Search

While the functional effects of many recurrent cancer mutations have been characterized, the TCGA repository comprises more than 10M non-recurrent events, whose function is unknown. We propose that the context specific activity of transcription factor (TF) proteins—as measured by expression of their transcriptional targets—provides a sensitive and accurate reporter assay to assess the functional role of oncoprotein mutations. Analysis of differentially active TFs in samples harboring mutations of unknown significance—compared to established gain (GOF/hypermorph) or loss (LOF/hypomorph) of function—helped functionally characterize 577,866 individual mutational events across TCGA cohorts, including identification of mutations that are either neomorphic (gain of novel function) or phenocopy other mutations (mutational mimicry). Validation using mutation knock-in assays confirmed 15 out of 15 predicted gain and loss of function mutations and 15 of 20 predicted neomorphic mutations. This could help determine targeted therapy in patients with mutations of unknown significance in established oncoproteins.

Engineered tissues can be used to model human pathophysiology and test the efficacy and safety of drugs. Yet, to model whole-body physiology and systemic diseases, engineered tissues with preserved phenotypes need to physiologically communicate. Here we report the development and applicability of a tissue-chip system in which matured human heart, liver, bone and skin tissue niches are linked by recirculating vascular flow to allow for the recapitulation of interdependent organ functions. Each tissue is cultured in its own optimized environment and is separated from the common vascular flow by a selectively permeable endothelial barrier. The interlinked tissues maintained their molecular, structural and functional phenotypes over 4 weeks of culture, recapitulated the pharmacokinetic and pharmacodynamic profiles of doxorubicin in humans, allowed for the identification of early miRNA biomarkers of cardiotoxicity, and increased the predictive values of clinically observed miRNA responses relative to tissues cultured in isolation and to fluidically interlinked tissues in the absence of endothelial barriers. Vascularly linked and phenotypically stable matured human tissues may facilitate the clinical applicability of tissue chips.

Single-cell genomics enables dissection of tumor heterogeneity and molecular underpinnings of drug response at an unprecedented resolution1-11. However, broad clinical application of these methods remains challenging, due to several practical and preanalytical challenges that are incompatible with typical clinical care workflows, namely the need for relatively large, fresh tissue inputs. In the present study, we show that multimodal, single-nucleus (sn)RNA/T cell receptor (TCR) sequencing, spatial transcriptomics and whole-genome sequencing (WGS) are feasible from small, frozen tissues that approximate routinely collected clinical specimens (for example, core needle biopsies). Compared with data from sample-matched fresh tissue, we find a similar quality in the biological outputs of snRNA/TCR-seq data, while reducing artifactual signals and compositional biases introduced by fresh tissue processing. Profiling sequentially collected melanoma samples from a patient treated in the KEYNOTE-001 trial12, we resolved cellular, genomic, spatial and clonotype dynamics that represent molecular patterns of heterogeneous intralesional evolution during anti-programmed cell death protein 1 therapy. To demonstrate applicability to banked biospecimens of rare diseases13, we generated a single-cell atlas of uveal melanoma liver metastasis with matched WGS data. These results show that single-cell genomics from archival, clinical specimens is feasible and provides a framework for translating these methods more broadly to the clinical arena.

Respiratory failure is the leading cause of death in patients with severe SARS-CoV-2 infection1,2, but the host response at the lung tissue level is poorly understood. Here we performed single-nucleus RNA sequencing of about 116,000 nuclei from the lungs of nineteen individuals who died of COVID-19 and underwent rapid autopsy and seven control individuals. Integrated analyses identified substantial alterations in cellular composition, transcriptional cell states, and cell-to-cell interactions, thereby providing insight into the biology of lethal COVID-19. The lungs from individuals with COVID-19 were highly inflamed, with dense infiltration of aberrantly activated monocyte-derived macrophages and alveolar macrophages, but had impaired T cell responses. Monocyte/macrophage-derived interleukin-1β and epithelial cell-derived interleukin-6 were unique features of SARS-CoV-2 infection compared to other viral and bacterial causes of pneumonia. Alveolar type 2 cells adopted an inflammation-associated transient progenitor cell state and failed to undergo full transition into alveolar type 1 cells, resulting in impaired lung regeneration. Furthermore, we identified expansion of recently described CTHRC1+ pathological fibroblasts3 contributing to rapidly ensuing pulmonary fibrosis in COVID-19. Inference of protein activity and ligand–receptor interactions identified putative drug targets to disrupt deleterious circuits. This atlas enables the dissection of lethal COVID-19, may inform our understanding of long-term complications of COVID-19 survivors, and provides an important resource for therapeutic development. Lung samples collected soon after death from COVID-19 are used to provide a single-cell atlas of SARS-CoV-2 infection and the ensuing molecular changes.

Non-small cell lung cancer (NSCLC) accounts for nearly half of all newly diagnosed patients with brain metastasis (BM), followed by melanoma and breast carcinomas. The presence of BM is associated with reduced response to several modern cancer therapies and a poor prognosis, but the underlying molecular underpinnings remain poorly understood. Here, we performed multi-modal single-nucleus RNA, T cell receptor, single-cell spatial, and whole-genome sequencing (WGS) of 44 primary NSCLC tumors (PTs) and BMs. Through combination of WGS with inferred copy-number alterations (CNAs) and gene expression snRNA-seq, we robustly identify malignant cells despite the presence of healthy cell mosaicism. We find a strong association of chromosomal instability (CIN) and brain-metastatic organotropism. Through integration with clinical information and thousands of publicly available whole-exome sequencing (WES) profiles obtained from patients with NSCLC, we validate this observation and show that CIN progressively increases from PTs to extracranial metastases (ECMs) and is the highest in BMs. Using non-negative matrix factorization, we identify recurrent transcriptional hallmarks cancer metastasis, and additionally find that cancer cells from BMs strongly enrich for a neuronal-like cell state. At single-cell resolution, we indeed identify a rare cancer cell population genomically define by very high CIN, and transcriptionally characterized by a program of epithelial-to-mesenchymal transition (EMT), neuronal-like differentiation, and loss of lineage attribution. We show in our data and external scRNA-seq data that this cell state does not ecist in healthy lungs, progressively enriches from PTs to ECMs, and is most abundant in BMs, suggesting that these cells may indeed give rise to BMs. Furthermore, through integration of snRNA/TCR-seq and spatial transcriptomics, we find distinct tumor-microenvironments across disease sites, including, nearly exclusive expansion of tissue-resident myeloid cells in PTs, while BMs are largely dominated by dense infiltration with monocyte-derived macrophages and granulocytes, impaired T cell infiltration, activation and clonal expansion. Lastly, spatial transcriptomics also recurrent, cell-type specific patterns of geographic variability in key pathways, including antigen presentation, EMT, oxidative phosphorylation, and inflammatory response and associated cellular micro-niches. Together, this work identifies cellular, genomic, and transcriptional features of NSCLC BMs and has important therapeutic implications for novel therapies, in particular immunomodulatory approaches targeting cell types/states unique to disease sites. Citation Format: Somnath Tagore, Lindsay Caprio, Amit Dipak Amin, Irving Barrera, Johannes Melms, Karan Luthria, Yiping Wang, Yohanna Georgis, Abhi Jaiswal, Galina G. Lagos, Zachary Walsh, Parin Shah, Jana Biermann, Neha Sheikh, Priyanka Ramaradj, Niroshana Anandasabapathy, Hanina Hibshoosh, Gary Schwartz, Brian Henick, Alison Taylor, Fei Chen, Benjamin Izar. Multi-modal single-cell and spatial genomics reveals genomic, adaptive and microenvironmental features of human non-small cell lung cancer brain metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3517.

