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Abstract Background Elucidating the genotype-phenotype connection is one of the big challenges of modern molecular biology. To fully understand this connection, it is necessary to consider the underlying networks and the time factor. In this context of data deluge and heterogeneous information, visualization plays an essential role in interpreting complex and dynamic topologies. Thus, software that is able to bring the network, phenotypic and temporal information together is needed. Arena3D has been previously introduced as a tool that facilitates link discovery between processes. It uses a layered display to separate different levels of information while emphasizing the connections between them. We present novel developments of the tool for the visualization and analysis of dynamic genotype-phenotype landscapes. Results Version 2.0 introduces novel features that allow handling time course data in a phenotypic context. Gene expression levels or other measures can be loaded and visualized at different time points and phenotypic comparison is facilitated through clustering and correlation display or highlighting of impacting changes through time. Similarity scoring allows the identification of global patterns in dynamic heterogeneous data. In this paper we demonstrate the utility of the tool on two distinct biological problems of different scales. First, we analyze a medium scale dataset that looks at perturbation effects of the pluripotency regulator Nanog in murine embryonic stem cells. Dynamic cluster analysis suggests alternative indirect links between Nanog and other proteins in the core stem cell network. Moreover, recurrent correlations from the epigenetic to the translational level are identified. Second, we investigate a large scale dataset consisting of genome-wide knockdown screens for human genes essential in the mitotic process. Here, a potential new role for the gene lsm14a in cytokinesis is suggested. We also show how phenotypic patterning allows for extensive comparison and identification of high impact knockdown targets. Conclusions We present a new visualization approach for perturbation screens with multiple phenotypic outcomes. The novel functionality implemented in Arena3D enables effective understanding and comparison of temporal patterns within morphological layers, to help with the system-wide analysis of dynamic processes. Arena3D is available free of charge for academics as a downloadable standalone application from: http://arena3d.org/.

Abstract Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system.

Spatial transcriptomics is revolutionizing the exploration of intratissue heterogeneity in cancer, yet capturing cellular niches and their spatial relationships remains challenging. We introduce SpottedPy, a Python package designed to identify tumor hotspots and map spatial interactions within the cancer ecosystem. Using SpottedPy, we examine epithelial-mesenchymal plasticity in breast cancer and highlight stable niches associated with angiogenic and hypoxic regions, shielded by CAFs and macrophages. Hybrid and mesenchymal hotspot distribution follows transformation gradients reflecting progressive immunosuppression. Our method offers flexibility to explore spatial relationships at different scales, from immediate neighbors to broader tissue modules, providing new insights into tumor microenvironment dynamics., Competing Interests: Declarations Ethics approval and consent to participate All datasets employed in this study are publicly available and comply with ethical regulations, with approval and informed consent for collection and sharing already obtained by the relevant organizations. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Secreted proteins regulate the balance between cellular proliferation and G0 arrest and therefore play important roles in tumour dormancy. Tumour dormancy presents a significant clinical challenge for breast cancer patients, where non-proliferating, G0-arrested cancer cells remain at metastatic sites, below the level of clinical detection, some of which can re-enter proliferation and drive tumour relapse. Knowing which secreted proteins can regulate entry into and exit from G0 allows us to manipulate their signalling to prevent tumour relapse. To identify novel secreted proteins that can promote breast cancer G0 arrest, we performed a secretome-wide, image-based screen for proteins that increase the fraction of cells in G0 arrest. From a secretome library of 1282 purified proteins, we identified 29 candidates that promote G0 arrest in non-transformed and transformed breast epithelial cells. The assay we have developed can be adapted for use in other perturbation screens in other cell types. All datasets have been made available for re-analysis and our candidate proteins are presented for alternative bioinformatic refinement or further experimental follow up., (© 2024. The Author(s).)

