients from these studies are well characterized clinically and have been followed up for
more that 5 years. The information of genomic expression in tumors will be complemented with two
additional
sources of data: genomic expression and germline polymorphisms in histologically normal colonic
mucosa resected from patients with colorectal cancer and copy number variation. These data will be
measured in a limited sample, but large enough to ensure powe
r to detect the most relevant signals. Genomic
expression of normal mucosa and inherited genomic variation has been shown to have relevant prognostic
value prior to the development of the tumor. In this study we aim to use a whole
-
genome approach, together

with appropriate data mining techniques, to select the most informative prognostic markers.



Validation of classifiers and signatures


Reliable and robust predictive models are essential to realize the promises of personalized medicine. Valid
biomarkers
derived from high
-
throughput technologies need to undergo a quality control process that can
address the control of variability along all steps of the processing pipeline. A first consensus on intra
-

and
inter
-
lab reproducibility and comparability between
alternative platforms was reached by the FDA
-
led
initiative Microarray Quality Control (MAQC). Through a consortium of 60 organizations (including 32 data
analysis teams), the FDA

is now leading an international effort (MAQC
-
II) for defining best practice
s on the validation of classifiers
and signatures based on gene expression data from microarrays (edkb.fda.gov/MAQC). Beside the need of
controlling selection bias (Ambroise et 2002) through carefully designed Data Analysis Protocols (DAP), a
specific conc
ern regards the stability of derived classifiers and signatures. The lists of biomarkers can be
drastically different for changes along the process (generation, signal processing, normalization, etc), with
slight perturbation of the training dataset produc
ing different markers (Davis et al 2006, Simon 2006).


In the HiPerDART project, we will evaluate and control variability of the new technology by adopting
BioDCV, a state
-
of
-
the
-
art microarray profiling platform that implements a complete validation DAP
(Furlanello et al 2003, 2005) and a rich family of machine learning algorithms for feature ranking. The
platform can develop predictive signatures on genome
-
wide chips (Riccadonna et al. 2007), and it has been
recently extended with a set of algebraic indi
cators of stability for ranked gene lists (Jurman et al 2008)


currently tested in the MAQC
-
II initiative for potential inclusion as a guideline. We will compare signatures
produced

with the HiPerDART or alternative platforms, and within the same platfor
m, for different
technical implementation of the upstream data production and preprocessing phases. Quality control and best
practices defined from the EMERALD consortium will also be considered and implemented.