Non-small cell lung cancers (NSCLCs) harboring deletions or inactivating mutations in STK11 (encoding LKB1) are associated with treatment-resistance, including to immune checkpoint blockade, and poor survival, yet the underlying mechanisms are poorly understood. Here, we combined multi-modal single-cell transcriptomics, whole-genome sequencing (WGS), and analysis of public data bases totaling >10,000 NSCLC whole-exome sequencing (WES) and when available RNA (RNA-seq) profiles of AACR GENIE, MSK IMPACT, TCGA and CPTAC, high-content imaging, and functional assays, to determine genomic and mechanistic features of STK11-mutant NSCLC. Across human WES/RNA-seq data, we find that STK11-mutant NSCLC have a significantly higher fraction of genome altered (FGA) and score strongly for the mRNA-based CIN70 signature compared to other NSCLC genotypes, overall indicating that these tumors have a higher degree of chromosomal instability (CIN). Using high-content imaging, we show that both human and murine STK11-mutant models have a higher rate of micronuclei and chromosome-mis-segregation events, confirming their CINhigh status in dynamic assays. Next, we show that tonic CIN-induced activation of cGAS-STING signaling, whose activation is typically thought of inducing type I interferon expression, results in strong suppression of anti-tumor immune signaling. Remarkably, genetic or pharmacologic suppression of cGAS, and thus depletion of the STING ligand cGAMP, results in reprogramming and re-sensitization of STK11-mutant models to cGAS-STING mediated type I interferon responses. Furthermore, we show that CIN promotes excessive production cGAMP in STK11-mutant models. We find upregulation of two ectonucleotidases (ENPP1 and CD73/NT5E) that hydrolyze exported cGAMP to adenosine, which may further contribute to the highly dysfunctional tumor-microenvironment observed in STK11-mutant tumors. Lastly, suppression of CIN through overexpression of mitotic-centromere associated kinesin (MCAK) in murine STK11-mutant models results in decreased tumor growth and sensitization to anti-PD1 therapy. In summary, we define STK11-mutant as archetypical chromosomally form of NSCLC, provide mechanistic basis that corelates with poor clinical outcomes, and demonstrate how relief of tonic CIN-induced changes may be therapeutically leveraged. Citation Format: Lindsay A. Caprio, Christy Hong, Amit Dipak Amin, Somnath Tagore, Johannes Melms, Luke Cai, Yiping Wang, Patricia Ho, Michael Mu, Hanina Hibshoosh, Brian Henick, Kwok K. Wong, Samuel F. Bakhoum, Benjamin Izar. Relief of chromosomal instability-induced cGAS-STING signaling sensitizes STK11-mutant non-small cell lung cancer to immune checkpoint blockade [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5944.

SummaryThe codon usage of each genome is closely correlated with the abundance of tRNA isoacceptors. How codon-usage bias is resolved by tRNA post-transcriptional modifications is largely unknown. Here we demonstrate that the N1-methylation of guanosine at position 37 (m1G37) on the 3’-side of the anticodon, while not directly responsible for reading of codons, is a neutralizer that resolves differential decoding of proline codons. A genome-wide suppressor screen of a non-viable E. coli strain, m1G37-lacking, identifies proS suppressor mutations, indicating a coupling of methylation with tRNA prolyl-aminoacylation that sets the limit of cell viability. Using these suppressors, where prolyl-aminoacylation is decoupled from tRNA methylation, we show that m1G37 neutralizes differential translation of proline codons by the major isoacceptor. Lacking of m1G37 inactivates this neutralization and exposes the need for a minor isoacceptor for cell viability. This work has medical implications for bacterial genomes that exclusively use the major isoacceptor for survival.

Single-cell RNA-sequencing (scRNA-seq) is a powerful technology to uncover cellular heterogeneity in tumor ecosystems. Due to differences in underlying gene load, direct comparison between patient samples is challenging, and this is further complicated by the sparsity of data matrices in scRNA-seq. Here, we present a factorization method called KINOMO (Kernel dIfferentiability correlation-based NOn-negative Matrix factorization algorithm using Kullback-Leibler divergence loss Optimization). This tool uses quadratic approximation approach for error correction and an iterative multiplicative approach, which improves the quality assessment of NMF-identified factorization, while mitigating biases introduced by inter-patient genomic variability. We benchmarked this new approach against nine different methods across 15 scRNA-seq experiments and find that KINOMO outperforms prior methods when evaluated with an adjusted Rand index (ARI), ranging 0.82-0.91 compared to 0.68-0.77. Thus, KINOMO provides an improved approach for determining coherent transcriptional programs (and meta-programs) from scRNA-seq data of cancer tissues, enabling comparison of patients with variable genomic backgrounds.ClassificationPhysical Sciences (Applied Mathematics; Biophysics and Computational Biology), Biological Sciences (Applied Biological Sciences; Biophysics and Computational Biology; Medical Sciences; Systems Biology.).Significance StatementIdentification of shared or distinct cell programs in single-cell RNA-seq data of patient cancer cells is challenging due to underlying variability of gene load which determines transcriptional output. We developed an analytical approach to define transcriptional variability more accurately across patients and therefore enable comparison of program expression despite inherent genetic heterogeneity. Thus, this method overcomes challenges not adequately addressed by other methods broadly used for the analysis of single-cell genomics data.

SummaryAs a result of current advances in the analysis of patient sequencing data, many tumors have been characterized in a personalized manner. Such data can also be used to characterize genes that act as either oncogenes or tumor suppressors. These include “defective” tumor suppressor genes which may function as driver oncogenes that play a key role in cancer proliferation due to various genetic alterations, specifically, chromosomal translocations. In this study, we considered protein networks, mutations, methylation data and cancer fusions to classify tumor suppressors that may convert into oncogenes. Moreover, we developed a novel network-based parameter called the ‘preferential attachment score’ to categorize genes as oncogenes and/or tumor suppressors. Such classification was achieved using a naïve Bayes computation approach. We used an ABC-MCMC method for selecting features for training our classification algorithm. We then performed a survey of tumor suppressors and oncogenes from the perspective of somatic mutations and network properties for 691 TCGA cases. For comparative purposes, we chose currently well-established methods, such as MutSigCV, OncodriveCLUST, Oncodrive-FM, 20/20+, ActiveDriver, MuSiC, TUSON, OncodriveFML, and found that our algorithm outperformed these other tolls, with 93.3% efficiency. Based on 691 TCGA cohorts, we found that tumor suppressors presented the highest mutation frequency in most tumor types, relative to oncogenes. Using protein-protein interaction data, we found that essential proteins, tumor suppressors and oncogenes had higher degrees of connectivity and betweenness centrality, relative to normal proteins. Similarly, tumor suppressors and oncogenes had lower clustering coefficients, as well as shortest path distances (FDR < 0.05). Finally, most mutated tumor suppressors integrate hyper-methylated partners in the protein interaction networks of 3091 fusions, following the patterns of oncogenes (43%). Thus, these results further characterize cancer oncogenes and tumor suppressors in the context of deep analysis of cancer network alterations.AvailabilitySource scripts are available at https://github.com/somnathtagore/NBC and the resource is available at http://ontum.md.biu.ac.il/index.html

