Colorectal Cancer Cells Enter a Diapause-like DTP State to Survive Chemotherapy.

Cancer cells enter a reversible drug-tolerant persister (DTP) state to evade death from chemotherapy and targeted agents. It is increasingly appreciated that DTPs are important drivers of therapy failure and tumor relapse. We combined cellular barcoding and mathematical modeling in patient-derived colorectal cancer models to identify and characterize DTPs in response to chemotherapy. Barcode analysis revealed no loss of clonal complexity of tumors that entered the DTP state and recurred following treatment cessation. Our data fit a mathematical model where all cancer cells, and not a small subpopulation, possess an equipotent capacity to become DTPs. Mechanistically, we determined that DTPs display remarkable transcriptional and functional similarities to diapause, a reversible state of suspended embryonic development triggered by unfavorable environmental conditions. Our study provides insight into how cancer cells use a developmentally conserved mechanism to drive the DTP state, pointing to novel therapeutic opportunities to target DTPs.

Proteogenomic characterization of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.

PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis.

Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose-response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug-response analysis such as tissue distribution of dose-response metrics and biomarker analysis; and (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug-response phenotypes of cancer models.

Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models.

The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a crucial task of precision oncology. Machine learning methods have been employed to predict drug sensitivity based on the multiple omics data available for large panels of cancer cell lines. However, there are no comprehensive guidelines on how to properly train and validate such machine learning models for drug sensitivity prediction. In this paper, we introduce a set of guidelines for different aspects of training gene expression-based predictors using cell line datasets. These guidelines provide extensive analysis of the generalization of drug sensitivity predictors and challenge many current practices in the community including the choice of training dataset and measure of drug sensitivity. The application of these guidelines in future studies will enable the development of more robust preclinical biomarkers.

CellWhere: graphical display of interaction networks organized on subcellular localizations.

Given a query list of genes or proteins, CellWhere produces an interactive graphical display that mimics the structure of a cell, showing the local interaction network organized into subcellular locations. This user-friendly tool helps in the formulation of mechanistic hypotheses by enabling the experimental biologist to explore simultaneously two elements of functional context: (i) protein subcellular localization and (ii) protein-protein interactions or gene functional associations. Subcellular localization terms are obtained from public sources (the Gene Ontology and UniProt-together containing several thousand such terms) then mapped onto a smaller number of CellWhere localizations. These localizations include all major cell compartments, but the user may modify the mapping as desired. Protein-protein interaction listings, and their associated evidence strength scores, are obtained from the Mentha interactome server, or power-users may upload a pre-made network produced using some other interactomics tool. The Cytoscape.js JavaScript library is used in producing the graphical display. Importantly, for a protein that has been observed at multiple subcellular locations, users may prioritize the visual display of locations that are of special relevance to their research domain. CellWhere is at http://cellwhere-myology.rhcloud.com.

Text-mining-based feature selection for anticancer drug response prediction.

Motivation: Predicting anticancer treatment response from baseline genomic data is a critical obstacle in personalized medicine. Machine learning methods are commonly used for predicting drug response from gene expression data. In the process of constructing these machine learning models, one of the most significant challenges is identifying appropriate features among a massive number of genes. Results: In this study, we utilize features (genes) extracted using the text-mining of scientific literatures. Using two independent cancer pharmacogenomic datasets, we demonstrate that text-mining-based features outperform traditional feature selection techniques in machine learning tasks. In addition, our analysis reveals that text-mining feature-based machine learning models trained on in vitro data also perform well when predicting the response of in vivo cancer models. Our results demonstrate that text-mining-based feature selection is an easy to implement approach that is suitable for building machine learning models for anticancer drug response prediction. Availability and implementation: https://github.com/merlab/text_features.

Characterization of the diversity of type IV secretion system-encoding plasmids in Acinetobacter.

