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Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success. The identification of treatments that selectively co-inhibit cancerous cell populations remains a challenge. Here, a machine learning approach, scTherapy, leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. [ABSTRACT FROM AUTHOR]

Detection and measurement of amyloid‐beta (Aβ) in the brain is a key factor for early identification and diagnosis of Alzheimer's disease (AD). We aimed to develop a deep learning model to predict Aβ cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 Aβ PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical Aβ. We also compared the brain region's relevance for the model's CSF prediction within clinical‐based and biological‐based classifications. ArcheD‐predicted Aβ CSF values correlated with measured Aβ CSF values (r = 0.92; q < 0.01), SUVR (rAV45 = −0.64, rFBB = −0.69; q < 0.01) and episodic memory measures (0.33 < r < 0.44; q < 0.01). For both classifications, cerebral white matter significantly contributed to CSF prediction (q < 0.01), specifically in non‐symptomatic and early stages of AD. However, in late‐stage disease, the brain stem, subcortical areas, cortical lobes, limbic lobe and basal forebrain made more significant contributions (q < 0.01). Considering cortical grey matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted Aβ CSF concentration from Aβ PET scans, offering potential clinical utility for Aβ level determination. [ABSTRACT FROM AUTHOR]

Imaging flow cytometry (IFC) combines flow cytometry with microscopy, allowing rapid characterization of cellular and molecular properties via high‐throughput single‐cell fluorescent imaging. However, fluorescent labeling is costly and time‐consuming. We present a computational method called DeepIFC based on the Inception U‐Net neural network architecture, able to generate fluorescent marker images and learn morphological features from IFC brightfield and darkfield images. Furthermore, the DeepIFC workflow identifies cell types from the generated fluorescent images and visualizes the single‐cell features generated in a 2D space. We demonstrate that rarer cell types are predicted well when a balanced data set is used to train the model, and the model is able to recognize red blood cells not seen during model training as a distinct entity. In summary, DeepIFC allows accurate cell reconstruction, typing and recognition of unseen cell types from brightfield and darkfield images via virtual fluorescent labeling. [ABSTRACT FROM AUTHOR]

Background: A deep understanding of carcinogenesis at the DNA level underpins many advances in cancer prevention and treatment. Mutational signatures provide a breakthrough conceptualisation, as well as an analysis framework, that can be used to build such understanding. They capture somatic mutation patterns and at best identify their causes. Most studies in this context have focused on an inherently additive analysis, e.g. by non-negative matrix factorization, where the mutations within a cancer sample are explained by a linear combination of independent mutational signatures. However, other recent studies show that the mutational signatures exhibit non-additive interactions. Results: We carefully analysed such additive model fits from the PCAWG study cataloguing mutational signatures as well as their activities across thousands of cancers. Our analysis identified systematic and non-random structure of residuals that is left unexplained by the additive model. We used hierarchical clustering to identify cancer subsets with similar residual profiles to show that both systematic mutation count overestimation and underestimation take place. We propose an extension to the additive mutational signature model—multiplicatively acting modulatory processes—and develop a maximum-likelihood framework to identify such modulatory mutational signatures. The augmented model is expressive enough to almost fully remove the observed systematic residual patterns. Conclusion: We suggest the modulatory processes biologically relate to sample specific DNA repair propensities with cancer or tissue type specific profiles. Overall, our results identify an interesting direction where to expand signature analysis. [ABSTRACT FROM AUTHOR]

Microsatellite sequences are particularly prone to slippage during DNA replication, forming insertion-deletion loops that, if left unrepaired, result in de novo mutations (expansions or contractions of the repeat array). Mismatch repair (MMR) is a critical DNA repair mechanism that corrects these insertion-deletion loops, thereby maintaining microsatellite stability. MMR deficiency gives rise to the molecular phenotype known as microsatellite instability (MSI). By sequencing MMR-proficient and -deficient (Mlh1+/+ and Mlh1−/−) single-cell exomes from mouse T cells, we reveal here several previously unrecognized features of in vivo MSI. Specifically, mutational dynamics of insertions and deletions were different on multiple levels. Factors that associated with propensity of mononucleotide microsatellites to insertions versus deletions were: microsatellite length, nucleotide composition of the mononucleotide tract, gene length and transcriptional status, as well replication timing. Here, we show on a single-cell level that deletions — the predominant MSI type in MMR-deficient cells — are preferentially associated with longer A/T tracts, long or transcribed genes and later-replicating genes. [ABSTRACT FROM AUTHOR]