Protein fusions produced by the “slippage” of two genes or by chromosomal translocations are essential diagnostic biomarkers of cancer. Fusions produce novel protein-protein interactions, which eliminate other interactions by changing protein domains in such fusions. The impact of these changes disseminates along protein-protein interaction networks, thereby altering cancer-promoting activity and creating cancer phenotypes. Currently, for most patients, the determination of appropriate drugs is totally empirical. As such, a personalized therapy approach based on unique patient genomic markers is needed. In this study, we considered 672 aliquot IDs containing 3091 fusions from The Cancer Genome Atlas (TCGA), accounting for 25 cancer sub-types assigned as leukemias, lymphomas, sarcomas, melanoma, glioblastoma and carcinomas. Protein-protein interaction maps showed distinct patterns according to cancer sub-type, reflected as different phenotypic traits. We induced site-directed percolations, i.e., critical transitions, by selective knockouts of genes encoding proteins in a given interaction network so as to identify breakdown points. The number of genes that needed to be knocked out in a site-directed manner before inducing a breakdown ranged from 1-7 based on whether the fusion protein was part of a network hub. Quantitatively, in leukemia, lymphoma, melanoma and glioblastoma, breakdown was achieved in 218 fusion networks when only higher degree hubs were percolated, and in 280 networks when a combination of higher and lower degree hubs was targeted, with an FDR < 0.05. These were subsequently addressed in survival studies. We found that patient survival may be improved by considering ‘breakdown points’ as drug targets.

In contrast to fossorial and above-ground organisms, subterranean species have adapted to the extreme stresses of living underground. We analyzed the predicted protein–protein interactions (PPIs) of all gene products, including those of stress-response genes, among nine subterranean, ten fossorial, and 13 aboveground species. We considered 10,314 unique orthologous protein families and constructed 5,879,879 PPIs in all organisms using ChiPPI. We found strong association between PPI network modulation and adaptation to specific habitats, noting that mutations in genes and changes in protein sequences were not linked directly with niche adaptation in the organisms sampled. Thus, orthologous hypoxia, heat-shock, and circadian clock proteins were found to cluster according to habitat, based on PPIs rather than on sequence similarities. Curiously, "ordered" domains were preserved in aboveground species, while "disordered" domains were conserved in subterranean organisms, and confirmed for proteins in DistProt database. Furthermore, proteins with disordered regions were found to adopt significantly less optimal codon usage in subterranean species than in fossorial and above-ground species. These findings reveal design principles of protein networks by means of alterations in protein domains, thus providing insight into deep mechanisms of evolutionary adaptation, generally, and particularly of species to underground living and other confined habitats.

Single-cell genomics are enabling technologies, but their broad clinical application remains challenging. We report an easily adaptable approach for single-cell transcriptome and T cell receptor (TCR)-sequencing, and matched whole-genome sequencing from tiny, frozen clinical specimens. We achieve similar quality and biological outputs while reducing artifactual signals compared to data from matched fresh tissue samples. Profiling sequentially collected melanoma samples from the KEYNOTE-001 trial, we resolve cellular, genomic, and clonotype dynamics that encapsulate molecular patterns of tumor evolution during anti-PD-1 therapy. To demonstrate applicability to banked biospecimens of rare diseases, we generate a large uveal melanoma liver metastasis single-cell and matched WGS atlas, which revealed niche-specific impairment of clonal T cell expansion. This study provides a foundational framework for propelling single-cell genomics to the clinical arena.

Despite extensive efforts, reproducible assessment of pancreatic ductal adenocarcinoma (PDA) heterogeneity and plasticity at the single cell level remains elusive. Systematic, network-based analysis of single cell RNA-seq profiles showed that most PDA tumors comprise three coexisting lineages whose aberrant transcriptional state is mechanistically controlled by distinct regulatory programs. These lineages were characterized by the aberrant activation of either gastrointestinal lineage markers (GLS), transcriptional effectors of morphogen pathways (MOS) and acinar to ductal metaplasia markers (ALS). Each lineage was characterized by cells in two different cell states determined by the differential activation of MEK signaling (M+/M-) and high cellular plasticity. These states were confirmed in multiple cohorts, cell lines, PDX models and harmonized with bulk profile analyses. Master regulators (MRs) of GLS and MOS state were predictive of patient’s survival in bulk profiles. Cross-state plasticity was confirmed by lineage tracing assays, while pooled CRISPR/Cas9 assays confirmed the essentiality of identified MR proteins. Finally, mechanistic MR-mediated cell state control was confirmed by MR expression-mediated reprogramming of MOS cells to a GLS state. Our work provided a mechanistic model of pancreatic cancer heterogeneity and testable hypothesis to target cell state-specific pancreatic cancer dependencies. Citation Format: Pasquale Laise, Mikko Turunen, Hans Carlo Maurer, Alvaro Curiel Garcia, Ela Elyada, Bernhard Schmierer, Lorenzo Tomassoni, Jeremy Worley, Mariano J. Alvarez, Jordan Kesner, Xiangtian Tan, Somnath Tagore, Ester Calvo Fernandez, Kelly Wong, Alexander L. E. Wang, Sabrina Ge, Alina C. Iuga, Aaron T. Griffin, Winston Wong, Gulam A. Manji, Faiyaz Notta, David A. Tuveson, Kenneth P. P. Olive, Andrea Califano. Pancreatic cancer comprises co-existing transcriptional states regulated by distinct master regulator programs [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A007.

Melanoma brain metastasis (MBM) frequently occurs in patients with advanced melanoma, yet our understanding of the underlying salient biology is rudimentary. Here, we performed single-cell/nucleus RNA-seq in 22 treatment-naïve MBM and 10 extracranial melanoma metastases (ECM), and matched spatial single-cell transcriptomics and T cell receptor (TCR)-seq. Cancer cells from MBM were more chromosomally unstable, adopted a neuronal-like cell state, and enriched for spatially variably expressed metabolic pathways. Key observations were validated in independent patient cohorts, patient-derived MBM/ECM xenograft models, RNA/ATAC-seq, proteomics, and multiplexed imaging. Integrated spatial analyses revealed distinct geography of putative cancer immune evasion, and evidence for more abundant intra-tumoral B to plasma cell differentiation in lymphoid aggregates in MBM. MBM harbored larger fractions of monocyte-derived macrophages and dysfunctional TOX(+)CD8(+) T cells with distinct expression of immune checkpoints. This work provides comprehensive insights into MBM biology and serves as a foundational resource for further discovery and therapeutic exploration.

