01 / Oncogene · 2014
New RNA discoveries from one patient pair.
Discovery from one patient: one tumour and one matched normal sample.
AN ASSUMPTION PUT TO THE TEST
The assumption I challenged
One patient pair seemed too small for discovery. Gene-expression arrays defined the established view of RNA, while early Solexa sequencing was too expensive to apply to a large patient cohort and lacked a mature analytical workflow.
What we showed
Sequencing beyond predefined probes revealed DUNQU1, an uncharacterized transcript with protein-coding potential that was preferentially expressed in HCC (liver-cancer) tumours compared with adjacent normal liver tissue. Different proportions of FGFR2 RNA versions were linked to tumour size and whether the cancer returned, as well as hepatitis infection and liver scarring.
My contribution
I worked out the early Solexa analysis and developed the qPCR approach to measure the relative proportions of FGFR2 RNA variants.
The evidence we established
The team validated preferential DUNQU1 expression in liver tumours across 55 tumour–normal pairs. Functional experiments showed that increasing DUNQU1 expression enhanced colony formation in liver-cancer cells, adding biological evidence to the computational discovery.
Why it matters
The study uncovered new liver-cancer markers with potential to improve prediction of disease outcomes and guide the search for new treatment targets. It turned previously unmeasured RNA signals into concrete candidates for biomarker and therapeutic research.
Discovery can begin with one pair. Validation must go further.
Unprecedented transcriptome resolution.
Look for sequences already on the array
A microarray detects RNA through predefined probes. What is outside those measurements can remain unseen.
A useful measurement of known targets can still leave important biology outside the view.
Explore the evidence & discovery process
- 01A measurement beyond the array
- Early Solexa generated approximately 250 million paired-end reads from the tumour and its adjacent normal tissue. The search included RNA sequences absent from existing microarray probe coverage.
- 02Turning sequencing candidates into cohort measurements
- The qPCR analysis used ΔΔCt measurements to derive splicing inclusion ratios, so validation did not depend on sequencing every patient. The published method includes a corrected equation in the 2016 erratum.
- 03Connecting the signal to disease
- Nested RT-PCR across 55 tumour–normal pairs detected DUNQU1 in most HCC tumours, with no detectable signal in most adjacent normal liver samples. Four male HBV-positive pairs showed signal in both tissues (Figure 2c), so the pattern was preferential rather than universally tumour-exclusive. The FGFR2 splicing pattern was associated with clinical features, including tumour size and recurrence.
- 04Testing cancer-cell behaviour
- Collaborators increased DUNQU1 expression in Huh7 liver-cancer cells and observed enhanced colony formation in soft agar, providing functional evidence of altered cancer-cell behaviour (Figure 3). This cell-culture experiment did not establish a treatment effect in patients.
- 05The next question
- Which of these RNA signals adds useful information in independent patients, and which reflects biology that can be therapeutically investigated?
Technical context & publication
HCC means hepatocellular carcinoma. RNA sequencing generated approximately 250 million paired-end reads from one tumour–adjacent-normal pair; empirical validation used 55 pairs. The qPCR method derives splicing inclusion measurements from ΔΔCt values; the corrected equation is provided in the 2016 erratum (DOI 10.1038/onc.2016.62). Protein-coding potential is not by itself proof of a functional protein or a validated therapeutic target. The account of skepticism reflects my experience, rather than a claim of universal agreement across the field.
Read the Oncogene paper ↗