Davide Prandi

Tumor clone dynamics in lethal prostate cancer

Independent clones with distinct genomic patterns show complex dynamics over the lethal course of prostate cancer, with gradual emergence of drug-resistant clones. Treacherous Evolution of Prostate Cancer As cancers grow and evolve, they develop a variety of mutations, some of which enable resistance to anticancer therapeutics. Now, Carreira et al. have shown that lethal prostate cancer contains a mixture of independent clones with different genetic makeup and different ability to survive drug treatment, which evolves over time. As the cancer progresses and is exposed to different drugs, the resulting selection pressure results in the emergence of clones that are activated by some of the drugs, indicating the importance of careful monitoring and timely changes in therapeutic regimens to avoid giving the cancer cells an unwanted boost. It is unclear whether a single clone metastasizes and remains dominant over the course of lethal prostate cancer. We describe the clonal architectural heterogeneity at different stages of disease progression by sequencing serial plasma and tumor samples from 16 ERG-positive patients. By characterizing the clonality of commonly occurring deletions at 21q22, 8p21, and 10q23, we identified multiple independent clones in metastatic disease that are differentially represented in tissue and circulation. To exemplify the clinical utility of our studies, we then showed a temporal association between clinical progression and emergence of androgen receptor (AR) mutations activated by glucocorticoids in about 20% of patients progressing on abiraterone and prednisolone or dexamethasone. Resistant clones showed a complex dynamic with temporal and spatial heterogeneity, suggesting distinct mechanisms of resistance at different sites that emerged and regressed depending on treatment selection pressure. This introduces a management paradigm requiring sequential monitoring of advanced prostate cancer patients with plasma and tumor biopsies to ensure early discontinuation of agents when they become potential disease drivers.

SOX2 promotes lineage plasticity and antiandrogen resistance in TP53- and RB1-deficient prostate cancer

Evading cancer drugs by identity fraud Prostate cancer growth is fueled by male hormones called androgens. Drugs targeting the androgen receptor (AR) are initially efficacious, but most tumors eventually become resistant (see the Perspective by Kelly and Balk). Mu et al. found that prostate cancer cells escaped the effects of androgen deprivation therapy through a change in lineage identity. Functional loss of the tumor suppressors TP53 and RB1 promoted a shift from AR-dependent luminal epithelial cells to AR-independent basal-like cells. In related work, Ku et al. found that prostate cancer metastasis, lineage switching, and drug resistance were driven by the combined loss of the same tumor suppressors and were accompanied by increased expression of the epigenetic regulator Ezh2. Ezh2 inhibitors reversed the lineage switch and restored sensitivity to androgen deprivation therapy in experimental models. Science, this issue p. 84, p. 78; see also p. 29 Prostate cancer cells escape androgen deprivation therapy by morphing into a cell type that does not require androgens. Some cancers evade targeted therapies through a mechanism known as lineage plasticity, whereby tumor cells acquire phenotypic characteristics of a cell lineage whose survival no longer depends on the drug target. We use in vitro and in vivo human prostate cancer models to show that these tumors can develop resistance to the antiandrogen drug enzalutamide by a phenotypic shift from androgen receptor (AR)–dependent luminal epithelial cells to AR-independent basal-like cells. This lineage plasticity is enabled by the loss of TP53 and RB1 function, is mediated by increased expression of the reprogramming transcription factor SOX2, and can be reversed by restoring TP53 and RB1 function or by inhibiting SOX2 expression. Thus, mutations in tumor suppressor genes can create a state of increased cellular plasticity that, when challenged with antiandrogen therapy, promotes resistance through lineage switching.

Whole-Exome Sequencing of Metastatic Cancer and Biomarkers of Treatment Response

IMPORTANCE Understanding molecular mechanisms of response and resistance to anticancer therapies requires prospective patient follow-up and clinical and functional validation of both common and low-frequency mutations. We describe a whole-exome sequencing (WES) precision medicine trial focused on patients with advanced cancer. OBJECTIVE To understand how WES data affect therapeutic decision making in patients with advanced cancer and to identify novel biomarkers of response. DESIGN, SETTING, AND PATIENTS Patients with metastatic and treatment-resistant cancer were prospectively enrolled at a single academic center for paired metastatic tumor and normal tissue WES during a 19-month period (February 2013 through September 2014). A comprehensive computational pipeline was used to detect point mutations, indels, and copy number alterations. Mutations were categorized as category 1, 2, or 3 on the basis of actionability; clinical reports were generated and discussed in precision tumor board. Patients were observed for 7 to 25 months for correlation of molecular information with clinical response. MAIN OUTCOMES AND MEASURES Feasibility, use of WES for decision making, and identification of novel biomarkers. RESULTS A total of 154 tumor-normal pairs from 97 patients with a range of metastatic cancers were sequenced, with a mean coverage of 95X and 16 somatic alterations detected per patient. In total, 16 mutations were category 1 (targeted therapy available), 98 were category 2 (biologically relevant), and 1474 were category 3 (unknown significance). Overall, WES provided informative results in 91 cases (94%), including alterations for which there is an approved drug, there are therapies in clinical or preclinical development, or they are considered drivers and potentially actionable (category 1-2); however, treatment was guided in only 5 patients (5%) on the basis of these recommendations because of access to clinical trials and/or off-label use of drugs. Among unexpected findings, a patient with prostate cancer with exceptional response to treatment was identified who harbored a somatic hemizygous deletion of the DNA repair gene FANCA and putative partial loss of function of the second allele through germline missense variant. Follow-up experiments established that loss of FANCA function was associated with platinum hypersensitivity both in vitro and in patient-derived xenografts, thus providing biologic rationale and functional evidence for his extreme clinical response. CONCLUSIONS AND RELEVANCE The majority of advanced, treatment-resistant tumors across tumor types harbor biologically informative alterations. The establishment of a clinical trial for WES of metastatic tumors with prospective follow-up of patients can help identify candidate predictive biomarkers of response.