Despite recent progress in acute lymphoblastic leukemia (ALL) therapies, a significant subset of adult and pediatric ALL patients has a dismal prognosis. Better understanding of leukemogenesis and recognition of germline genetic changes may provide new tools for treating patients. Given that hematopoietic stem cell transplantation, often from a family member, is a major form of treatment in ALL, acknowledging the possibility of hereditary predisposition is of special importance. Reports of comprehensive germline analyses performed in adult ALL patients are scarce. Aiming at fulfilling this gap of knowledge, we investigated variants in 93 genes predisposing to hematologic malignancies and 70 other cancer-predisposing genes from exome data obtained from 61 adult and 87 pediatric ALL patients. Our results show that pathogenic (P) or likely pathogenic (LP) germline variants in genes associated with predisposition to ALL or other cancers are prevalent in ALL patients: 8% of adults and 11% of children. Comparison of P/LP germline variants in patients to population-matched controls (gnomAD Finns) revealed a 2.6-fold enrichment in ALL cases (CI 95% 1.5–4.2, p = 0.00071). Acknowledging inherited factors is crucial, especially when considering hematopoietic stem cell transplantation and planning post-therapy follow-up. Harmful germline variants may also predispose patients to excessive toxicity potentially compromising the outcome. We propose integrating germline genetics into precise ALL patient care and providing families genetic counseling. [ABSTRACT FROM AUTHOR]

Background: Uterine leiomyomas can be classified into molecularly distinct subtypes according to their genetic triggers: MED12 mutations, HMGA2 upregulation, or inactivation of FH. The aim of this study was to identify metabolites and metabolic pathways that are dysregulated in different subtypes of leiomyomas.Methods: We performed global metabolomic profiling of 25 uterine leiomyomas and 17 corresponding myometrium specimens using liquid chromatography-tandem mass spectroscopy.Results: A total of 641 metabolites were detected. All leiomyomas displayed reduced homocarnosine and haeme metabolite levels. We identified a clearly distinct metabolomic profile for leiomyomas of the FH subtype, characterised by metabolic alterations in the tricarboxylic acid cycle and pentose phosphate pathways, and increased levels of multiple lipids and amino acids. Several metabolites were uniquely elevated in leiomyomas of the FH subtype, including N6-succinyladenosine and argininosuccinate, serving as potential biomarkers for FH deficiency. In contrast, leiomyomas of the MED12 subtype displayed reduced levels of vitamin A, multiple membrane lipids and amino acids, and dysregulation of vitamin C metabolism, a finding which was also compatible with gene expression data.Conclusions: The study reveals the metabolomic heterogeneity of leiomyomas and provides the requisite framework for strategies designed to target metabolic alterations promoting the growth of these prevalent tumours. [ABSTRACT FROM AUTHOR]

Background: Approximately 4% of colorectal cancer (CRC) patients have at least two simultaneous cancers in the colon. Due to the shared environment, these synchronous CRCs (SCRCs) provide a unique setting to study colorectal carcinogenesis. Understanding whether these tumours are genetically similar or distinct is essential when designing therapeutic approaches. Methods: We performed exome sequencing of 47 primary cancers and corresponding normal samples from 23 patients. Additionally, we carried out a comprehensive mutational signature analysis to assess whether tumours had undergone similar mutational processes and the first immune cell score analysis (IS) of SCRC to analyse the interplay between immune cell invasion and mutation profile in both lesions of an individual. Results: The tumour pairs shared only few mutations, favouring different mutations in known CRC genes and signalling pathways and displayed variation in their signature content. Two tumour pairs had discordant mismatch repair statuses. In majority of the pairs, IS varied between primaries. Differences were not explained by any clinicopathological variable or mutation burden. Conclusions: The study shows major diversity within SCRCs. Rather than rely on data from one tumour, our study highlights the need to evaluate both tumours of a synchronous pair for optimised targeted therapy. [ABSTRACT FROM AUTHOR]