Background: Carcinogenesis is driven by interactions between genetic mutations and the local tumor microenvironment. Recent research has identified hundreds of cancer driver genes; however, these studies often include a mixture of different molecular subtypes and ecological niches and ignore the impact of the immune system., Results: In this study, we compare the landscape of driver genes in tumors that escaped the immune system (escape +) versus those that did not (escape -). We analyze 9896 primary tumors from The Cancer Genome Atlas using the ratio of non-synonymous to synonymous mutations (dN/dS) and find 85 driver genes, including 27 and 16 novel genes, in escape - and escape + tumors, respectively. The dN/dS of driver genes in immune escaped tumors is significantly lower and closer to neutrality than in non-escaped tumors, suggesting selection buffering in driver genes fueled by immune escape. Additionally, we find that immune evasion leads to more mutated sites, a diverse array of mutational signatures and is linked to tumor prognosis., Conclusions: Our findings highlight the need for improved patient stratification to identify new therapeutic targets for cancer treatment., (© 2024. The Author(s).)

The dysregulated immune response and inflammation resulting in severe COVID-19 are still incompletely understood. Having recently determined that aberrant death-ligand-induced cell death can cause lethal inflammation, we hypothesized that this process might also cause or contribute to inflammatory disease and lung failure following SARS-CoV-2 infection. To test this hypothesis, we developed a novel mouse-adapted SARS-CoV-2 model (MA20) that recapitulates key pathological features of COVID-19. Concomitantly with occurrence of cell death and inflammation, FasL expression was significantly increased on inflammatory monocytic macrophages and NK cells in the lungs of MA20-infected mice. Importantly, therapeutic FasL inhibition markedly increased survival of both, young and old MA20-infected mice coincident with substantially reduced cell death and inflammation in their lungs. Intriguingly, FasL was also increased in the bronchoalveolar lavage fluid of critically-ill COVID-19 patients. Together, these results identify FasL as a crucial host factor driving the immuno-pathology that underlies COVID-19 severity and lethality, and imply that patients with severe COVID-19 may significantly benefit from therapeutic inhibition of FasL., (© 2024. The Author(s).)

The proteostasis network (PN) is a collection of protein folding and degradation pathways that spans cellular compartments and acts to preserve the integrity of the proteome. The differential expression of PN genes is a hallmark of many cancers, and the inhibition of protein quality control factors is an effective way to slow cancer cell growth. However, little is known about how the expression of PN genes differs between patients and how this impacts survival outcomes. To address this, we applied unbiased hierarchical clustering to gene expression data obtained from primary and metastatic cutaneous melanoma (CM) samples and found that two distinct groups of individuals emerge across each sample type. These patient groups are distinguished by the differential expression of genes encoding ATP-dependent and ATP-independent chaperones, and proteasomal subunits. Differences in PN gene expression were associated with increased levels of the transcription factors, MEF2A, SP4, ZFX, CREB1 and ATF2, as well as markedly different survival outcomes. However, surprisingly, similar PN alterations in primary and metastatic samples were associated with discordant survival outcomes in patients. Our findings reveal that the expression of PN genes demarcates CM patients and highlights several new proteostasis sub-networks that could be targeted for more effective suppression of CM within specific individuals., (© 2024. The Author(s).)

