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Radiation therapy is an important and effective treatment option for prostate cancer, but high-risk patients are prone to relapse due to radioresistance of cancer cells. Molecular mechanisms that contribute to radioresistance are not fully understood. Novel computational strategies are needed to identify radioresistance driver genes from hundreds of gene copy number alterations. We developed a network-based approach based on lasso regression in combination with network propagation for the analysis of prostate cancer cell lines with acquired radioresistance to identify clinically relevant marker genes associated with radioresistance in prostate cancer patients. We analyzed established radioresistant cell lines of the prostate cancer cell lines DU145 and LNCaP and compared their gene copy number and expression profiles to their radiosensitive parental cells. We found that radioresistant DU145 showed much more gene copy number alterations than LNCaP and their gene expression profiles were highly cell line specific. We learned a genome-wide prostate cancer-specific gene regulatory network and quantified impacts of differentially expressed genes with directly underlying copy number alterations on known radioresistance marker genes. This revealed several potential driver candidates involved in the regulation of cancer-relevant processes. Importantly, we found that ten driver candidates from DU145 (ADAMTS9, AKR1B10, CXXC5, FST, FOXL1, GRPR, ITGA2, SOX17, STARD4, VGF) and four from LNCaP (FHL5, LYPLAL1, PAK7, TDRD6) were able to distinguish irradiated prostate cancer patients into early and late relapse groups. Moreover, in-depth in vitro validations for VGF (Neurosecretory protein VGF) showed that siRNA-mediated gene silencing increased the radiosensitivity of DU145 and LNCaP cells. Our computational approach enabled to predict novel radioresistance driver gene candidates. Additional preclinical and clinical studies are required to further validate the role of VGF and other candidate genes as potential biomarkers for the prediction of radiotherapy responses and as potential targets for radiosensitization of prostate cancer., Competing Interests: The authors have declared that no competing interests exist.

Precision oncology implies the ability to predict which patients will likely respond to specific cancer therapies based on increasingly accurate, high-resolution molecular diagnostics as well as the functional and mechanistic understanding of individual tumors. While molecular stratification of patients can be achieved through different means, a promising approach is next-generation sequencing of tumor DNA and RNA, which can reveal genomic alterations that have immediate clinical implications. Furthermore, certain genetic alterations are shared across multiple histologic entities, raising the fundamental question of whether tumors should be treated by molecular profile and not tissue of origin. We here describe MASTER (Molecularly Aided Stratification for Tumor Eradication Research), a clinically applicable platform for prospective, biology-driven stratification of younger adults with advanced-stage cancer across all histologies and patients with rare tumors. We illustrate how a standardized workflow for selection and consenting of patients, sample processing, whole-exome/genome and RNA sequencing, bioinformatic analysis, rigorous validation of potentially actionable findings, and data evaluation by a dedicated molecular tumor board enables categorization of patients into different intervention baskets and formulation of evidence-based recommendations for clinical management. Critical next steps will be to increase the number of patients that can be offered comprehensive molecular analysis through collaborations and partnering, to explore ways in which additional technologies can aid in patient stratification and individualization of treatment, to stimulate clinically guided exploratory research projects, and to gradually move away from assessing the therapeutic activity of targeted interventions on a case-by-case basis toward controlled clinical trials of genomics-guided treatments., (© 2017 UICC.)

Changes in gene expression programs play a central role in cancer. Chromosomal aberrations such as deletions, duplications and translocations of DNA segments can lead to highly significant positive correlations of gene expression levels of neighboring genes. This should be utilized to improve the analysis of tumor expression profiles. Here, we develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in tumor. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the simultaneous usage of higher-order state-transitions and autoregressive emissions as novel model features. We apply autoregressive higher-order HMMs to the analysis of breast cancer and glioma gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other related methods. The performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions. This benefit could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumors independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas. An implementation is available under www.jstacs.de/index.php/ARHMM.