Background: Approximately 4% of colorectal cancer (CRC) patients have at least two simultaneous cancers in the colon. Due to the shared environment, these synchronous CRCs (SCRCs) provide a unique setting to study colorectal carcinogenesis. Understanding whether these tumours are genetically similar or distinct is essential when designing therapeutic approaches.Methods: We performed exome sequencing of 47 primary cancers and corresponding normal samples from 23 patients. Additionally, we carried out a comprehensive mutational signature analysis to assess whether tumours had undergone similar mutational processes and the first immune cell score analysis (IS) of SCRC to analyse the interplay between immune cell invasion and mutation profile in both lesions of an individual.Results: The tumour pairs shared only few mutations, favouring different mutations in known CRC genes and signalling pathways and displayed variation in their signature content. Two tumour pairs had discordant mismatch repair statuses. In majority of the pairs, IS varied between primaries. Differences were not explained by any clinicopathological variable or mutation burden.Conclusions: The study shows major diversity within SCRCs. Rather than rely on data from one tumour, our study highlights the need to evaluate both tumours of a synchronous pair for optimised targeted therapy. [ABSTRACT FROM AUTHOR]

Background: Uterine leiomyomas from hereditary leiomyomatosis and renal cell cancer (HLRCC) patients are driven by fumarate hydratase (FH) inactivation or occasionally by mediator complex subunit 12 (MED12) mutations. The aim of this study was to analyse whether MED12 mutations and FH inactivation are mutually exclusive and to determine the contribution of MED12 mutations on HLRCC patients' myomagenesis.Methods: MED12 exons 1 and 2 mutation screening and 2SC immunohistochemistry indicative for FH deficiency was performed on a comprehensive series of HLRCC patients' (122 specimens) and sporadic (66 specimens) tumours. Gene expression analysis was performed using Affymetrix GeneChip Human Exon Arrays (Affymetrix, Santa Clara, CA, USA).Results: Nine tumours from HLRCC patients harboured a somatic MED12 mutation and were negative for 2SC immunohistochemistry. All remaining successfully analysed lesions (107/116) were deficient for FH. Of sporadic tumours, 35/64 were MED12 mutation positive and none displayed a FH defect. In global gene expression analysis FH-deficient tumours clustered together, whereas HLRCC patients' MED12 mutation-positive tumours clustered together with sporadic MED12 mutation-positive tumours.Conclusions: Somatic MED12 mutations and biallelic FH inactivation are mutually exclusive in both HLRCC syndrome-associated and sporadic uterine leiomyomas. The great majority of HLRCC patients' uterine leiomyomas are caused by FH inactivation, but incidental tumours driven by somatic MED12 mutations also occur. These MED12 mutation-positive tumours display similar expressional profiles with their sporadic counterparts and are clearly separate from FH-deficient tumours. [ABSTRACT FROM AUTHOR]

Background: Typical human genome differs from the reference genome at 4-5 million sites. This diversity is increasingly catalogued in repositories such as ExAC/gnomAD, consisting of >15,000 whole-genomes and >126,000 exome sequences from different individuals. Despite this enormous diversity, resequencing data workflows are still based on a single human reference genome. Identification and genotyping of genetic variants is typically carried out on short-read data aligned to a single reference, disregarding the underlying variation. Results: We propose a new unified framework for variant calling with short-read data utilizing a representation of human genetic variation - a pan-genomic reference. We provide a modular pipeline that can be seamlessly incorporated into existing sequencing data analysis workflows. Our tool is open source and available online: https://gitlab.com/dvalenzu/PanVC. Conclusions: Our experiments show that by replacing a standard human reference with a pan-genomic one we achieve an improvement in single-nucleotide variant calling accuracy and in short indel calling accuracy over the widely adopted Genome Analysis Toolkit (GATK) in difficult genomic regions. [ABSTRACT FROM AUTHOR]

Small bowel adenocarcinoma (SBA) is an aggressive disease with limited treatment options. Despite previous studies, its molecular genetic background has remained somewhat elusive. To comprehensively characterize the mutational landscape of this tumor type, and to identify possible targets of treatment, we conducted the first large exome sequencing study on a population-based set of SBA samples from all three small bowel segments. Archival tissue from 106 primary tumors with appropriate clinical information were available for exome sequencing from a patient series consisting of a majority of confirmed SBA cases diagnosed in Finland between the years 2003–2011. Paired-end exome sequencing was performed using Illumina HiSeq 4000, and OncodriveFML was used to identify driver genes from the exome data. We also defined frequently affected cancer signalling pathways and performed the first extensive allelic imbalance (AI) analysis in SBA. Exome data analysis revealed significantly mutated genes previously linked to SBA (TP53, KRAS, APC, SMAD4, and BRAF), recently reported potential driver genes (SOX9, ATM, and ARID2), as well as novel candidate driver genes, such as ACVR2A, ACVR1B, BRCA2, and SMARCA4. We also identified clear mutation hotspot patterns in ERBB2 and BRAF. No BRAF V600E mutations were observed. Additionally, we present a comprehensive mutation signature analysis of SBA, highlighting established signatures 1A, 6, and 17, as well as U2 which is a previously unvalidated signature. Finally, comparison of the three small bowel segments revealed differences in tumor characteristics. This comprehensive work unveils the mutational landscape and most frequently affected genes and pathways in SBA, providing potential therapeutic targets, and novel and more thorough insights into the genetic background of this tumor type. [ABSTRACT FROM AUTHOR]