Brain metastases are the most frequent malignancies in the brain and are associated with significant morbidity and mortality. Melanoma brain metastases (MBM) occur in most patients with advanced melanoma and are challenging to treat. Our understanding of the treatment-naïve landscape of MBM is still rudimentary, and there are no site-specific molecular therapies available. To gain comprehensive insights into the niche-specific biology of MBM, we performed multi-modal profiling of fresh and frozen samples using single-cell RNA-seq, single-cell TCR-seq, single-nuclei RNA-seq, and spatial transcriptional profiling. We evolved single-nucleus RNA-seq processing methods to enable profiling of minute amounts of archival, frozen specimens and compared data quality and structure between matched fresh and frozen MBM. We curated a treatment-naïve single-transcriptome atlas of MBM, collected either fresh samples over 1 year or profiled frozen samples dating back more than 15 years, and compared these samples to extracranial melanoma metastases (ECMM). In total, we profiled 25 samples with more than 114,000 transcriptomes. We identified more than 20 different cell types, including diverse tumor-infiltrating T-cell subsets and rare dendritic cell types, and tissue-specific cell types, such as activated microglia. Tumor cells in MBM showed an increase in copy number alterations (CNAs) compared to ECMM, which we validated using an external dataset of whole exome sequencing (WES) data including both MBM and ECMM. MBM-derived tumor cells show enrichment of genes involved in neuronal development and function, and site-specific metabolic programs (e.g., oxidative phosphorylation). Comparison with an external bulk RNA-seq dataset validated enriched key genes in MBM and ECMM as putative dependencies. We recovered cell-cell interactions between tumor and brain-resident cells involved in brain development, homeostasis, and disease. Similar to ECMM, the tumor microenvironment of MBM contained CD8+ T cells across a spectrum of differentiation, exhaustion and expansion, which was associated with loss of TCF7 expression and adoption of a TOX+ cell state. CD4+ T cells included T regulatory, T helper and T follicular-helper-like expression profiles. Plasma cells showed spatially localized expansion and limited heterogeneity. Myeloid cells largely adopted pro-tumorigenic cell states, including microglia, the brain-resident myeloid cells, which showed an activation trajectory characterized by expression of SPP1 (osteopontin). Spatial transcriptional analysis revealed restricted expression of antigen presentation genes with only a subset of these locations showing a type I interferon response. In summary, this work presents a multi-modal single-cell approach to dissect and compare the landscape of treatment-naïve MBM and ECMM. Citation Format: Johannes C. Melms, Jana Biermann, Amit Dipak Amin, Yiping Wang, Somnath Tagore, Massimo Andreatta, Ajay Nair, Meri Rogava, Patricia Ho, Lindsay A. Caprio, Zachary H. Walsh, Shivem Shah, Daniel H. Vacarro, Blake Caldwell, Adrienne M. Luoma, Joseph Driver, Matthew Ingham, Suthee Rapisuwon, Jennifer Wargo, Craig L. Slinguff, Evan Z. Macosco, Fei Chen, Richard Carvajal, Michael B. Atkins, Michael A. Davies, Elham Azizi, Santiago J. Carmona, Hanina Hibshoosh, Peter D. Canoll, Jeffrey N. Bruce, Wenya L. Bi, Gary K. Schwartz, Benjamin Izar. Dissecting the ecosystem of treatment-naïve melanoma brain metastasis using multi-modal single-cell analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 984.

Despite many clinical trials, outcome for osteosarcoma (OS) patients remains poor, especially for those with metastatic or relapsed/refractory disease. We leveraged network-based systems biology approaches to discover Master Regulator (MR) proteins representing pharmacologically accessible, mechanistic determinants of OS cell state, and to dissect tumor transcriptional heterogeneity. MRs were identified by interrogating an OS regulatory network—generated from 87 RNAseq profiles in the TARGET OS cohort—with gene expression signatures from 149 diagnostic OS samples, using the VIPER algorithm. RNAseq profiles were generated in four sarcoma cell lines following perturbation with ~400 oncology drugs, and used to identify drugs capable of inverting MR activity profiles with the OncoTreat algorithm. Unsupervised, protein activity-based clustering of the samples identified two clusters, characterized by a significant overall survival difference (76% vs 38%, log-rank p-val 0.0041). Patients in the high-risk cluster presented aberrant activity of proteins involved in cell cycle (FOXM1, CENPF, TOP2A) and epigenetic remodeling (EZH2, KDM1A), while those in the low-risk cluster had aberrant activity of proteins involved in immune response (VAV1, CD86, CEBPE), interferon gama signaling (IRF5, MNDA), and senescence control (CREG1, TFEC). We further identified patients with metastatic disease and particularly poor outcome ( Citation Format: Somnath Tagore, Jovana Pavisic, Aaron T. Griffin, Katherine Janeway, Andrew L. Kung, Filemon Dela Cruz, Alejandro Sweet-Cordero, Inge Behroozfard, Stanley Leung, Alex Lee, Darrell Yamashiro, Julia Glade Bender, Andrea Califano. A systems biology approach to defining tumor heterogeneity and prognostic and targetable master regulator protein signatures from bulk and single-cell RNAseq in osteosarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 488.

e23518 Background: Single-cell RNA-seq is an enabling technology that may inform the molecular underpinnings of drug response and resistance in patient biopsies. This method is difficult to implement in the study of rare diseases such as sarcomas due to specimen requirements and technical limitations. Methods: Here, we evolved novel methods that we recently reported in melanoma ( Wang, Fan, et al., bioRxiv, 2022), which enable single-nucleus RNA, T cell receptor (snRNA/TCR)-seq, and pool-matched whole-genome sequencing (WGS) from archival, frozen sarcoma tissue. Results: This enabled profiling of 75,716 cells and 788 matched TCR clonotypes from six patients with intimal sarcoma (INS) and undifferentiated pleomorphic sarcomas (UPS), including two matched pair samples from pre/post-immune checkpoint inhibitor (ICI). Our analysis revealed substantial transcriptional cancer cell heterogeneity driven by varying copy number alterations (CNAs). In one patient with INS with a complete response to ICI followed by an isolated recurrence, we identified a rare cancer cell clone defined by CNA (confirmed with WGS) and resulting transcriptional outputs that pre-existed and emerged during resistance. Furthermore, in a patient with UPS with intrinsic resistance to ICI, we find adequate T cell clonal expansion and activation, suggesting appropriate T cell response to ICI dampened by intrinsic mechanisms of ICI resistance within the cancer cells. Non-negative matrix factorization (NMF) analysis identified cell states associated with either intrinsic or adaptive resistance to ICI that was distinct from resistance to doxorubicin. These observations are consistent with those previously reported from sequential biopsies obtained from KEYNOTE-001 in metastatic melanoma ( Wang, Fan, et al., bioRxiv, 2022), which also revealed emergence of pre-existing populations of resistant clones defined by their underlying aneuploidy patterns. Conclusions: Together, these results demonstrate feasibility of implementing single-cell genomics from archival tissue to study sarcoma and propel our understanding of drug resistance. Conceptually, this work suggests that large-scale CNAs may drive cell sub-populations associated with ICI resistance in sarcoma and in other diseases.