The multi-drug resistant pathogen Acinetobacter baumannii has gained global attention as an important clinical challenge. Owing to its ability to survive on surfaces, its capacity for horizontal gene transfer, and its resistance to front-line antibiotics, A. baumannii has established itself as a successful pathogen. Bacterial conjugation is a central mechanism for pathogen evolution. The epidemic multidrug-resistant A. baumannii ACICU harbours a plasmid encoding a Type IV Secretion System (T4SS) with homology to the E. coli F-plasmid, and plasmids with homologous gene clusters have been identified in several A. baumannii sequence types. However the genetic and host strain diversity, global distribution, and functional ability of this group of plasmids is not fully understood. Using systematic analysis, we show that pACICU2 belongs to a group of almost 120 T4SS-encoding plasmids within four different species of Acinetobacter and one strain of Klebsiella pneumoniae from human and environmental origin, and globally distributed across 20 countries spanning 4 continents. Genetic diversity was observed both outside and within the T4SS-encoding cluster, and 47% of plasmids harboured resistance determinants, with two plasmids harbouring eleven. Conjugation studies with an extensively drug-resistant (XDR) strain showed that the XDR plasmid could be successfully transferred to a more divergent A. baumanii, and transconjugants exhibited the resistance phenotype of the plasmid. Collectively, this demonstrates that these T4SS-encoding plasmids are globally distributed and more widespread among Acinetobacter than previously thought, and that they represent an important potential reservoir for future clinical concern.

Structural insights into an atypical histone binding mechanism by a PHD finger.

Complex associating with SET1 (COMPASS) is a histone H3K4 tri-methyltransferase controlled by several regulatory subunits including CXXC zinc finger protein 1 (Cfp1). Prior studies established the structural underpinnings controlling H3K4me3 recognition by the PHD domain of Cfp1's yeast homolog (Spp1). However, metazoans Cfp1PHD lacks structural elements important for H3K4me3 stabilization in Spp1, suggesting that in metazoans, Cfp1PHD domain binds H3K4me3 differently. The structure of Cfp1PHD in complex with H3K4me3 shows unique features such as non-canonical coordination of the first zinc atom and a disulfide bond forcing the reorientation of Cfp1PHD N-terminus, thereby leading to an atypical H3K4me3 binding pocket. This configuration minimizes Cfp1PHD reliance on canonical residues important for histone binding functions of other PHD domains. Cancer-related mutations in Cfp1PHD impair H3K4me3 binding, implying a potential impact on epigenetic signaling. Our work highlights a potential diversification of PHD histone binding modes and the impact of cancer mutations on Cfp1 functions.

A novel approach for protein subcellular location prediction using amino acid exposure.

BACKGROUND: Proteins perform their functions in associated cellular locations. Therefore, the study of protein function can be facilitated by predictions of protein location. Protein location can be predicted either from the sequence of a protein alone by identification of targeting peptide sequences and motifs, or by homology to proteins of known location. A third approach, which is complementary, exploits the differences in amino acid composition of proteins associated to different cellular locations, and can be useful if motif and homology information are missing. Here we expand this approach taking into account amino acid composition at different levels of amino acid exposure. RESULTS: Our method has two stages. For stage one, we trained multiple Support Vector Machines (SVMs) to score eukaryotic protein sequences for membership to each of three categories: nuclear, cytoplasmic and extracellular, plus extra category nucleocytoplasmic, accounting for the fact that a large number of proteins shuttles between those two locations. In stage two we use an artificial neural network (ANN) to propose a category from the scores given to the four locations in stage one. The method reaches an accuracy of 68% when using as input 3D-derived values of amino acid exposure. Calibration of the method using predicted values of amino acid exposure allows classifying proteins without 3D-information with an accuracy of 62% and discerning proteins in different locations even if they shared high levels of identity. CONCLUSIONS: In this study we explored the relationship between residue exposure and protein subcellular location. We developed a new algorithm for subcellular location prediction that uses residue exposure signatures. Our algorithm uses a novel approach to address the multiclass classification problem. The algorithm is implemented as web server 'NYCE' and can be accessed at http://cbdm.mdc-berlin.de/~amer/nyce.

Large-scale phenogenomic analysis of human cancers uncovers frequent alterations affecting SMC5/6 complex components in breast cancer.

Cancer cells often experience large-scale alterations in genome architecture because of DNA damage and replication stress. Whether mutations in core regulators of chromosome structure can also lead to cancer-promoting loss in genome stability is not fully understood. To address this question, we conducted a systematic analysis of mutations affecting a global regulator of chromosome biology -the SMC5/6 complex- in cancer genomics cohorts. Analysis of 64 959 cancer samples spanning 144 tissue types and 199 different cancer genome studies revealed that the SMC5/6 complex is frequently altered in breast cancer patients. Patient-derived mutations targeting this complex associate with strong phenotypic outcomes such as loss of ploidy control and reduced overall survival. Remarkably, the phenotypic impact of several patient mutations can be observed in a heterozygous context, hence providing an explanation for a prominent role of SMC5/6 mutations in breast cancer pathogenesis. Overall, our findings suggest that genes encoding global effectors of chromosome architecture can act as key contributors to cancer development in humans.