ABSTRACT MED12 is a key component of the transcription-regulating Mediator complex. Specific missense and in-frame insertion/deletion mutations in exons 1 and 2 have been identified in uterine leiomyomas, breast tumors, and chronic lymphocytic leukemia. Here, we characterize the first MED12 5′ end nonsense mutation (c.97G>T, p.E33X) identified in acute lymphoblastic leukemia and show that it escapes nonsense-mediated mRNA decay (NMD) by using an alternative translation initiation site. The resulting N-terminally truncated protein is unable to enter the nucleus due to the lack of identified nuclear localization signal (NLS). The absence of NLS prevents the mutant MED12 protein to be recognized by importin-α and subsequent loading into the nuclear pore complex. Due to this mislocalization, all interactions between the MED12 mutant and other Mediator components are lost. Our findings provide new mechanistic insights into the MED12 functions and indicate that somatic nonsense mutations in early exons may avoid NMD. [ABSTRACT FROM AUTHOR]

Introduction: In sporadic acromegaly, downregulation of AIP protein of the adenomas associates with invasive tumor features and reduced responsiveness to somatostatin analogues. AIP is a regulator of Gai signaling, but it is not known how the biological function of the Gai pathway is controlled. Aim: To study GNAS and AIP mutation status, AIP and Gai-2 protein expressions, Ki-67 proliferation indices and clinical parameters in patients having primary surgery because of acromegaly at a single center between years 2000 and 2010. Results: Sixty patients (F/M, 31/29), mean age 49 (median 50), mean follow-up 7.7 years (range 0.6–14.0) underwent primary surgery. Four adenoma specimens (6.8%) harbored an AIP and 21 (35.6%) an activating GNAS (Gsp+) mutation. Altogether 13/56 (23%) adenomas had low AIP protein levels, and 14/56 (25%) low Gai-2 staining. In regression modeling, AIP expression associated with Gai-2 (P = 2.33 × 10−9) and lower Ki-67 (P = 0.04). In pairwise comparison, low AIP protein predicted high GH at last follow-up (mean 7.7 years after surgery, q = 0.045). Extent of treatments given for acromegaly associated with higher preoperative GH (P = 7.94 × 10−4), KNOSP (P = 0.003) and preoperative hypopituitarism (P = 0.03) and remission at last follow-up with change in 3-month postoperative IGF1 (P = 2.07 × 10−7). Conclusions: We demonstrate, for the first time, that AIP protein expression associates with Gai-2 protein intensities in sporadic somatotropinomas, suggesting a joint regulation on somatostatin signaling. Low AIP level associates with higher proliferative activity and predicts high GH concentrations after long-term follow-up. The AIP mutation rate of 6.8% is fairly high, reflecting the genetic composition of the Finnish population. [ABSTRACT FROM AUTHOR]

Background: Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis. Results: A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model. Conclusions: The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. [ABSTRACT FROM AUTHOR]