Background: Homologous recombination is a robust, broadly error-free mechanism of double-strand break repair, and deficiencies lead to PARP inhibitor sensitivity. Patients displaying homologous recombination deficiency can be identified using 'mutational signatures'. However, these patterns are difficult to reliably infer from exome sequencing. Additionally, as mutational signatures are a historical record of mutagenic processes, this limits their utility in describing the current status of a tumour., Methods: We apply two methods for characterising homologous recombination deficiency in breast cancer to explore the features and heterogeneity associated with this phenotype. We develop a likelihood-based method which leverages small insertions and deletions for high-confidence classification of homologous recombination deficiency for exome-sequenced breast cancers. We then use multinomial elastic net regression modelling to develop a transcriptional signature of heterogeneous homologous recombination deficiency. This signature is then applied to single-cell RNA-sequenced breast cancer cohorts enabling analysis of homologous recombination deficiency heterogeneity and differential patterns of tumour microenvironment interactivity., Results: We demonstrate that the inclusion of indel events, even at low levels, improves homologous recombination deficiency classification. Whilst BRCA-positive homologous recombination deficient samples display strong similarities to those harbouring BRCA1/2 defects, they appear to deviate in microenvironmental features such as hypoxic signalling. We then present a 228-gene transcriptional signature which simultaneously characterises homologous recombination deficiency and BRCA1/2-defect status, and is associated with PARP inhibitor response. Finally, we show that this signature is applicable to single-cell transcriptomics data and predict that these cells present a distinct milieu of interactions with their microenvironment compared to their homologous recombination proficient counterparts, typified by a decreased cancer cell response to TNFα signalling., Conclusions: We apply multi-scale approaches to characterise homologous recombination deficiency in breast cancer through the development of mutational and transcriptional signatures. We demonstrate how indels can improve homologous recombination deficiency classification in exome-sequenced breast cancers. Additionally, we demonstrate the heterogeneity of homologous recombination deficiency, especially in relation to BRCA1/2-defect status, and show that indications of this feature can be captured at a single-cell level, enabling further investigations into interactions between DNA repair deficient cells and their tumour microenvironment., (© 2023. The Author(s).)

Head and neck squamous cell carcinoma (HNSCC) is a molecularly and spatially heterogeneous disease frequently characterized by impairment of immunosurveillance mechanisms. Despite recent success with immunotherapy treatment, disease progression still occurs quickly after treatment in the majority of cases, suggesting the need to improve patient selection strategies. In the quest for biomarkers that may help inform response to checkpoint blockade, we characterized the tumor microenvironment (TME) of 162 HNSCC primary tumors of diverse etiologic and spatial origin, through gene expression and IHC profiling of relevant immune proteins, T-cell receptor (TCR) repertoire analysis, and whole-exome sequencing. We identified five HNSCC TME categories based on immune/stromal composition: (i) cytotoxic, (ii) plasma cell rich, (iii) dendritic cell rich, (iv) macrophage rich, and (v) immune-excluded. Remarkably, the cytotoxic and plasma cell rich subgroups exhibited a phenotype similar to tertiary lymphoid structures (TLS), which have been previously linked to immunotherapy response. We also found an increased richness of the TCR repertoire in these two subgroups and in never smokers. Mutational patterns evidencing APOBEC activity were enriched in the plasma cell high subgroup. Furthermore, specific signal propagation patterns within the Ras/ERK and PI3K/AKT pathways associated with distinct immune phenotypes. While traditionally CD8/CD3 T-cell infiltration and immune checkpoint expression (e.g., PD-L1) have been used in the patient selection process for checkpoint blockade treatment, we suggest that additional biomarkers, such as TCR productive clonality, smoking history, and TLS index, may have the ability to pull out potential responders to benefit from immunotherapeutic agents., Significance: Here we present our findings on the genomic and immune landscape of primary disease in a cohort of 162 patients with HNSCC, benefitting from detailed molecular and clinical characterization. By employing whole-exome sequencing and gene expression analysis of relevant immune markers, TCR profiling, and staining of relevant proteins involved in immune response, we highlight how distinct etiologies, cell intrinsic, and environmental factors combine to shape the landscape of HNSCC primary disease., (© 2023 The Authors; Published by the American Association for Cancer Research.)

Hematoxylin and eosin (H&E) stained slides are widely used in disease diagnosis. Remarkable advances in deep learning have made it possible to detect complex molecular patterns in these histopathology slides, suggesting automated approaches could help inform pathologists' decisions. Multiple instance learning (MIL) algorithms have shown promise in this context, outperforming transfer learning (TL) methods for various tasks, but their implementation and usage remains complex. We introduce HistoMIL, a Python package designed to streamline the implementation, training and inference process of MIL-based algorithms for computational pathologists and biomedical researchers. It integrates a self-supervised learning module for feature encoding, and a full pipeline encompassing TL and three MIL algorithms: ABMIL, DSMIL, and TransMIL. The PyTorch Lightning framework enables effortless customization and algorithm implementation. We illustrate HistoMIL's capabilities by building predictive models for 2,487 cancer hallmark genes on breast cancer histology slides, achieving AUROC performances of up to 85%., Competing Interests: The authors declare no competing interests., (© 2023 The Author(s).)