GPU computing for systems biology

The development of detailed, coherent, models of complex biological systems is recognized as a key requirement for integrating the increasing amount of experimental data. In addition, in-silico simulation of bio-chemical models provides an easy way to test different experimental conditions, helping in the discovery of the dynamics that regulate biological systems. However, the computational power required by these simulations often exceeds that available on common desktop computers and thus expensive high performance computing solutions are required. An emerging alternative is represented by general-purpose scientific computing on graphics processing units (GPGPU), which offers the power of a small computer cluster at a cost of approximately

Beta-binders for Biological Quantitative Experiments

400. Computing with a GPU requires the development of specific algorithms, since the programming paradigm substantially differs from traditional CPU-based computing. In this paper, we review some recent efforts in exploiting the processing power of GPUs for the simulation of biological systems.

Clonal evolution of chemotherapy-resistant urothelial carcinoma

Abstract The similarities between biological systems and distributed and mobile systems suggest that the theory of process calculi could be a useful starting point for understanding, if not predicting, the behaviour of complex biological systems. To formally model in vitro or in vivo experiments, appropriate quantitative extensions of process calculi have to be investigated. This paper focuses on Beta-binders, a language of processes with typed interaction sites which has been recently introduced to accurately represent biological entities. Here the syntax and semantics of Beta-binders are enriched to achieve a stochastic version of it, in order to obtain quantitative measures on biological phenomena. The quantitative parameters are derived from typed interaction sites introducing the concept of affinity . The relevance of quantitative reasoning is outlined running a biological example.

Unraveling the clonal hierarchy of somatic genomic aberrations

Chemotherapy-resistant urothelial carcinoma has no uniformly curative therapy. Understanding how selective pressure from chemotherapy directs the evolution of urothelial carcinoma and shapes its clonal architecture is a central biological question with clinical implications. To address this question, we performed whole-exome sequencing and clonality analysis of 72 urothelial carcinoma samples, including 16 matched sets of primary and advanced tumors prospectively collected before and after chemotherapy. Our analysis provided several insights: (i) chemotherapy-treated urothelial carcinoma is characterized by intra-patient mutational heterogeneity, and the majority of mutations are not shared; (ii) both branching evolution and metastatic spread are very early events in the natural history of urothelial carcinoma; (iii) chemotherapy-treated urothelial carcinoma is enriched with clonal mutations involving L1 cell adhesion molecule (L1CAM) and integrin signaling pathways; and (iv) APOBEC-induced mutagenesis is clonally enriched in chemotherapy-treated urothelial carcinoma and continues to shape the evolution of urothelial carcinoma throughout its lifetime.

SPOP mutation leads to genomic instability in prostate cancer

Defining the chronology of molecular alterations may identify milestones in carcinogenesis. To unravel the temporal evolution of aberrations from clinical tumors, we developed CLONET, which upon estimation of tumor admixture and ploidy infers the clonal hierarchy of genomic aberrations. Comparative analysis across 100 sequenced genomes from prostate, melanoma, and lung cancers established diverse evolutionary hierarchies, demonstrating the early disruption of tumor-specific pathways. The analyses highlight the diversity of clonal evolution within and across tumor types that might be informative for risk stratification and patient selection for targeted therapies. CLONET addresses heterogeneous clinical samples seen in the setting of precision medicine.

Stochastic COWS

Genomic instability is a fundamental feature of human cancer often resulting from impaired genome maintenance. In prostate cancer, structural genomic rearrangements are a common mechanism driving tumorigenesis. However, somatic alterations predisposing to chromosomal rearrangements in prostate cancer remain largely undefined. Here, we show that SPOP, the most commonly mutated gene in primary prostate cancer modulates DNA double strand break (DSB) repair, and that SPOP mutation is associated with genomic instability. In vivo, SPOP mutation results in a transcriptional response consistent with BRCA1 inactivation resulting in impaired homology-directed repair (HDR) of DSB. Furthermore, we found that SPOP mutation sensitizes to DNA damaging therapeutic agents such as PARP inhibitors. These results implicate SPOP as a novel participant in DSB repair, suggest that SPOP mutation drives prostate tumorigenesis in part through genomic instability, and indicate that mutant SPOP may increase response to DNA-damaging therapeutics. DOI: http://dx.doi.org/10.7554/eLife.09207.001

Purpose control: did you process the data for the intended purpose?

A stochastic extension of COWS is presented. First the formalism is given an operational semantics leading to finitely branching transition systems. Then its syntax and semantics are enriched along the lines of Markovian extensions of process calculi. This allows addressing quantitative reasoning about the behaviour of the specified web services. For instance, a simple case study shows that services can be analyzed using the PRISM probabilistic model checker.