Oligodendroglial tumors form a distinct subgroup of gliomas, characterized by a better response to treatment and prolonged overall survival. Most oligodendrogliomas and also some oligoastrocytomas are characterized by a unique and typical unbalanced translocation, der(1,19), resulting in a 1p/19q co-deletion. Candidate tumor suppressor genes targeted by these losses, CIC on 19q13.2 and FUBP1 on 1p31.1, were only recently discovered. We analyzed 17 oligodendrogliomas and oligoastrocytomas by applying a comprehensive approach consisting of RNA expression analysis, DNA sequencing of CIC, FUBP1, IDH1/2, and array CGH. We confirmed three different genetic subtypes in our samples: i) the "oligodendroglial" subtype with 1p/19q co-deletion in twelve out of 17 tumors; ii) the "astrocytic" subtype in three tumors; iii) the "other" subtype in two tumors. All twelve tumors with the 1p/19q co-deletion carried the most common IDH1 R132H mutation. In seven of these tumors, we found protein-disrupting point mutations in the remaining allele of CIC, four of which are novel. One of these tumors also had a deleterious mutation in FUBP1. Only by integrating RNA expression and array CGH data, were we able to discover an exon-spanning homozygous microdeletion within the remaining allele of CIC in an additional tumor with 1p/19q co-deletion. Therefore we propose that the mutation rate might be underestimated when looking at sequence variants alone. In conclusion, the high frequency and the spectrum of CIC mutations in our 1p/19q-codeleted tumor cohort support the hypothesis that CIC acts as a tumor suppressor in these tumors, whereas FUBP1 might play only a minor role.

Background Precision oncology, guided by high-resolution molecular diagnostics, refers to the ability to identify patients that are likely to respond to specific anticancer therapies. Although recent studies demonstrated the utility of comprehensive molecular profiling based on whole-exome/genome sequencing and transcriptome sequencing to guide therapeutic decisions in individual patients, many questions remain unanswered. Methods We report the clinical results of MASTER, a multicenter, cross-entity registry trial for prospective, biology-driven stratification of younger adults with advanced-stage cancer and patients with rare tumors conducted by NCT Heidelberg/Dresden and the German Cancer Consortium. From 2013 to 2018, we discussed the molecular profiles from 1,311 poor-prognosis patients in a cross-institutional molecular tumor board. Results Evaluation of the data based on a standardized bioinformatics workflow and manual curation by a team of translational oncologists allowed categorization into 7 intervention baskets and formulation of evidence-based recommendations for clinical management in more than 80% of patients, which could be acted upon in approximately one third of cases. Overall response and disease control rates on molecularly guided treatment were improved compared to prior systemic therapies, which translated into a progression-free survival ratio of greater than 1.3 in more than 30% of cases. Comprehensive genomic profiling also provided relevant diagnostic information that led to categorization of some tumors based on genotype and subsequent histopathologic reevaluation. Conclusions Our experience demonstrates that comprehensive molecular profiling in a multi-institutional clinical setting is feasible, complements and advances routine molecular diagnostics, and creates clinically meaningful therapeutic opportunities. Current and future initiatives of the MASTER network are focused on the standardization of variant classification and evidence levels in molecular tumor boards; the implementation of molecularly stratified basket trials; and the integration of additional layers of patient characterization. Legal entity responsible for the study The authors. Funding German Cancer Consortium, Heidelberg Institute for Personalized Oncology. Disclosure W. Weichert: Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (self): Roche; Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (self): MSD; Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (self): BMS; Advisory / Consultancy, Speaker Bureau / Expert testimony: AstraZeneca; Advisory / Consultancy, Speaker Bureau / Expert testimony: Pfizer; Advisory / Consultancy, Speaker Bureau / Expert testimony: Merck; Advisory / Consultancy, Speaker Bureau / Expert testimony: Lilly; Advisory / Consultancy, Speaker Bureau / Expert testimony: Boehringer Ingelheim; Advisory / Consultancy, Speaker Bureau / Expert testimony: Novartis; Advisory / Consultancy, Speaker Bureau / Expert testimony: Takeda; Research grant / Funding (self): Bruker. All other authors have declared no conflicts of interest. [ABSTRACT FROM AUTHOR]