A variety of mutational processes drive cancer development, but their dynamics across the entire disease spectrum from pre-cancerous to advanced neoplasia are poorly understood. We explore the mutagenic processes shaping oesophageal adenocarcinoma tumorigenesis in 997 instances comprising distinct stages of this malignancy, from Barrett Oesophagus to primary tumours and advanced metastatic disease. The mutational landscape is dominated by the C[T > C/G]T substitution enriched signatures SBS17a/b, which are linked with TP53 mutations, increased proliferation, genomic instability and disease progression. The APOBEC mutagenesis signature is a weak but persistent signal amplified in primary tumours. We also identify prevalent alterations in DNA damage repair pathways, with homologous recombination, base and nucleotide excision repair and translesion synthesis mutated in up to 50% of the cohort, and surprisingly uncoupled from transcriptional activity. Among these, the presence of base excision repair deficiencies show remarkably poor prognosis in the cohort. In this work, we provide insights on the mutational aetiology and changes enabling the transition from pre-neoplastic to advanced oesophageal adenocarcinoma., (© 2023. The Author(s).)

Background: Therapy resistance in cancer is often driven by a subpopulation of cells that are temporarily arrested in a non-proliferative G0 state, which is difficult to capture and whose mutational drivers remain largely unknown., Results: We develop methodology to robustly identify this state from transcriptomic signals and characterise its prevalence and genomic constraints in solid primary tumours. We show that G0 arrest preferentially emerges in the context of more stable, less mutated genomes which maintain TP53 integrity and lack the hallmarks of DNA damage repair deficiency, while presenting increased APOBEC mutagenesis. We employ machine learning to uncover novel genomic dependencies of this process and validate the role of the centrosomal gene CEP89 as a modulator of proliferation and G0 arrest capacity. Lastly, we demonstrate that G0 arrest underlies unfavourable responses to various therapies exploiting cell cycle, kinase signalling and epigenetic mechanisms in single-cell data., Conclusions: We propose a G0 arrest transcriptional signature that is linked with therapeutic resistance and can be used to further study and clinically track this state., (© 2023. The Author(s).)

The epithelial to mesenchymal transition (EMT) is a key cellular process underlying cancer progression, with multiple intermediate states whose molecular hallmarks remain poorly characterised. To fill this gap, we present a method to robustly evaluate EMT transformation in individual tumours based on transcriptomic signals. We apply this approach to explore EMT trajectories in 7180 tumours of epithelial origin and identify three macro-states with prognostic and therapeutic value, attributable to epithelial, hybrid E/M and mesenchymal phenotypes. We show that the hybrid state is relatively stable and linked with increased aneuploidy. We further employ spatial transcriptomics and single cell datasets to explore the spatial heterogeneity of EMT transformation and distinct interaction patterns with cytotoxic, NK cells and fibroblasts in the tumour microenvironment. Additionally, we provide a catalogue of genomic events underlying distinct evolutionary constraints on EMT transformation. This study sheds light on the aetiology of distinct stages along the EMT trajectory, and highlights broader genomic and environmental hallmarks shaping the mesenchymal transformation of primary tumours., (© 2023. The Author(s).)

