Cancer origin tracing and timing in two high-risk prostate cancers using multisample whole genome analysis: prospects for personalized medicine

BACKGROUND: Prostate cancer (PrCa) genomic heterogeneity causes resistance to therapies such as androgen deprivation. Such heterogeneity can be deciphered in the context of evolutionary principles, but current clinical trials do not include evolution as an essential feature. Whether or not analysis of genomic data in an evolutionary context in primary prostate cancer can provide unique added value in the research and clinical domains remains an open question. METHODS: We used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high-risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones. RESULTS: In patient GP5, CDK12 inactivation was among the first mutations, leading to a PrCa tandem duplicator phenotype and initiating the cancer around age 50, followed by rapid cancer evolution after age 57, and metastasis around age 59, 5 years prior to prostatectomy. In patient GP12, accelerated cancer progression was detected after age 54, and metastasis occurred around age 56, 3 years prior to prostatectomy. Multiple metastasis-originating events were identified in each patient and tracked anatomically. Metastasis from prostate to lymph nodes occurred strictly ipsilaterally in all 12 detected events. In this pilot, metastatic subclone content analysis appears to substantially enhance the identification of key drivers. Evolutionary analysis' potential impact on therapy selection appears positive in these pilot cases. CONCLUSIONS: PrCa evolutionary analysis allows tracking of anatomic site of origin, timing of cancer origin and spread, and distinction of metastatic-capable from non-metastatic subclones. This enables better identification of actionable targets for therapy. If extended to larger cohorts, it appears likely that similar analyses could add substantial biological insight and clinically relevant value

Transcription factor motif quality assessment requires systematic comparative analysis [version 2; referees: 2 approved]

Transcription factor (TF) binding site prediction remains a challenge in gene regulatory research due to degeneracy and potential variability in binding sites in the genome. Dozens of algorithms designed to learn binding models (motifs) have generated many motifs available in research papers with a subset making it to databases like JASPAR, UniPROBE and Transfac. The presence of many versions of motifs from the various databases for a single TF and the lack of a standardized assessment technique makes it difficult for biologists to make an appropriate choice of binding model and for algorithm developers to benchmark, test and improve on their models. In this study, we review and evaluate the approaches in use, highlight differences and demonstrate the difficulty of defining a standardized motif assessment approach. We review scoring functions, motif length, test data and the type of performance metrics used in prior studies as some of the factors that influence the outcome of a motif assessment. We show that the scoring functions and statistics used in motif assessment influence ranking of motifs in a TF-specific manner. We also show that TF binding specificity can vary by source of genomic binding data. We also demonstrate that information content of a motif is not in isolation a measure of motif quality but is influenced by TF binding behaviour. We conclude that there is a need for an easy-to-use tool that presents all available evidence for a comparative analysis

Genome profiling is an efficient tool to avoid the STUMP classification of uterine smooth muscle lesions: a comprehensive array-genomic hybridization analysis of 77 tumors.

The diagnosis of a uterine smooth muscle lesion is, in the majority of cases, straightforward. However, in a small number of cases, the morphological criteria used in such lesions cannot differentiate with certainty a benign from a malignant lesion and a diagnosis of smooth muscle tumor with uncertain malignant potential (STUMP) is made. Uterine leiomyosarcomas are often easy to diagnose but it is difficult or even impossible to identify a prognostic factor at the moment of the diagnosis with the exception of the stage. We hypothesize, for uterine smooth muscle lesions, that there is a gradient of genomic complexity that correlates to outcome. We first tested this hypothesis on STUMP lesions in a previous study and demonstrated that this 'gray category' could be split according to genomic index into two groups. A benign group, with a low to moderate alteration rate without recurrence and a malignant group, with a highly rearranged profile akin to uterine leiomyosarcomas. Here, we analyzed a large series of 77 uterine smooth muscle lesions (from 76 patients) morphologically classified as 19 leiomyomas, 14 STUMP and 44 leiomyosarcomas with clinicopathological and genomic correlations. We confirmed that genomic index with a cut-off=10 is a predictor of recurrence (P<0.0001) and with a cut-off=35 is a marker for poor overall survival (P=0.035). For the tumors confined to the uterus, stage as a prognostic factor was not useful in survival prediction. At stage I, among the tumors reclassified as molecular leiomyosarcomas (ie, genomic index ≥10), the poor prognostic markers were: 5p gain (overall survival P=0.0008), genomic index at cut-off=35 (overall survival P=0.0193), 13p loss including RB1 (overall survival P=0.0096) and 17p gain including MYOCD gain (overall survival P=0.0425). Based on these findings (and the feasibility of genomic profiling by array-comparative genomic hybridization), genomic index, 5p and 17p gains prognostic value could be evaluated in future prospective chemotherapy trials

Additional file 1 of A genetic model for central chondrosarcoma evolution correlates with patient outcome

Additional file 1: Supplementary Tables 1-6