Tumor-specific elucidation of physical and functional oncoprotein interactions could improve tumorigenic mechanism characterization and therapeutic response prediction. Current interaction models and pathways, however, lack context specificity and are not oncoprotein specific. We introduce SigMaps as context-specific networks, comprising modulators, effectors and cognate binding-partners of a specific oncoprotein. SigMaps are reconstructed de novo by integrating diverse evidence sources—including protein structure, gene expression and mutational profiles—via the OncoSig machine learning framework. We first generated a KRAS-specific SigMap for lung adenocarcinoma, which recapitulated published KRAS biology, identified novel synthetic lethal proteins that were experimentally validated in three-dimensional spheroid models and established uncharacterized crosstalk with RAB/RHO. To show that OncoSig is generalizable, we first inferred SigMaps for the ten most mutated human oncoproteins and then for the full repertoire of 715 proteins in the COSMIC Cancer Gene Census. Taken together, these SigMaps show that the cell’s regulatory and signaling architecture is highly tissue specific. Interaction networks for any protein can be generated using a machine learning algorithm.

Respiratory failure is the leading cause of death in patients with severe SARS-CoV-2 infection

Despite extensive efforts to characterize the transcriptional landscape of pancreatic ductal adenocarcinoma (PDA), reproducible assessment of subtypes with actionable dependencies remains challenging. Systematic, network-based analysis of regulatory protein activity stratified PDA tumours into novel functional subtypes that were highly conserved across multiple cohorts, including at the single cell level and in laser capture microdissected (LCM) samples. Identified subtypes were characterized by activation of master regulator proteins representing either gastrointestinal lineage markers or transcriptional effectors of morphogen pathways. Single cell analysis confirmed the existence of Lineage and Morphogenic states but also revealed a dominant population of more differentiated Oncogenic Precursor (OP) cells , present in all sampled patients, yet not apparent from bulk tumor analysis. Master regulators were validated by pooled, CRISPR/Cas9 screens, demonstrating both subtype-specific and universal dependencies. Conversely, ectopic expression of Lineage MRs, such as OVOL2, was sufficient to reprogram Morphogenic cells, thus providing a roadmap for the future targeting of patient-specific dependencies in PDA.

Although annotation of genomic mutations is a highly relevant and complex segment of the analysis of sequence-based genomic analyses, currently more than ten million variants lack functional annotation. While computational predictions of variant function are usually integrated into gene-based analyses of rare-variants, there is limited information for assessing variant function in the context of a particular disease. The goal of this study is to assess the mechanistic characterization of context-dependent hyper-, hypo-, and neomorph mutations in cancers. We developed a computational approach to characterize these mutations and assess the functional effects of mutations occurring in the same or different protein domains, and differentiate mutations in terms of their hyper-morph (gain-of-function), hypo-morph (loss-of-function), neutral, or neo-morph (establishing a novel function) potential. To elucidate the functional consequence of such mutations, we based our computational pipeline on inferring the activity of regulatory proteins such as transcription factors (TFs) and co-factors (co-TFs) using the VIPER algorithm. Our analytical pipeline integrates structural and functional information encompassing six topics : 1) structural domains affected by the mutation, 2) the overlap between mutation-specific TF/co-TFs, 3) differential activity signatures and signatures induced by established hyper-morph, hypo-morph and neutral or neo-morph mutations, 4) in vitro data generated by reporter assays, 5) the VIPER-inferred activity of each protein relative to a validated control, and 6) the fraction of proteins in a sample that are not affected by established hyper-morphs and hypo-morphs (candidate neo-morphs).The current repertoire considers 25 TCGA cohorts for 3830 proteins (oncoproteins/tumor suppressors). As an example, we validated our method on the TCGA Breast Cancer dataset (TCGA-BRCA) by predicting with very high confidence several neo-morphic phenotypes, including the previously-described PIK3CAE545, PIK3CAE542, PIK3CAH1047, PIK3CAQ546K and PIK3CAG1049R. Interestingly, PIK3CAE545K, classified previously as a gain-of-function mutation (in one TCGA-BRCA sample) or a loss-of-function mutation (in two other TCGA-BRCA samples), is predicted to be a neo-morph based on our approach. Further validation of these mutations using PIK3CA reporter assays led to the identification of several significant hypo-morphic signals in TP53 mutant samples. We defined this phenomenon as mutational mimicry (i.e. mutations in proteins mimicking those in established oncogenes) and we propose it as a tool for predicting tumor sensitivity/resistance to drugs. Citation Format: Somnath Tagore, Andrea Califano. A comprehensive characterization of hyper-morph, hypo-morph, and neo-morph mutations in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 245.

Fusion proteins, comprising peptides deriving from the translation of two parental genes, are produced in cancer by chromosomal aberrations. The expressed fusion protein incorporates domains of both parental proteins. Using a methodology that treats discrete protein domains as binding sites for specific domains of interacting proteins, we have cataloged the protein interaction networks for 11 528 cancer fusions (ChiTaRS-3.1). Here, we present our novel method, chimeric protein-protein interactions (ChiPPI) that uses the domain-domain co-occurrence scores in order to identify preserved interactors of chimeric proteins. Mapping the influence of fusion proteins on cell metabolism and pathways reveals that ChiPPI networks often lose tumor suppressor proteins and gain oncoproteins. Furthermore, fusions often induce novel connections between non-interactors skewing interaction networks and signaling pathways. We compared fusion protein PPI networks in leukemia/lymphoma, sarcoma and solid tumors finding distinct enrichment patterns for each disease type. While certain pathways are enriched in all three diseases (Wnt, Notch and TGF β), there are distinct patterns for leukemia (EGFR signaling, DNA replication and CCKR signaling), for sarcoma (p53 pathway and CCKR signaling) and solid tumors (FGFR and EGFR signaling). Thus, the ChiPPI method represents a comprehensive tool for studying the anomaly of skewed cellular networks produced by fusion proteins in cancer. This work is funded by the Project Retos BFU2015- 71241-R of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC), co-funded by European Re-gional Development Fund (ERDF) and by the ProjectPT13/0001/0030, Instituto de Salud Carlos III (ISCIII),Strategic Action in Health, co-funded by European Re-gional Development Fund (ERDF). The work of MFM issupported by the Israel Cancer Association (ICA) fund, the work of ST is supported by the VaTaT Postdoctoral Fel-lowship for excellent students [22351, 20027, 26912]. AV issupported by the Joint BSC-CRG-IRB Programme in Com- Downloaded from https://academic.oup.com/nar/article-abstract/45/12/7094/3854948 by Spanish National Cancer Research Center user on 04 February 2019 putational Biology. Funding for open access charge: ICA[e-cancer-diagnosis].Conflict of interest statement.None declared. Sí

Despite considerable efforts, the mechanisms linking genomic alterations to the transcriptional identity of cancer cells remain elusive. Integrative genomic analysis, using a network-based approach, identified 407 master regulator (MR) proteins responsible for canalizing the genetics of individual samples from 20 cohorts in The Cancer Genome Atlas (TCGA) into 112 transcriptionally distinct tumor subtypes. MR proteins could be further organized into 24 pan-cancer, master regulator block modules (MRBs), each regulating key cancer hallmarks and predictive of patient outcome in multiple cohorts. Of all somatic alterations detected in each individual sample, >50% were predicted to induce aberrant MR activity, yielding insight into mechanisms linking tumor genetics and transcriptional identity and establishing non-oncogene dependencies. Genetic and pharmacological validation assays confirmed the predicted effect of upstream mutations and MR activity on downstream cellular identity and phenotype. Thus, co-analysis of mutational and gene expression profiles identified elusive subtypes and provided testable hypothesis for mechanisms mediating the effect of genetic alterations.