Many lines of evidence have highlighted the role played by heterogeneous nuclear ribonucleoproteins in amyotrophic lateral sclerosis. In this study, we have aimed to identify transcripts co-regulated by TAR DNA-binding protein 43 kDa and highly conserved heterogeneous nuclear ribonucleoproteins which have been previously shown to regulate TAR DNA-binding protein 43 kDa toxicity (deleted in azoospermia-associated protein 1, heterogeneous nuclear ribonucleoprotein -Q, -D, -K and -U). Using the transcriptome analyses, we have uncovered that Nitric Oxide Synthase 1 Adaptor Protein mRNA is a direct TAR DNA-binding protein 43 kDa target, and in flies, its modulation alone can rescue TAR DNA-binding protein 43 kDa pathology. In primary mouse cortical neurons, we show that TAR DNA-binding protein 43 kDa mediated downregulation of Nitric Oxide Synthase 1 Adaptor Protein expression strongly affects the NMDA-receptor signalling pathway. In human patients, the downregulation of Nitric Oxide Synthase 1 Adaptor Protein mRNA strongly correlates with TAR DNA-binding protein 43 kDa proteinopathy as measured by cryptic Stathmin-2 and Unc-13 homolog A cryptic exon inclusion. Overall, our results demonstrate that Nitric Oxide Synthase 1 Adaptor Protein may represent a novel disease-relevant gene, potentially suitable for the development of new therapeutic strategies., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.)

TDP-43 (TAR DNA-binding protein 43) aggregation and redistribution are recognised as a hallmark of amyotrophic lateral sclerosis and frontotemporal dementia. As TDP-43 inclusions have recently been described in the muscle of inclusion body myositis patients, this highlights the need to understand the role of TDP-43 beyond the central nervous system. Using RNA-seq, we directly compare TDP-43-mediated RNA processing in muscle (C2C12) and neuronal (NSC34) mouse cells. TDP-43 displays a cell-type-characteristic behaviour targeting unique transcripts in each cell-type, which is due to characteristic expression of RNA-binding proteins, that influence TDP-43's performance and define cell-type specific splicing. Among splicing events commonly dysregulated in both cell lines, we identify some that are TDP-43-dependent also in human cells. Inclusion levels of these alternative exons are altered in tissues of patients suffering from FTLD and IBM. We therefore propose that TDP-43 dysfunction contributes to disease development either in a common or a tissue-specific manner., (© 2022. The Author(s).)

Variants of UNC13A, a critical gene for synapse function, increase the risk of amyotrophic lateral sclerosis and frontotemporal dementia 1-3 , two related neurodegenerative diseases defined by mislocalization of the RNA-binding protein TDP-43 4,5 . Here we show that TDP-43 depletion induces robust inclusion of a cryptic exon in UNC13A, resulting in nonsense-mediated decay and loss of UNC13A protein. Two common intronic UNC13A polymorphisms strongly associated with amyotrophic lateral sclerosis and frontotemporal dementia risk overlap with TDP-43 binding sites. These polymorphisms potentiate cryptic exon inclusion, both in cultured cells and in brains and spinal cords from patients with these conditions. Our findings, which demonstrate a genetic link between loss of nuclear TDP-43 function and disease, reveal the mechanism by which UNC13A variants exacerbate the effects of decreased TDP-43 function. They further provide a promising therapeutic target for TDP-43 proteinopathies., (© 2022. The Author(s).)

Tumor mass dormancy is the key intermediate step between immune surveillance and cancer progression, yet due to its transitory nature it has been difficult to capture and characterize. Little is understood of its prevalence across cancer types and of the mutational background that may favor such a state. While this balance is finely tuned internally by the equilibrium between cell proliferation and cell death, the main external factors contributing to tumor mass dormancy are immunological and angiogenic. To understand the genomic and cellular context in which tumor mass dormancy may develop, we comprehensively profiled signals of immune and angiogenic dormancy in 9,631 cancers from the Cancer Genome Atlas and linked them to tumor mutagenesis. We find evidence for immunological and angiogenic dormancy-like signals in 16.5% of bulk sequenced tumors, with a frequency of up to 33% in certain tissues. Mutations in the CASP8 and HRAS oncogenes were positively selected in dormant tumors, suggesting an evolutionary pressure for controlling cell growth/apoptosis signals. By surveying the mutational damage patterns left in the genome by known cancer risk factors, we found that aging-induced mutations were relatively depleted in these tumors, while patterns of smoking and defective base excision repair were linked with increased tumor mass dormancy. Furthermore, we identified a link between APOBEC mutagenesis and dormancy, which comes in conjunction with immune exhaustion and may partly depend on the expression of the angiogenesis regulator PLG as well as interferon and chemokine signals. Tumor mass dormancy also appeared to be impaired in hypoxic conditions in the majority of cancers. The microenvironment of dormant cancers was enriched in cytotoxic and regulatory T cells, as expected, but also in macrophages and showed a reduction in inflammatory Th17 signals. Finally, tumor mass dormancy was linked with improved patient survival outcomes. Our analysis sheds light onto the complex interplay between dormancy, exhaustion, APOBEC activity and hypoxia, and sets directions for future mechanistic explorations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Wiecek, Jacobson, Lason and Secrier.)

