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Keywords | Urology | clinical analysis | anticancer therapy | prognonsis | microarray |<br />
Drop-Top<br />
Integration of DNA, RNA and protein<br />
markers in a tool for the prognosis<br />
and diagnosis of human disease<br />
Summary<br />
The management of patients with superfi cial bladder cancer<br />
is diffi cult. No reliable means exists to determine whether<br />
a tumour will progress towards an infi ltrative form, which<br />
requires radical surgery (cystectomy), or whether it will<br />
remain superfi cial, which requires only conservative surgery<br />
(resection). In addition, no dependable marker exists to predict<br />
whether a primary tumour will reappear or not during<br />
the years following surgical resection, forcing patients to<br />
undergo constant revisions that reduce their quality of life<br />
and overburden health care systems.<br />
Numerous markers of various types (genes, transcripts and<br />
proteins) have been analyzed in bladder cancer studies.<br />
Some of them have been found to harbor potential for<br />
the prognosis (progression and recurrence) of superfi cial<br />
tumours. However, analyses have often been limited to a single<br />
type of marker or even to a single marker. To the best of<br />
our knowledge, no study has attempted to integrate diff erent<br />
types of markers for an increased predictive power.<br />
The main scientifi c goal of Drop-Top is to identify a set of<br />
markers with high predictive power for tumour progression<br />
and recurrence. To this end, we propose to collect tumour<br />
and urine samples from bladder cancer patients with<br />
a detailed clinical record, to measure in them markers of<br />
diff erent types, to fi nd statistically signifi cant correlations<br />
between measurements and clinical records, and to select<br />
a predictor set.<br />
In addition, Drop-Top pursues an ambitious technological<br />
challenge: the development of a prognosis microarray for<br />
the detection of said predictor set. In order to achieve this<br />
we propose to measure all three types of marker biomolecules<br />
by means of a single type of probe: oligonucleotides.<br />
Specifi cally, we propose to use short, long and aptamer oligonucleotides<br />
for the detection of gene, transcript and<br />
protein markers respectively.<br />
Problem<br />
<strong>Cancer</strong> is the second cause of death in the Western world<br />
and its incidence is increasing due to the overall aging of the<br />
population. Although advances in our understanding of the<br />
mechanisms of tumour onset and progression have been<br />
EARLY DETECTION, DIAGNOSIS AND PROGNOSIS<br />
enormous, major impact on survival has been restricted to<br />
haematopoietic malignancies, some pediatric tumours, and<br />
very few solid tumours. Improvement in survival can be<br />
attributed not only to advances in standard chemo- and<br />
radio-therapy and to the recent implementation of targeteddrug<br />
therapy, but also to advances in diagnosis and the<br />
identifi cation of high-risk groups, which allow for earlier and<br />
better treatment selection.<br />
In contrast, the overall prognosis of the most common cancers,<br />
such as lung, colon, prostate, breast and bladder<br />
cancers remains poor, specially when the tumour cannot be<br />
cured by surgery. One limitation is that the pathologist’s<br />
interpretation of the tumour’s histological features remains<br />
the ‘gold standard’. Recent advances in microarray technology<br />
together with information derived from the sequencing<br />
of the human genome have raised hopes that this situation<br />
will change dramatically in the coming years. For these<br />
hopes to be realized, it is essential to make appropriate use<br />
of information, technology and clinical resources. We believe<br />
that resources are often wasted because of inappropriate<br />
approaches and inadequate collaboration between clinical,<br />
academic and industrial partners.<br />
Transcriptome analysis by DNA microarrays has been successfully<br />
used for the identifi cation of biomarkers of tumour<br />
progression. However, and due to the use of diff erent microarray<br />
platforms and patient selection strategies, among<br />
others, the biomarkers identifi ed for a given clinical condition<br />
vary from study to study. Therefore, their application to<br />
common clinical practice has not yet taken place, and<br />
requires prospective studies. The Drop-Top proposal intends<br />
to overcome the above limitations by a double approach:<br />
• prospective validation of the information acquired<br />
through retrospective studies. For this, a collaborative<br />
multicentre eff ort is essential;<br />
• integration of biomarkers from genome, transcriptome<br />
and proteome analysis in a single predictor set. Even<br />
though each of these three analyses by itself will likely<br />
contribute, it is expected that the combination of biomarker<br />
types will result in an enhanced predictive power.<br />
This type of strategy has scarcely been used due to:<br />
• the high cost of microarray technology;<br />
• the need to have access to diff erent platforms for<br />
the detection of diff erent biomolecules;<br />
• the fragmentary nature of most of the published<br />
work (multiple DNA, mRNA and protein markers<br />
studied, but only individually and in most cases<br />
weakly associated to disease phenotype);<br />
• the lack of bioinformatics and biostatistics tools to<br />
handle heterogeneous data;<br />
• the limited amount of clinical and follow-up information<br />
usually available. In addition, most of these studies<br />
are performed without taking into consideration potential<br />
bias in the patient population under study (i.e. large<br />
tumour cases are more likely to be studied than small<br />
tumour cases for sample availability reasons).<br />
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