Cancer classification is often based on morphological appearance, which can have serious limitations. For example, tumours with similar histological appearance can follow significantly different clinical courses and show different responses to therapy. In some cases, identification of specific molecular abnormalities, such as chromosomal translocations, can provide the critical diagnostic tool to effectively classify specific tumours. A highly sensitive and specific approach towards translocation detection would allow for a more complete molecular profile that would both support the basic science discovery process, and would be of great use to the clinical care of patients.
We developed a technique called ADOT to detect chromosomal translocations, and tested the approach using Ewing sarcoma as our model. ADOT combines custom oligonucleotide microarrays with the S9.6 antibody to identify chromosomal translocations in cancer. Compared to traditional microarray techniques, ADOT utilizes total RNA without poly(A) selection, reverse transcription, RNA (or DNA) amplification, or nucleic acid labelling. The S9.6 antibody recognizes an RNA–DNA hybrid of ~15 bp, enabling detection of translocation transcripts even using poor-quality RNA. This study shows that ADOT can be used to detect translocations from cell lines, frozen tumours, and FFPE tumours. RNA extracted from FFPE samples is usually highly degraded and is thus not ideal for RT-PCR. However, degraded RNA appears sufficient for hybridization to DNA probes and recognition by the S9.6 antibody in ADOT. Antibody S9.6 has no sequence specificity and does not show a significant bias for GC content. However, even one mismatched base pair reduces signal by 80-fold, and a second mismatch ~20,000-fold (Dutrow et al, 2008
). ADOT is also very sensitive: it could detect translocations from as little as 200 ng of total RNA, containing ~3.3 × 10−5
fmol of translocation transcript.
Because the S9.6 antibody binds to RNA–DNA hybrids in a length-dependent manner and the sequence of fusion point is specific, we were limited in our ability to optimize the probes for similar melting temperature and GC content during the design phase. However, the normalization procedure we used negated much of the concern resulting from higher- or lower-than anticipated hybridization characteristics. Another strength of ADOT is that translocations are identified via two independent sources of information: the signal from the fusion probes, as well as those from the wild-type exon and splice probes. These two results serve as cross-references and decrease the likelihood of false identifications. Thus, we were able to accurately identify translocations from all cases except one (which likely had a translocation not included in the current version of the array).
Additional work will be required if ADOT is to move into the clinical realm. First, the design of the array will need to be expanded to include other translocations of interest. For an array focused on Ewing sarcoma, probes designed to detect EWS/ETV1, EWS/ETV4, EWS/FEV, TLS/ERG or TLS/FEV would need to be included. Furthermore, there are additional translocations that have been identified in ‘Ewing's-like tumours’ that should be included (Sankar & Lessnick, 2011
). Indeed, one could envision an array design that includes all known translocations in cancer.
Second, an important consideration for Clinical Laboratory Improvement Amendments (CLIA) certification of this approach will be to develop test samples that can be used to assess the performance of the system in the molecular pathology laboratory. Most of the alternate Ewing sarcoma translocations are rare, and so it is unlikely that large stocks of tumour-derived material could be available for quality assurance and quality control purposes. One approach might be to develop a series of cell lines expressing the alternate translocations and isoforms to use for such purposes.
One consideration for implementation into the clinical realm is that of cost. Costs associated with microarray-based approaches are constantly changing (in general, becoming less expensive). Using the approach we described in this report, the absolute cost of ADOT is similar to the cost of using FISH for an EWSR1 break-apart probe. However, ADOT is more cost-effective because it can define translocation products at the exon level and can detect a greater number of translocations for the same cost. Related to this, it is possible that next-generation sequencing might become applicable to translocation analysis in the future. At the moment, however, such sequencing techniques require higher-quality RNA than is typically available in FFPE tumour specimens, these techniques are more expensive, slower and not as readily available to the clinical pathology laboratory as microarray-based approaches.
In summary, we developed a novel technique, ADOT, for the detection and analysis of chromosomal translocations. ADOT is highly sensitive and provides detailed exonic information about the translocation. Furthermore, ADOT is capable of detecting known or unknown translocations in biological samples, including those most commonly encountered during the diagnostic work-up of a patient. ADOT bears promise as a discovery tool for identifying fusion transcripts in cancers, as well as a diagnostic tool for patients with translocation-associated tumours. With additional design, ADOT could develop into an important component of the diagnostic work-up.