Neoadjuvant therapy followed by surgery is the standard of care for locally advanced esophageal adenocarcinoma (EAC). Unfortunately, response to neoadjuvant chemotherapy (NAC) is poor (20-37%), as is the overall survival benefit at five years (9%). The EAC genome is complex and heterogeneous between patients, and it is not yet understood whether specific mutational patterns may result in chemotherapy sensitivity or resistance. To identify associations between genomic events and response to NAC in EAC, a comparative genomic analysis was performed in 65 patients with extensive clinical and pathological annotation using whole-genome sequencing (WGS). We defined response using Mandard Tumor Regression Grade (TRG), with responders classified as TRG1-2 ( n = 27) and non-responders classified as TRG4-5 ( n =38). We report a higher non-synonymous mutation burden in responders (median 2.08/Mb vs. 1.70/Mb, p = 0.036) and elevated copy number variation in non-responders (282 vs. 136/patient, p < 0.001). We identified copy number variants unique to each group in our cohort, with cell cycle ( CDKN2A , CCND1 ), c-Myc ( MYC ), RTK/PIK3 ( KRAS , EGFR ) and gastrointestinal differentiation ( GATA6 ) pathway genes being specifically altered in non-responders. Of note, NAV3 mutations were exclusively present in the non-responder group with a frequency of 22%. Thus, lower mutation burden, higher chromosomal instability and specific copy number alterations are associated with resistance to NAC.

The neuromuscular junction (NMJ) is the peripheral synapse formed between a motor neuron axon terminal and a muscle fibre. NMJs are thought to be the primary site of peripheral pathology in many neuromuscular diseases, but innervation/denervation status is often assessed qualitatively with poor systematic criteria across studies, and separately from 3D morphological structure. Here, we describe the development of 'NMJ-Analyser', to comprehensively screen the morphology of NMJs and their corresponding innervation status automatically. NMJ-Analyser generates 29 biologically relevant features to quantitatively define healthy and aberrant neuromuscular synapses and applies machine learning to diagnose NMJ degeneration. We validated this framework in longitudinal analyses of wildtype mice, as well as in four different neuromuscular disease models: three for amyotrophic lateral sclerosis (ALS) and one for peripheral neuropathy. We showed that structural changes at the NMJ initially occur in the nerve terminal of mutant TDP43 and FUS ALS models. Using a machine learning algorithm, healthy and aberrant neuromuscular synapses are identified with 95% accuracy, with 88% sensitivity and 97% specificity. Our results validate NMJ-Analyser as a robust platform for systematic and structural screening of NMJs, and pave the way for transferrable, and cross-comparison and high-throughput studies in neuromuscular diseases.

Transcriptomics is a developing field with new methods of analysis being produced which may hold advantages in price, accuracy, or information output. QuantSeq is a form of 3' sequencing produced by Lexogen which aims to obtain similar gene-expression information to RNA-seq with significantly fewer reads, and therefore at a lower cost. QuantSeq is also able to provide information on differential polyadenylation. We applied both QuantSeq at low read depth and total RNA-seq to the same two sets of mouse spinal cord RNAs, each comprised by four controls and four mutants related to the neurodegenerative disease amyotrophic lateral sclerosis. We found substantial differences in which genes were found to be significantly differentially expressed by the two methods. Some of this difference likely due to the difference in number of reads between our QuantSeq and RNA-seq data. Other sources of difference can be explained by the differences in the way the two methods handle genes with different primary transcript lengths and how likely each method is to find a gene to be differentially expressed at different levels of overall gene expression. This work highlights how different methods aiming to assess expression difference can lead to different results., (Copyright © 2020 Jarvis, Birsa, Secrier, Fratta and Plagnol.)

