Interfocal heterogeneity challenges the clinical usefulness of molecular classification of primary prostate cancer.
Kristina Totland CarmAndreas M HoffAnne Cathrine BakkenUlrika AxcronaKarol AxcronaRagnhild A LotheRolf I SkotheimMarthe LøvfPublished in: Scientific reports (2019)
Prostate cancer is a highly heterogeneous disease and typically multiple distinct cancer foci are present at primary diagnosis. Molecular classification of prostate cancer can potentially aid the precision of diagnosis and treatment. A promising genomic classifier was published by The Cancer Genome Atlas (TCGA), successfully classifying 74% of primary prostate cancers into seven groups based on one cancer sample per patient. Here, we explore the clinical usefulness of this classification by testing the classifier's performance in a multifocal context. We analyzed 106 cancer samples from 85 distinct cancer foci within 39 patients. By somatic mutation data from whole-exome sequencing and targeted qualitative and quantitative gene expression assays, 31% of the patients were uniquely classified into one of the seven TCGA classes. Further, different samples from the same focus had conflicting classification in 12% of the foci. In conclusion, the level of both intra- and interfocal heterogeneity is extensive and must be taken into consideration in the development of clinically useful molecular classification of primary prostate cancer.
Keyphrases
- prostate cancer
- papillary thyroid
- machine learning
- gene expression
- deep learning
- radical prostatectomy
- squamous cell
- end stage renal disease
- ejection fraction
- newly diagnosed
- single cell
- dna methylation
- peritoneal dialysis
- randomized controlled trial
- childhood cancer
- lymph node metastasis
- copy number
- artificial intelligence
- patient reported outcomes
- high resolution
- drug delivery
- case report
- mass spectrometry
- big data