Highlights d Detailed, large-scale mechanistic model of cancer-related signaling pathways d Speedup of over 10,000-fold enables data-driven modeling at unprecedented scales d Pronounced parameter uncertainties do not imply pronounced prediction uncertainties d Mechanistic models can predict response to drug combinations from single drug data

Patients with an interstitial 13q deletion that contains the RB1 gene show retinoblastoma and variable clinical features. Relationship between phenotypic expression and loss of specific neighboring genes are unresolved, yet. We obtained clinical, cytogenetic and molecular data in 63 patients with an interstitial 13q deletion involving RB1. Whole-genome array analysis or customized high-resolution array analysis for 13q14.11q14.3 was performed in 38 patients, and cytogenetic analysis was performed in 54 patients. Deletion sizes ranged between 4.2 kb and more than 33.43 Mb; breakpoints were non-recurrent. Sequence analysis of deletion junctions in five patients revealed microhomology and insertion of 2-34 base pairs suggestive of non-homologous end joining. Milder phenotypic expression of retinoblastoma was observed in patients with deletions larger than 1 Mb, which contained the MED4 gene. Clinical features were compared between patients with small (within 13q14), medium (within 13q12.3q21.2) and large (within 13q12q31.2) deletions. Patients with a small deletion can show macrocephaly, tall stature, obesity, motor and/or speech delay. Patients with a medium deletion show characteristic facial features, mild to moderate psychomotor delay, short stature and microcephaly. Patients with a large deletion have characteristic craniofacial dysmorphism, short stature, microcephaly, mild to severe psychomotor delay, hypotonia, constipation and feeding problems. Additional features included deafness, seizures and brain and heart anomalies. We found no correlation between clinical features and parental origin of the deletion. Our data suggest that hemizygous loss of NUFIP1 and PCDH8 may contribute to psychomotor delay, deletion of MTLR1 to microcephaly and loss of EDNRB to feeding difficulties and deafness.

AML/MDS patients carrying 11q amplifications involving the mixed lineage leukemia gene (MLL) locus are characterized by a complex aberrant karyotype (CAK) frequently including deletions within 5q, 17p, and 7q, older age and fast progression of the disease with extremely poor prognosis. MLL has been shown to be overexpressed in cases with 11q amplification. However, in most of the cases, the amplified region is not restricted to the MLL locus. In this study, we investigated 19 patients with AML/MDS and MLL gain/amplification. By means of array CGH performed in 12 patients, we were able to delineate the minimal deleted regions within 5q and 17p and identified three independent regions 11q/I-III that were amplified in all cases. Gene expression profiles established in 15 cases were used to identify candidate genes within these regions. Notably, analysis of our data suggests a correlation of loss of 5q and 17p and expression of genes present in 11q23-25. Furthermore, we demonstrate that the gene expression signature can be used to discriminate AML/MDS with MLL amplification from several other types of AML.

The response of cancer cells to drugs is determined by various factors, including the cells' mutations and gene expression levels. These factors can be assessed using next-generation sequencing. Their integration with vast prior knowledge on signaling pathways is, however, limited by the availability of mathematical models and scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. With this framework, we parameterized a mechanistic model describing major cancer-associated signaling pathways (>1200 species and >2600 reactions) using drug response data. For the parameterized mechanistic model, we found a prediction accuracy, which exceeds that of the considered statistical approaches. Our results demonstrate for the first time the massive integration of heterogeneous datasets using large-scale mechanistic models, and how these models facilitate individualized predictions of drug response. We anticipate our parameterized model to be a starting point for the development of more comprehensive, curated models of signaling pathways, accounting for additional pathways and drugs. IntroductionPersonalized tumor therapy relies on our ability to predict the drug response of cancer cells from genomic data 1 . This requires the integration of genomic data with available prior knowledge, and its interpretation in the context of cancer-associated processes 2 . At the heart of this endeavor are statistical and mechanistic mathematical models 3 . In patient . CC-BY-NC-ND 4.0 International license not peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was . http://dx.doi.org/10.1101/174094 doi: bioRxiv preprint first posted online Aug. 9, 2017; 2 stratification, statistical models are used to derive prognostic and predictive signatures of tumor subtypes 4,5 . Linear and nonlinear regression, machine learning methods and related approaches have been used to obtain such signatures 6 . Yet, purely statistical models do not provide mechanistic insights or information about actionable targets. High-quality mechanistic models of cancer signaling are thus of interest to researchers and clinicians in systems biology and systems medicine.Mechanistic models aim to quantitatively describe biological processes. Consequently, they facilitate the systematic integration of prior knowledge on signaling pathways, as well as the effect of somatic mutations and gene expression. These models have been used for the identification of drug targets 7 as well as the development of prognostic signatures 8,9 .Furthermore, mechanistic modeling has facilitated the study of oncogene addiction 10 , After the construction of a mechanistic model, parameterization from experimental data is necessary to render the model predictive. Optimization methods achieve this by iteratively minimizing the objective f...

Accidents with ionizing radiation often involve single, acute high-dose exposures that can lead to acute radiation syndrome and late effects such as carcinogenesis. To study such effects at the cellular level, we investigated acute ionizing radiation-induced chromosomal aberrations in A549 adenocarcinoma cells at the genome-wide level by exposing the cells to an acute dose of 6 Gy 240 kV X rays. One sham-irradiated clone and four surviving irradiated clones were recovered by minimal dilution and further expanded and analyzed by chromosome painting and tiling-path array CGH, with the nonirradiated clone 0 serving as the control. Acute X-ray exposure induced specific translocations and changes in modal chromosome number in the four irradiated clones. Array CGH disclosed unique and recurrent genomic changes, predominantly losses, and revealed that the fragile sites FRA3B and FRA16D were preferential regions of genomic alterations in all irradiated clones, which is likely related to radioresistant S-phase progression and genomic stress. Furthermore, clone 4 displayed an increased radiosensitivity at doses >5 Gy. Pairwise comparisons of the gene expression patterns of all irradiated clones to the sham-irradiated clone 0 revealed an enrichment of the Gene Ontology term "M Phase" (P = 6.2 × 10(-7)) in the set of differentially expressed genes of clone 4 but not in those of clones 1-3. Ionizing radiation-induced genomic changes and fragile site expression highlight the capacity of a single acute radiation exposure to affect the genome of exposed cells by inflicting genomic stress.