No treatment for frontotemporal dementia (FTD), the second most common type of early-onset dementia, is available, but therapeutics are being investigated to target the 2 main proteins associated with FTD pathological subtypes: TDP-43 (FTLD-TDP) and tau (FTLD-tau). Testing potential therapies in clinical trials is hampered by our inability to distinguish between patients with FTLD-TDP and FTLD-tau. Therefore, we evaluated truncated stathmin-2 (STMN2) as a proxy of TDP-43 pathology, given the reports that TDP-43 dysfunction causes truncated STMN2 accumulation. Truncated STMN2 accumulated in human induced pluripotent stem cell-derived neurons depleted of TDP-43, but not in those with pathogenic TARDBP mutations in the absence of TDP-43 aggregation or loss of nuclear protein. In RNA-Seq analyses of human brain samples from the NYGC ALS cohort, truncated STMN2 RNA was confined to tissues and disease subtypes marked by TDP-43 inclusions. Last, we validated that truncated STMN2 RNA was elevated in the frontal cortex of a cohort of patients with FTLD-TDP but not in controls or patients with progressive supranuclear palsy, a type of FTLD-tau. Further, in patients with FTLD-TDP, we observed significant associations of truncated STMN2 RNA with phosphorylated TDP-43 levels and an earlier age of disease onset. Overall, our data uncovered truncated STMN2 as a marker for TDP-43 dysfunction in FTD.

The scientific community has avoided using tissue samples from patients that have been exposed to systemic chemotherapy to infer the genomic landscape of a given cancer. Esophageal adenocarcinoma is a heterogeneous, chemoresistant tumor for which the availability and size of pretreatment endoscopic samples are limiting. This study compares whole-genome sequencing data obtained from chemo-naive and chemo-treated samples. The quality of whole-genomic sequencing data is comparable across all samples regardless of chemotherapy status. Inclusion of samples collected post-chemotherapy increased the proportion of late-stage tumors. When comparing matched pre- and post-chemotherapy samples from 10 cases, the mutational signatures, copy number, and SNV mutational profiles reflect the expected heterogeneity in this disease. Analysis of SNVs in relation to allele-specific copy-number changes pinpoints the common ancestor to a point prior to chemotherapy. For cases in which pre- and post-chemotherapy samples do show substantial differences, the timing of the divergence is near-synchronous with endoreduplication. Comparison across a large prospective cohort (62 treatment-naive, 58 chemotherapy-treated samples) reveals no significant differences in the overall mutation rate, mutation signatures, specific recurrent point mutations, or copy-number events in respect to chemotherapy status. In conclusion, whole-genome sequencing of samples obtained following neoadjuvant chemotherapy is representative of the genomic landscape of esophageal adenocarcinoma. Excluding these samples reduces the material available for cataloging and introduces a bias toward the earlier stages of cancer., (© 2017 Noorani et al.; Published by Cold Spring Harbor Laboratory Press.)

Esophageal adenocarcinoma (EAC) is highly mutated and molecularly heterogeneous. The number of cell lines available for study is limited and their genome has been only partially characterized. The availability of an accurate annotation of their mutational landscape is crucial for accurate experimental design and correct interpretation of genotype-phenotype findings. We performed high coverage, paired end whole genome sequencing on eight EAC cell lines-ESO26, ESO51, FLO-1, JH-EsoAd1, OACM5.1 C, OACP4 C, OE33, SK-GT-4-all verified against original patient material, and one esophageal high grade dysplasia cell line, CP-D. We have made available the aligned sequence data and report single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number alterations, identified by comparison with the human reference genome and known single nucleotide polymorphisms (SNPs). We compare these putative mutations to mutations found in primary tissue EAC samples, to inform the use of these cell lines as a model of EAC.