Spatiotemporal modeling of chemoresistance evolution in breast tumors uncovers dependencies on SLC38A7 and SLC46A1.

In solid tumors, drug concentrations decrease with distance from blood vessels. However, cellular adaptations accompanying the gradated exposure of cancer cells to drugs are largely unknown. Here, we modeled the spatiotemporal changes promoting chemotherapy resistance in breast cancer. Using pairwise cell competition assays at each step during the acquisition of chemoresistance, we reveal an important priming phase that renders cancer cells previously exposed to sublethal drug concentrations refractory to dose escalation. Therapy-resistant cells throughout the concentration gradient display higher expression of the solute carriers SLC38A7 and SLC46A1 and elevated intracellular concentrations of their associated metabolites. Reduced levels of SLC38A7 and SLC46A1 diminish the proliferative potential of cancer cells, and elevated expression of these SLCs in breast tumors from patients correlates with reduced survival. Our work provides mechanistic evidence to support dose-intensive treatment modalities for patients with solid tumors and reveals two members of the SLC family as potential actionable targets.

Differential DNA damage repair and PARP inhibitor vulnerability of the mammary epithelial lineages.

It is widely assumed that all normal somatic cells can equally perform homologous recombination (HR) and non-homologous end joining in the DNA damage response (DDR). Here, we show that the DDR in normal mammary gland inherently depends on the epithelial cell lineage identity. Bioinformatics, post-irradiation DNA damage repair kinetics, and clonogenic assays demonstrated luminal lineage exhibiting a more pronounced DDR and HR repair compared to the basal lineage. Consequently, basal progenitors were far more sensitive to poly(ADP-ribose) polymerase inhibitors (PARPis) in both mouse and human mammary epithelium. Furthermore, PARPi sensitivity of murine and human breast cancer cell lines as well as patient-derived xenografts correlated with their molecular resemblance to the mammary progenitor lineages. Thus, mammary epithelial cells are intrinsically divergent in their DNA damage repair capacity and PARPi vulnerability, potentially influencing the clinical utility of this targeted therapy.

Bimodal Gene Expression in Patients with Cancer Provides Interpretable Biomarkers for Drug Sensitivity.

Identifying biomarkers predictive of cancer cell response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have opened new avenues of research to develop predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common pitfall to these methods is the lack of interpretability as to how they make predictions, hindering the clinical translation of these models. To alleviate this issue, we used the recent logic modeling approach to develop a new machine learning pipeline that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. The performance of this approach was showcased in a compendium of the three largest in vitro pharmacogenomic datasets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rates in independent datasets. These results along with in vivo and clinical validation support a better translation of gene expression biomarkers between model systems using bimodal gene expression. SIGNIFICANCE: A new machine learning pipeline exploits the bimodality of gene expression to provide a reliable set of candidate predictive biomarkers with a high potential for clinical translatability.

Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer.

Resistance to targeted therapies is an important clinical problem in HER2-positive (HER2+) breast cancer. "Drug-tolerant persisters" (DTP), a subpopulation of cancer cells that survive via reversible, nongenetic mechanisms, are implicated in resistance to tyrosine kinase inhibitors (TKI) in other malignancies, but DTPs following HER2 TKI exposure have not been well characterized. We found that HER2 TKIs evoke DTPs with a luminal-like or a mesenchymal-like transcriptome. Lentiviral barcoding/single-cell RNA sequencing reveals that HER2+ breast cancer cells cycle stochastically through a "pre-DTP" state, characterized by a G0-like expression signature and enriched for diapause and/or senescence genes. Trajectory analysis/cell sorting shows that pre-DTPs preferentially yield DTPs upon HER2 TKI exposure. Cells with similar transcriptomes are present in HER2+ breast tumors and are associated with poor TKI response. Finally, biochemical experiments indicate that luminal-like DTPs survive via estrogen receptor-dependent induction of SGK3, leading to rewiring of the PI3K/AKT/mTORC1 pathway to enable AKT-independent mTORC1 activation. SIGNIFICANCE: DTPs are implicated in resistance to anticancer therapies, but their ontogeny and vulnerabilities remain unclear. We find that HER2 TKI-DTPs emerge from stochastically arising primed cells ("pre-DTPs") that engage either of two distinct transcriptional programs upon TKI exposure. Our results provide new insights into DTP ontogeny and potential therapeutic vulnerabilities. This article is highlighted in the In This Issue feature, p. 873.

