Early indicators of pancreatic cancer identified in precision medicine study.
Pancreatic cancer is a devastating disease, with a death rate close to the incidence rate. Because more than 90% of pancreatic cancer cases are diagnosed at the metastatic stage, when there are only limited therapeutic options, earlier diagnosis is anticipated to have a major impact on extending life expectancy for patients. However, there has been a lack of reliable markers, early indicators and risk factors associated with pancreatic cancer identified. Now, a study from researchers led by Beth Israel Deaconess Medical Center (BIDMC) identifies and validates an accurate 5-gene classifier for discriminating early pancreatic cancer from non-malignant tissue. The team state that their findings are a promising advance in the fight against this typically fatal disease and this new way of differentiating between healthy and malignant tissue offers hope for earlier diagnosis and treatment. The opensource study published in the journal Oncotarget.
Previous studies show a number of studies aimed at identifying differentially expressed genes in pancreatic cancer, however, no transcriptome data has yet translated into a clinically useful biomarker. Also, lack of standardisation of published gene signatures of individual microarray studies due to variability in analytical strategies makes comparative analysis difficult. One alternative to overcome the limitations of analyzing individual multiple datasets that have been processed by different approaches is meta-analysis of multiple transcriptional profiling studies applying identical analytics that can generate gene signatures with increased reproducibility and sensitivity. The current study develops a 5-gene pancreatic cancer predictor that can discriminate between cancerous and healthy tissue.
The current study applied the 5-gene predictor to datasets involving cancerous and benign lesions of the pancreas, including pancreatitis and early stage cancer. Results show that the predictor accurately differentiated pancreatic cancer, benign pancreatic lesions, early stage pancreatic cancer and healthy tissue. Data findings show that the predictor achieved on average 95% sensitivity and 89% specificity in discriminating pancreatic cancer from non-tumour samples in four training sets; it also achieved 94% sensitivity and 90% specificity in five independent validation datasets.
The lab explain that because these five genes are ‘turned on’ so early in the development of pancreatic cancer, they may play roles as drivers of this disease and may be exciting targets for therapies. They go on to note that most of the five genes, namely TMPRSS4, AHNAK2, POSTN, ECT2 and SERPINB5, have been linked to migration, invasion, adhesion, and metastasis of pancreatic or other cancers.
The group state that the first diagnostic application of the panel may be for analyses of fine needle biopsies routinely used for diagnosing pancreatic cancer and for determining the malignant potential of mostly benign pancreatic cysts that can sometimes be precursors of pancreatic cancer. They go on to add that in addition to providing a new tool for diagnoses, the research may also lead to new insights into how pancreatic cancer arises.
The team surmise that the identification and initial validation of a highly accurate 5-gene pancreatic cancer biomarker panel that can discriminate late and early stages of pancreatic cancer from normal pancreas and benign pancreatic lesions could facilitate early diagnosis of pancreatic cancer. They go on to add that their findings may open a window of opportunity for earlier diagnosis and, consequently, earlier intervention and more effective treatment of this deadly cancer, leading to higher survival rates. For the future, the researchers state that they plan to expand their biomarker by including non-coding RNAs, proteins, metabolites and mutations associated with pancreatic cancer. They conclude that this should result in development of the first of its kind biomarker that gauges pancreatic cancer alterations from multiple genomic angles for making highly accurate diagnoses.