Background: Lapatinib, a dual EGFR and HER2 inhibitor has shown disappointing results in clinical trials of metastatic oesophago-gastric adenocarcinomas (OGAs), and in vitro studies suggest that MET, IGFR, and HER3 confer resistance. This trial applied Lapatinib in the curative neoadjuvant setting and investigated the feasibility and utility of additional endoscopy and biopsy for assessment of resistance mechanisms ex vivo and in vivo., Methods: Patients with HER2 overexpressing OGA were treated for 10 days with Lapatinib monotherapy, and then in combination with three cycles of Oxaliplatin and Capecitabine before surgery. Endoscopic samples were taken for molecular analysis at: baseline including for ex vivo culture +/- Lapatinib to predict in vivo response, post-Lapatinib monotherapy and at surgery. Immunohistochemistry (IHC) and proteomic analysis was performed to assess cell kinetics and signalling activity., Results: The trial closed early (n=10) due to an anastomotic leak in two patients for which a causative effect of Lapatinib could not be excluded. The reduction in Phosphorylated-HER2 (P-HER2) and P-EGFR in the ex vivo-treated biopsy demonstrated good correlation with the in vivo response at day 10. Proteomic analysis pre and post-Lapatinib demonstrated target inhibition (P-ERBB2, P-EGFR, P-PI3K, P-AKT, and P-ERK) that persisted until surgery. There was also significant correlation between the activation of MET with the level of P-Erk (P=0.0005) and P-PI3K : T-PI3K (total PI3K) ratio (P=0.0037). There was no significant correlation between the activation status of IGFR and HER3 with downstream signalling molecules., Conclusions: Additional endoscopy and biopsy sampling for multiple biomarker endpoints was feasible and confirmed in vitro data that MET is likely to be a significant mechanism of Lapatinib resistance in vivo.

Time is of the essence in biology as in so much else. For example, monitoring disease progression or the timing of developmental defects is important for the processes of drug discovery and therapy trials. Furthermore, an understanding of the basic dynamics of biological phenomena that are often strictly time regulated (e.g. circadian rhythms) is needed to make accurate inferences about the evolution of biological processes. Recent advances in technologies have enabled us to measure timing effects more accurately and in more detail. This has driven related advances in visualization and analysis tools that try to effectively exploit this data. Beyond timeline plots, notable attempts at more involved temporal interpretation have been made in recent years, but awareness of the available resources is still limited within the scientific community. Here, we review some advances in biological visualization of time-driven processes and consider how they aid data analysis and interpretation., (© The Author 2013. Published by Oxford University Press.)

Timing common and specific modulators of disease progression is crucial for treatment, but the understanding of the underlying complex system of interactions is limited. While attempts at elucidating this experimentally have produced enormous amounts of phenotypic data, tools that are able to visualize and analyze them are scarce and the insight obtained from the data is often unsatisfactory. Linking and visualizing processes from genes to phenotypes and back, in a temporal context, remains a challenge in systems biology. We introduce PhenoTimer, a 2D/3D visualization tool for the mapping of time-resolved phenotypic links in a genetic context. It uses a novel visualization approach for relations between morphological defects, pathways or diseases, to enable fast pattern discovery and hypothesis generation. We illustrate its capabilities of tracing dynamic motifs on cell cycle datasets that explore the phenotypic order of events upon perturbations of the system, transcriptional activity programs and their connection to disease. By using this tool we are able to fine-grain regulatory programs for individual time points of the cell cycle and better understand which patterns arise when these programs fail. We also illustrate a way to identify common mechanisms of misregulation in diseases and drug abuse.

Background: Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process., Results: The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1) the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2) we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1) under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2) under enhanced DCX expression in mice we predict a normal or a higher bipolar migration., Conclusions: We present here a system-wide computational model of neuronal migration that integrates theory and data within a precise, testable framework. Our model accounts for a range of observable behaviors and affords a computational framework to study aspects of neuronal migration as a complex process that is driven by a relatively simple molecular program. Analysis of the model generated new hypotheses and yet unobserved phenomena that may guide future experimental studies. This paper thus reports a first step toward a comprehensive in-silico model of neuronal migration.