The significance of metabolic pathway prediction is to envision the viable unknown transformations that can occur provided the appropriate enzymes are present. It can facilitate the prediction of the consequences of host-pathogen interactions. In this article, we have proposed a new algorithm Architectural Similarity-based Automated Pathway Prediction (ASAPP) to predict metabolic pathways based on the structural similarity among the metabolites. ASAPP takes two-dimensional structure and molecular weight of metabolites as input, and generates a list of probable transformations without the knowledge of any externally established reactions, with an accuracy of 85.09 percent. ASAPP has also been applied to predict the outcome of pathogen liberated toxins on the carbohydrate and lipid pathways of the hosts. We have analyzed the disruption of host pathways in the presence of toxins, and have found that some metabolites in Glycolysis and the TCA cycle have a high chance of being the breakpoints in the pathway. The tool is available at http://asapp.droppages.com/ .

Today’s increase in scientific literature requires the efficient methods of data mining for improving the extraction of the useful information from texts. In this manuscript, we used a data and text mining method to identify fusions and their protein-protein interactions from published biomedical text. The extracted fusion proteins and their protein-protein interactions are used as a training set for a Naïve Bayes classifier that is further used for final identification of testing dataset, consisting of 1817 fusions. Our method has a literature corpus, text and annotation mappers; keywords, rule bases, negative tokens, and pattern extractor; synonym tagger, normalization, regular expression mapper; and Naïve Bayes classifier. We classified 1817 unique fusion proteins and their corresponding 2908 protein-protein interactions for 18 cancer types. Therefore, it can be used for screening literature for identifying mentions unique cases of fusions that can be further used for downstream analysis. It is available athttp://protfus.md.biu.ac.il/.

Animals living at high altitudes have evolved distinct phenotypic and genotypic adaptations against stressful environments. We studied the adaptive patterns of altitudinal stresses on transcriptome turnover in subterranean plateau zokors (Myospalax baileyi) in the high-altitude Qinghai-Tibetan Plateau. Transcriptomes of zokors from three populations with distinct altitudes and ecologies (Low: 2846 m, Middle: 3282 m, High: 3,714 m) were sequenced and compared. Phylogenetic and principal component analyses classified them into three divergent altitudinal population clusters. Genetic polymorphisms showed that the population at H, approaching the uppermost species boundary, harbors the highest genetic polymorphism. Moreover, 1056 highly up-regulated UniGenes were identified from M to H. Gene ontologies reveal genes like EPAS1 and COX1 were overexpressed under hypoxia conditions. EPAS1, EGLN1, and COX1 were convergent in high-altitude adaptation against stresses in other species. The fixation indices (F ST and G ST )-based outlier analysis identified 191 and 211 genes, highly differentiated among L, M, and H. We observed adaptive transcriptome changes in Myospalax baileyi, across a few hundred meters, near the uppermost species boundary, regardless of their relatively stable underground burrows’ microclimate. The highly variant genes identified in Myospalax were involved in hypoxia tolerance, hypercapnia tolerance, ATP-pathway energetics, and temperature changes.

Background Effector proteins of bacteria infect their hosts by specific dedicated machinery identified as secretion systems. Currently, no mechanism to identify the effector proteins based on their 3D structure has been reported in the literature. In order to identify effector proteins, extraction of features from their 3D structure is crucial. However, effector protein datasets are highly imbalanced. State-of-the-art oversampling algorithms are incapable of dealing with such datasets. They usually eliminate samples as noise. They do not ensure generation of synthetic samples strictly in the vicinity of the minority class samples. In effector protein datasets, deletion of any samples as noise would lead to loss of crucial information. Furthermore, generation of synthetic samples of the minority class in the vicinity of majority class samples would lead to an inept classifier. Method In this paper, we introduce an algorithm called Cluster Quality based Non-Reductional (CQNR) oversampling technique. Its novelty lies in generating new samples proportional to the distribution of samples of the minority classes, without eliminating any sample as noise. Utilizing CQNR, we develop a novel Effector Protein Predictor based on the 3D (EPP3D) structure of proteins. EPP3D is trained on a feature set, balanced by CQNR, comprising 3D structure-based features, namely, convex hull layer count, surface atom composition, radius of gyration, packing density and compactness, derived from the 3D structure of the experimentally verified effector proteins. Result Fscore and Gmean demonstrate that CQNR has outperformed some well-established oversampling methods by approximately 3–5%, with respect to classification accuracy, on five benchmark datasets and three other highly imbalanced synthetically generated datasets. Likewise, for classification of pathogenic effector proteins, a significant improvement of 7–9% in accuracy has been noticed, on the application of CQNR followed by EPP3D. Moreover, EPP3D has exhibited an improvement of 2–4% in classifying effector proteins based on their 3D structure compared to the classification of effector proteins based on their amino acid sequences. The software for CQNR and EPP3D are available at http://projectphd.droppages.com/CQNR.html .

Tailored therapy aims to cure cancer patients effectively and safely, based on the complex interactions between patients' genomic features, disease pathology and drug metabolism. Thus, the continual increase in scientific literature drives the need for efficient methods of data mining to improve the extraction of useful information from texts based on patients' genomic features. An important application of text mining to tailored therapy in cancer encompasses the use of mutations and cancer fusion genes as moieties that change patients' cellular networks to develop cancer, and also affect drug metabolism. Fusion proteins, which are derived from the slippage of two parental genes, are produced in cancer by chromosomal aberrations and trans-splicing. Given that the two parental proteins for predicted fusion proteins are known, we used our previously developed method for identifying chimeric protein–protein interactions (ChiPPIs) associated with the fusion proteins. Here, we present a validation approach that receives fusion proteins of interest, predicts their cellular network alterations by ChiPPI and validates them by our new method, ProtFus, using an online literature search. This process resulted in a set of 358 fusion proteins and their corresponding protein interactions, as a training set for a Naïve Bayes classifier, to identify predicted fusion proteins that have reliable evidence in the literature and that were confirmed experimentally. Next, for a test group of 1817 fusion proteins, we were able to identify from the literature 2908 PPIs in total, across 18 cancer types. The described method, ProtFus, can be used for screening the literature to identify unique cases of fusion proteins and their PPIs, as means of studying alterations of protein networks in cancers. Availability: http://protfus.md.biu.ac.il/, Author summary Tailored therapy aims to cure cancer patients in a fully personal way. Thus, the continual increase in scientific information and, particularly, in published literature, drives the need for efficient methods of data mining to find unique personal genomic features and their connections. Fusion proteins, which are derived from the slippage of two parental genes or chromosomal translocations, are frequently drivers of cancers. We used our previously developed method for identifying chimeric protein–protein interactions (ChiPPIs) for multiple fusion proteins. In this paper, we present a validation approach, ProtFus, which receives fusion proteins of interest, predicts their cellular network alterations by ChiPPI and validates them by online literature searches. This process resulted in a set of 358 fusion proteins and their corresponding protein interactions, as training set. Next, for a test set of 1817 fusion proteins, we were able to identify 2908 previously published PPIs across 18 different cancer types. The described method can be used for screening the literature to identify unique cases of fusion proteins and their PPI networks, as means of studying alterations of protein networks for personalized approaches in cancers.