IPO11 regulates the nuclear import of BZW1/2 and is necessary for AML cells and stem cells.

AML cells are arranged in a hierarchy with stem/progenitor cells giving rise to more differentiated bulk cells. Despite the importance of stem/progenitors in the pathogenesis of AML, the determinants of the AML stem/progenitor state are not fully understood. Through a comparison of genes that are significant for growth and viability of AML cells by way of a CRISPR screen, with genes that are differentially expressed in leukemia stem cells (LSC), we identified importin 11 (IPO11) as a novel target in AML. Importin 11 (IPO11) is a member of the importin ß family of proteins that mediate transport of proteins across the nuclear membrane. In AML, knockdown of IPO11 decreased growth, reduced engraftment potential of LSC, and induced differentiation. Mechanistically, we identified the transcription factors BZW1 and BZW2 as novel cargo of IPO11. We further show that BZW1/2 mediate a transcriptional signature that promotes stemness and survival of LSC. Thus, we demonstrate for the first time how specific cytoplasmic-nuclear regulation supports stem-like transcriptional signature in relapsed AML.

Novel subtypes of NPM1-mutated AML with distinct outcome.

Acute myeloid leukemia (AML) is heterogeneous with one common subtype recognized by the presence of recurrent mutation of nucleophosmin-1 (NPM1). Emerging evidence indicates that within NPM1 mutated AML there is variation in outcome which challenges how best to characterize and treat the individual patient. Our recent findings show that there are two distinct (primitive and committed) subtypes within NPM1 mutated AML patients. These subtypes exhibit specific molecular characteristics, disease differentiation states, patient survival, and differential drug responses.

Highlights from the 16th International Society for Computational Biology Student Council Symposium 2020.

In this meeting overview, we summarise the scientific program and organisation of the 16th International Society for Computational Biology Student Council Symposium in 2020 (ISCB SCS2020). This symposium was the first virtual edition in an uninterrupted series of symposia that has been going on for 15 years, aiming to unite computational biology students and early career researchers across the globe.

Orchestrating and sharing large multimodal data for transparent and reproducible research.

Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA ( orcestra.ca ), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies.

Assessing therapy response in patient-derived xenografts.

Quantifying response to drug treatment in mouse models of human cancer is important for treatment development and assignment, yet remains a challenging task. To be able to translate the results of the experiments more readily, a preferred measure to quantify this response should take into account more of the available experimental data, including both tumor size over time and the variation among replicates. We propose a theoretically grounded measure, KuLGaP, to compute the difference between the treatment and control arms. We test and compare KuLGaP to four widely used response measures using 329 patient-derived xenograft (PDX) models. Our results show that KuLGaP is more selective than currently existing measures, reduces the risk of false-positive calls, and improves translation of the laboratory results to clinical practice. We also show that outcomes of human treatment better align with the results of the KuLGaP measure than other response measures. KuLGaP has the potential to become a measure of choice for quantifying drug treatment in mouse models as it can be easily used via the kulgap.ca website.

Biological and therapeutic implications of a unique subtype of NPM1 mutated AML.

In acute myeloid leukemia (AML), molecular heterogeneity across patients constitutes a major challenge for prognosis and therapy. AML with NPM1 mutation is a distinct genetic entity in the revised World Health Organization classification. However, differing patterns of co-mutation and response to therapy within this group necessitate further stratification. Here we report two distinct subtypes within NPM1 mutated AML patients, which we label as primitive and committed based on the respective presence or absence of a stem cell signature. Using gene expression (RNA-seq), epigenomic (ATAC-seq) and immunophenotyping (CyToF) analysis, we associate each subtype with specific molecular characteristics, disease differentiation state and patient survival. Using ex vivo drug sensitivity profiling, we show a differential drug response of the subtypes to specific kinase inhibitors, irrespective of the FLT3-ITD status. Differential drug responses of the primitive and committed subtype are validated in an independent AML cohort. Our results highlight heterogeneity among NPM1 mutated AML patient samples based on stemness and suggest that the addition of kinase inhibitors to the treatment of cases with the primitive signature, lacking FLT3-ITD, could have therapeutic benefit.