Uterine leiomyomas are common benign smooth muscle tumors that impose a major burden on women's health. Recent sequencing studies have revealed recurrent and mutually exclusive mutations in leiomyomas, suggesting the involvement of molecularly distinct pathways. In this study, we explored transcriptional differences among leiomyomas harboring different genetic drivers, including high mobility group AT-hook 2 (HMGA2) rearrangements, mediator complex subunit 12 (MED12) mutations, biallelic inactivation of fumarate hydratase (FH), and collagen, type IV, alpha 5 and collagen, type IV, alpha 6 (COL4A5- COL4A6) deletions. We also explored the transcriptional consequences of 7q22, 22q, and 1p deletions, aiming to identify possible target genes. We investigated 94 leiomyomas and 60 corresponding myometrial tissues using exon arrays, whole genome sequencing, and SNP arrays. This integrative approach revealed subtype-specific expression changes in key driver pathways, including Wnt/β-catenin, Prolactin, and insulin-like growth factor (IGF)1 signaling. Leiomyomas with HMGA2 aberrations displayed highly significant up-regulation of the proto-oncogene pleomorphic adenoma gene 1 (PLAG1), suggesting that HMGA2 promotes tumorigenesis through PLAG1 activation. This was supported by the identification of genetic PLAG1 alterations resulting in expression signatures as seen in leiomyomas with HMGA2 aberrations. RAD51 paralog B (RAD51B), the preferential translocation partner of HMGA2, was up-regulated in MED12 mutant lesions, suggesting a role for this gene in the genesis of leiomyomas. FH-deficient leiomyomas were uniquely characterized by activation of nuclear factor erythroid 2-related factor 2 (NRF2) target genes, supporting the hypothesis that accumulation of fumarate leads to activation of the oncogenic transcription factor NRF2. This study emphasizes the need for molecular stratification in leiomyoma research and possibly in clinical practice as well. Further research is needed to determine whether the candidate biomarkers presented herein can provide guidance for managing the millions of patients affected by these lesions. [ABSTRACT FROM AUTHOR]

Approximately 15 % of colorectal cancers exhibit instability of short nucleotide repeat regions, microsatellites. These tumors display a unique clinicopathologic profile and the microsatellite instability status is increasingly used to guide clinical management as it is known to predict better prognosis as well as resistance to certain chemotherapeutics. A panel of five repeats determined by the National Cancer Institute, the Bethesda panel, is currently the standard for determining the microsatellite instability status in colorectal cancer. Recently, a quasimonomorphic mononucleotide repeat 16T/U at the 3′ untranslated region of the Ewing sarcoma breakpoint region 1 gene was reported to show perfect sensitivity and specificity in detecting mismatch repair deficient colorectal, endometrial, and gastric cancers in two independent populations. To confirm this finding, we replicated the analysis in 213 microsatellite unstable colorectal cancers from two independent populations, 148 microsatellite stable colorectal cancers, and the respective normal samples by PCR and fragment analysis. The repeat showed nearly perfect sensitivity for microsatellite unstable colorectal cancer as it was altered in 212 of the 213 microsatellite unstable (99.5 %) and none of the microsatellite stable colorectal tumors. This repeat thus represents the first potential single marker for detecting microsatellite instability. [ABSTRACT FROM AUTHOR]

Cohesin is present in almost all active enhancer regions, where it is associated with transcription factors. Cohesin frequently colocalizes with CTCF (CCCTC-binding factor), affecting genomic stability, expression and epigenetic homeostasis. Cohesin subunits are mutated in cancer, but CTCF/cohesin-binding sites (CBSs) in DNA have not been examined for mutations. Here we report frequent mutations at CBSs in cancers displaying a mutational signature where mutations in A•T base pairs predominate. Integration of whole-genome sequencing data from 213 colorectal cancer (CRC) samples and chromatin immunoprecipitation sequencing (ChIP-exo) data identified frequent point mutations at CBSs. In contrast, CRCs showing an ultramutator phenotype caused by defects in the exonuclease domain of DNA polymerase ɛ (POLE) displayed significantly fewer mutations at and adjacent to CBSs. Analysis of public data showed that multiple cancer types accumulate CBS mutations. CBSs are a major mutational hotspot in the noncoding cancer genome. [ABSTRACT FROM AUTHOR]

ARID1A has been identified as a novel tumor suppressor gene in ovarian cancer and subsequently in various other tumor types. ARID1A belongs to the ARID domain containing gene family, which comprises of 15 genes involved, for example, in transcriptional regulation, proliferation and chromatin remodeling. In this study, we used exome sequencing data to analyze the mutation frequency of all the ARID domain containing genes in 25 microsatellite unstable (MSI) colorectal cancers (CRCs) as a first systematic effort to characterize the mutation pattern of the whole ARID gene family. Genes which fulfilled the selection criteria in this discovery set (mutations in at least 4/25 [16%] samples, including at least one nonsense or splice site mutation) were chosen for further analysis in an independent validation set of 21 MSI CRCs. We found that in addition to ARID1A, which was mutated in 39% of the tumors (18/46), also ARID1B (13%, 6/46), ARID2 (13%, 6/46) and ARID4A (20%, 9/46) were frequently mutated. In all these genes, the mutations were distributed along the entire length of the gene, thus distinguishing them from typical MSI target genes previously described. Our results indicate that in addition to ARID1A, other members of the ARID gene family may play a role in MSI CRC. [ABSTRACT FROM AUTHOR]