Discovery of chimeric RNAs, which are produced by chromosomal translocations as well as the joining of exons from different genes by trans-splicing, has added a new level of complexity to our study and understanding of the transcriptome. The enhanced ChiTaRS-3.1 database (http://chitars.md.biu.ac.il) is designed to make widely accessible a wealth of mined data on chimeric RNAs, with easy-to-use analytical tools built-in. The database comprises 34 922: chimeric transcripts along with 11 714: cancer breakpoints. In this latest version, we have included multiple cross-references to GeneCards, iHop, PubMed, NCBI, Ensembl, OMIM, RefSeq and the Mitelman collection for every entry in the 'Full Collection'. In addition, for every chimera, we have added a predicted Chimeric Protein-Protein Interaction (ChiPPI) network, which allows for easy visualization of protein partners of both parental and fusion proteins for all human chimeras. The database contains a comprehensive annotation for 34 922: chimeric transcripts from eight organisms, and includes the manual annotation of 200 sense-antiSense (SaS) chimeras. The current improvements in the content and functionality to the ChiTaRS database make it a central resource for the study of chimeric transcripts and fusion proteins.

In-silico metabolic engineering is a very useful branch of systems biology for modeling, analysis and prediction of various outcomes of metabolic pathways. It can also be used for detecting interactions and dynamics within a network. Various protocols have been proposed for modeling a pathway. But most of these protocols have various disadvantages and shortcomings with respect to automated pathway modeling and analysis. In the present article, we have proposed a novel algorithm for automated pathway reconstruction. We have also made a comparative study of our algorithm with other standard protocols and discussed its advantages over others. We present StructurAl Grammar-based automated PAthway Reconstruction (SAGPAR), a fast and robust algorithm that generates any metabolic pathway using some given structural representations of metabolites. Users can model any pathway based on some pre-required features that are asked as an input by the algorithm. The algorithm also takes into considerations various thermodynamic thresholds and structural properties while modeling a pathway. The given algorithm has been tested on the standard pathway datasets of 25 pathways of Mycoplasma pneumoniae M129 and 24 pathways of Homo sapiens. The dataset is taken from KEGG and PubChem Compound data repositories. SAGPAR performs much better than some already present metabolic pathway analysis tools like Copasi, PHT, Gepasi, Jarnac and Path-A.

Background: A complex network of biochemical reactions present in an organism generates various biological moieties necessary for its survival. It is seen that biological systems are robust to genetic and environmental changes at all levels of organization. Functions of various organisms are sustained against mutational changes by using alternative pathways. It is also seen that if any one of the paths for production of the same metabolite is hampered, an alternate path tries to overcome this defect and helps in combating the damage. Methodology: Certain physical, chemical or genetic change in any of the precursor substrate of a biochemical reaction may damage the production of the ultimate product. We employ a quantitative approach for simulating this phenomena of causing a physical change in the biochemical reactions by performing external perturbations to 12 metabolic pathways under carbohydrate metabolism in Saccharomyces cerevisae as well as 14 metabolic pathways under carbohydrate metabolism in Homo sapiens. Here, we investigate the relationship between structure and degree of compatibility of metabolites against external perturbations, i.e., robustness. Robustness can also be further used to identify the extent to which a metabolic pathway can resist a mutation event. Biological networks with a certain connectivity distribution may be very resilient to a particular attack but not to another. The goal of this work is to determine the exact boundary of network breakdown due to both random and targeted attack, thereby analyzing its robustness. We also find that compared to various non-standard models, metabolic networks are exceptionally robust. Here, we report the use of a 'Resilience-based' score for enumerating the concept of 'network-breakdown'. We also use this approach for analyzing metabolite essentiality providing insight into cellular robustness that can be further used for future drug development. Results: We have investigated the behavior of metabolic pathways under carbohydrate metabolism in S. cerevisae and H. sapiens against random and targeted attack. Both random as well as targeted resilience were calculated by formulating a measure, that we termed as 'Resilience score'. Datasets of metabolites were collected for 12 metabolic pathways belonging to carbohydrate metabolism in S. cerevisae and 14 metabolic pathways belonging to carbohydrate metabolism in H. sapiens from Kyoto Encyclopedia for Genes and Genomes (KEGG).

Pathway analysis is one of the most interesting aspects of Systems Biology. Modeling biological pathways is interesting as well as difficult to optimize. Various modeling problems of diseases can be successfully analyzed using this simulation approach. Graphical probabilistic approaches are one of the unique methodologies that are used for designing and analyzing pathways. We have discussed the various graphical approaches that are actively involved in pathway modeling.

Toxicogenomics is defined as an integration of genomics (transcriptomics, proteomics and metabolomics) and toxicology. It is a scientific field that studies how the genome is involved in responses to environmental stressors and toxicants. It combines studies of mRNA expression, cell and tissue-wide protein expression and metabonomics to understand the role of gene-environment interactions in disease. One of the important aspects of toxicogenomics research is the development and application of bioinformatics tools and databases in order to facilitate the analysis, mining, visualizing and sharing of the vast amount of biological information being generated in this field. This rapidly growing area promises to have a large impact on many other scientific and medical disciplines as scientists could now generate complete descriptions of how components of biological systems work together in response to various stresses, drugs, or toxicants.

'Epigenomics' can be termed as the study of the effects of chromatin structure, including the higher order of chromatin folding and attachment to the nuclear matrix, packaging of DNA around nucleosomes, covalent modifications of histone tails and DNA methylation. This has evolved to include any process that alters gene activity without changing the DNA sequence, and leads to modifications that can be transmitted to daughter cells. It also leads to a better knowledge of the changes in the regulation of genes and genomes that occur in major psychosis. It may also aid in understanding why the same gene sequence may predispose an individual to schizophrenia or bipolar disorder and in other cases does not, and elucidate the molecular mechanisms of how harmful; environmental factors interact with the genome. Results from the work may further lead to new diagnostics and effective therapies.

The rapid developments in the field of genomics and proteomics are expected to lead to a further increase in the potential for early diagnosis, the fine-tuning of prognostic features of specific tumors and the detection of cancer predisposition. Oncogenomics has identified new drug targets for genotype-specific treatments and provided strategies to validate these targets and to develop drugs. With the potential need to stratify patients by genotype, clinical testing of targeted drugs has become more complicated while expectations of patients, investors, and funding agencies have become accelerated. Oncogenomics has progressed logically from molecular profiling to model systems, cancer pharmacology and clinical trials. Oncogenomics covers cutting-edge issues such as array-based diagnostics, pharmacogenomics, pharmacoproteomics and molecularly targeted therapeutics includes discussions of ethical, legal, and social issues related to cancer genomics and clinical trials.

Background: Studying biochemical pathway evolution for diseases is a flourishing area of Systems Biology. Here, we study Type 1 Diabetes Mellitus (T1D), focusing on growth of glutamate, β-alanine, taurine and hypotaurine, and butanoate metabolisms involved in onset of GAD and INS genes in Homo sapiens with comparative analysis in non-obese diabetic Mus musculus, biobreeding Diabetes-prone Rattus norvegicus, Pan troglodytes, Oryctolagus cuniculus, Danio rerio and Drosophila melanogaster respectively.Methods: We propose an algorithm for growth analysis for four metabolic pathways involved in T1D. It has three modules, pattern finding, interaction identification and growth detection. The first module identifies patterns using Community structures using Hamming distances and the Tanimoto coefficient. We have performed functional analysis by representing patterns using ODEs, and identified Stoichiometric, Gradient and Jacobian matrices. The second module identifies interactions among patterns using cut-sets and network-partitioning by 'Divide-and-conquer'. The third module identifies functions of patterns using interactions, thereby highlighting their nature of growth.Results: We observed that metabolites that are genetically robust and resist alterations against stable state during evolution, account for emergence of a scale-free network.Discussion: New modules get acquired to the fundamental cluster in a preferential manner, an instance of micro-evolution theory. For instance, (S)-3-hydroxy butanoyl-CoA, acetoacetyl-CoA, acetoacetate, acetyl-CoA, (S)-3-hydroxy-3-methyl glutaryl-CoA acts as a fundamental cluster in butanoate metabolism. Moreover, the interactions among metabolites are divergent in nature. Graphical abstractDisplay Omitted HighlightsAlgorithm for evolutionary growth of 4 pathways in Type 1 Diabetes Mellitus.Integration from 36 databases for constructing the metabolic pathways.Finds patterns for community, interaction, growth using Hamming, cuts and payoff.Pathways grow in a manner when densest metabolite evolves followed by its links.This model of evolution has been termed as 'divergent root-to-leaf' growth model.

Work on human immortalized cell lines is not considered research on human subjects, but does involve biohazards. It has also been estimated that about 80% of human cell lines are the kind of cells that they are expected. Cells that are cultured directly from a subject are referred to as primary cells. Clonetics is the term can be used to describe Human Immortalized Cell Lines. Using Clonetics, the process of drug discovery and development can be accelerated. It is expected to contribute to drug development in metabolic diseases. These can be successfully used in many medical treatments.

Metabolomics is one of the key approaches of systems biology that consists of studying biochemical networks having a set of metabolites, enzymes, reactions and their interactions. As biological networks are very complex in nature, proper techniques and models need to be chosen for their better understanding and interpretation. One of the useful strategies in this regard is using path mining strategies and graph-theoretical approaches that help in building hypothetical models and perform quantitative analysis. Furthermore, they also contribute to analyzing topological parameters in metabolome networks. Path mining techniques can be based on grammars, keys, patterns and indexing. Moreover, they can also be used for modeling metabolome networks, finding structural similarities between metabolites, in-silico metabolic engineering, shortest path estimation and for various graph-based analysis. In this manuscript, we have highlighted some core and applied areas of path-mining for modeling and analysis of metabolic networks.

Fats are immensely important for the survival of the human body. These are stored in the body in the form of fatty acids and reused whenever the body is unable to consume essential fats(at the time of exercise, fasting etc). In the human heart cell, the transport of certain fatty acids into the mitochondria is required to produce energy. This is done via a certain enzyme called Carnitine Translocase. This paper emphasizes on the simulation of the presence of Carnitine Translocase. Metabolic flux balance analysis was done to detect the presence of this enzyme, thus by preventing the accumulation of excess Fatty Acyl Carnitine that is known to give rise to various Cardiovascular diseases such as hypoglycemia, arrhythmia etc.

Disease Systems Biology is an area of life sciences, which is not very well understood to date. Analyzing infections and their spread in healthy metabolite networks can be one of the focussed areas in this regard. We have proposed a theory based on the classical forest fire model for analyzing the path of infection spread in healthy metabolic pathways. The theory suggests that when fire erupts in a forest, it spreads, and the surrounding trees also catch fire. Similarly, when we consider a metabolic network, the infection caused in the metabolites of the network spreads like a fire. We have constructed a simulation model which is used to study the infection caused in the metabolic networks from the start of infection, to spread and ultimately combating it. For implementation, we have used two approaches, first, based on quantitative strategies using ordinary differential equations and second, using graph-theory based properties. Furthermore, we are using certain probabilistic scores to complete this task and for interpreting the harm caused in the network, given by a 'critical value' to check whether the infection can be cured or not. We have tested our simulation model on metabolic pathways involved in Type I Diabetes mellitus in Homo sapiens. For validating our results biologically, we have used sensitivity analysis, both local and global, as well as for identifying the role of feedbacks in spreading infection in metabolic pathways. Moreover, information in literature has also been used to validate the results. The metabolic network datasets have been collected from the Kyoto Encyclopedia of Genes and Genomes (KEGG).

Modeling and analyzing the architecture of metabolic networks using various computational strategies can be successfully used for studying their internal metabolic dynamics as well as predicting missing links in diseased networks. In the present work, we have implemented our algorithm based on structural grammars, for automated metabolic pathway reconstruction and modeling in metabolic pathways responsible for coding genes responsible for the cause of Type 1 Diabetes mellitus (T1D) in Homo sapiens. We have especially implemented our algorithm for studying the metabolic pairs responsible for the functioning of GAD1 and GAD2 genes. We have also used the algorithm for automated reconstruction of glutamate metabolism, β-alanine metabolism, taurine & hypotaurine metabolism and butanoate metabolism pathway datasets. We have also used the algorithm for missing and multiple link prediction as well as nodal point analysis for all the four metabolic pathways with 90.4-100% accuracy.

The computational capability of living systems has intrigued researchers for years. Primarily, the focus has been on implementing aspects of living systems in computational devices. Computer literal peoples expand their hand to the molecular biologist and chemist to explore the potential for computation of biological molecules line Deoxyribonucleic Acid (DNA) and Ribonucleic Acid (RNA) which are information carrying molecules. In this context, DNA computation is basically a collection of specially selected DNA strands whose combinations will result in the solution to some problems. DNA computation rather DNA-based computing is at the intersection of several threads of research. Main advantages of DNA computation are miniaturization and parallelism over conventional silicon-based machines. The informationbearing capability of DNA molecules is a cornerstone of modern theories of genetics and molecular biology. In this paper we have tried to focus on some key issues regarding the used and implementation DNA-based devices in life science field. We have also tried to suggest its advantage over silicon computers.

Phylogenomics is the analysis of genomes of a group of closely related species. Almost all functional prediction methods rely on the identification, characterisation and quantification of sequence similarity between the gene of interest and genes for which functional information is available. This is the new evolved branch that is developed from the ongoing genome sequencing projects that have led to a phylogenetic approach based on genome-scale data. The use of large data sets in phylogenomic analysis results in a global increase in resolution owing to a decrease in sampling error.