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. [ABSTRACT FROM AUTHOR]

Hereditary factors are presumed to play a role in one third of colorectal cancer (CRC) cases. However, in the majority of familial CRC cases the genetic basis of predisposition remains unexplained. This is particularly true for families with few affected individuals. To identify susceptibility genes for this common phenotype, we examined familial cases derived from a consecutive series of 1514 Finnish CRC patients. Ninety-six familial CRC patients with no previous diagnosis of a hereditary CRC syndrome were included in the analysis. Eighty-six patients had one affected first-degree relative, and ten patients had two or more. Exome sequencing was utilized to search for genes harboring putative loss-of-function variants, because such alterations are likely candidates for disease-causing mutations. Eleven genes with rare truncating variants in two or three familial CRC cases were identified: UACA, SFXN4, TWSG1, PSPH, NUDT7, ZNF490, PRSS37, CCDC18, PRADC1, MRPL3, and AKR1C4. Loss of heterozygosity was examined in all respective cancer samples, and was detected in seven occasions involving four of the candidate genes. In all seven occasions the wild-type allele was lost (P = 0.0078) providing additional evidence that these eleven genes are likely to include true culprits. The study provides a set of candidate predisposition genes which may explain a subset of common familial CRC. Additional genetic validation in other populations is required to provide firm evidence for causality, as well as to characterize the natural history of the respective phenotypes. [ABSTRACT FROM AUTHOR]

Many cancer predisposition syndromes are rare or have incomplete penetrance, and traditional epidemiological tools are not well suited for their detection. Here we have used an approach that employs the entire population based data in the Finnish Cancer Registry (FCR) for analyzing familial aggregation of all types of cancer, in order to find evidence for previously unrecognized cancer susceptibility conditions. We performed a systematic clustering of 878,593 patients in FCR based on family name at birth, municipality of birth, and tumor type, diagnosed between years 1952 and 2011. We also estimated the familial occurrence of the tumor types using cluster score that reflects the proportion of patients belonging to the most significant clusters compared to all patients in Finland. The clustering effort identified 25,910 birth name-municipality based clusters representing 183 different tumor types characterized by topography and morphology. We produced information about familial occurrence of hundreds of tumor types, and many of the tumor types with high cluster score represented known cancer syndromes. Unexpectedly, Kaposi sarcoma (KS) also produced a very high score (cluster score 1.91, p-value <0.0001). We verified from population records that many of the KS patients forming the clusters were indeed close relatives, and identified one family with five affected individuals in two generations and several families with two first degree relatives. Our approach is unique in enabling systematic examination of a national epidemiological database to derive evidence of aberrant familial aggregation of all tumor types, both common and rare. It allowed effortless identification of families displaying features of both known as well as potentially novel cancer predisposition conditions, including striking familial aggregation of KS. Further work with high-throughput methods should elucidate the molecular basis of the potentially novel predisposition conditions found in this study. [ABSTRACT FROM AUTHOR]

Point mutations in cancer have been extensively studied but chromosomal gains and losses have been more challenging to interpret due to their unspecific nature. Here we examine high-resolution allelic imbalance (AI) landscape in 1699 colorectal cancers, 256 of which have been whole-genome sequenced (WGSed). The imbalances pinpoint 38 genes as plausible AI targets based on previous knowledge. Unbiased CRISPR-Cas9 knockout and activation screens identified in total 79 genes within AI peaks regulating cell growth. Genetic and functional data implicate loss of TP53 as a sufficient driver of AI. The WGS highlights an influence of copy number aberrations on the rate of detected somatic point mutations. Importantly, the data reveal several associations between AI target genes, suggesting a role for a network of lineage-determining transcription factors in colorectal tumorigenesis. Overall, the results unravel the contribution of AI in colorectal cancer and provide a plausible explanation why so few genes are commonly affected by point mutations in cancers. In this study the authors examine the allelic imbalance (AI) landscape of colorectal cancer, reporting loss of TP53 as a driver of AI. They use CRISPR-Cas9 screens to identify 79 genes (within AI regions) regulating cell growth and identify a network of transcription factors that may contribute to colorectal tumorigenesis. [ABSTRACT FROM AUTHOR]