Assessment of tumor characteristic gene expression in cell lines using a tissue similarity index (TSI)

Differences in treatment outcome between translational platforms in developing therapies for gastrointestinal cancers

The variability in translational models profoundly impacts the outcomes and predictive value of preclinical studies for gastrointestinal (GI) cancer treatments. Preclinical models, including 2D cell cultures, 3D organoids, patient-derived xenografts (PDXs), and animal models, provide distinct advantages and limitations in replicating the complex tumor microenvironment (TME) of human cancers. Each model's unique biological and structural differences contribute to discrepancies in treatment responses, challenging the direct translation of experimental results to clinical settings. While 2D cell cultures are cost-effective and suitable for high-throughput screening, they lack the 3D architecture and cellular interactions of the in vivo TME. Organoids offer a more comprehensive 3D structure that better mirrors tumor heterogeneity, yet they still face limitations in fully mimicking in vivo conditions, such as vascularization and immune cell interactions. PDXs, although more representative of human cancers due to their genetic fidelity and TME preservation, are costly and resource-intensive, with human stromal and immune components gradually replaced by murine counterparts over time. This review assesses the strengths and limitations of each model, highlighting recent advancements in translational platforms that incorporate complex TME features. Understanding the influence of model selection on treatment efficacy predictions is essential for enhancing the reliability of preclinical findings and advancing personalized therapeutic strategies for GI cancers.

Identifying cell lines across pan-cancer to be used in preclinical research as a proxy for patient tumor samples

In pre-clinical trials of anti-cancer drugs, cell lines are utilized as a model for patient tumor samples to understand the response of drugs. However, in vitro culture of cell lines, in general, alters the biology of the cell lines and likely gives rise to systematic differences from the tumor samples' genomic profiles; hence the drug response of cell lines may deviate from actual patients' drug response. In this study, we computed a similarity score for the selection of cell lines depicting the close and far resemblance to patient tumor samples in twenty-two different cancer types at genetic, genomic, and epigenetic levels integrating multi-omics datasets. We also considered the presence of immune cells in tumor samples and cancer-related biological pathways in this score which aids personalized medicine research in cancer. We showed that based on these similarity scores, cell lines were able to recapitulate the drug response of patient tumor samples for several FDA-approved cancer drugs in multiple cancer types. Based on these scores, several of the high-rank cell lines were shown to have a close likeness to the corresponding tumor type in previously reported in vitro experiments.

Proteomic features of soft tissue tumours in adolescents and young adults

BACKGROUND

Adolescents and young adult (AYA) patients with soft tissue tumours including sarcomas are an underserved group with disparities in treatment outcomes.

METHODS

To define the molecular features between AYA and older adult (OA) patients, we analysed the proteomic profiles of a large cohort of soft tissue tumours across 10 histological subtypes (AYA n = 66, OA n = 243), and also analysed publicly available functional genomic data from soft tissue tumour cell lines (AYA n = 5, OA n = 8).

RESULTS

Biological hallmarks analysis demonstrates that OA tumours are significantly enriched in MYC targets compared to AYA tumours. By comparing the patient-level proteomic data with functional genomic profiles from sarcoma cell lines, we show that the mRNA splicing pathway is an intrinsic vulnerability in cell lines from OA patients and that components of the spliceosome complex are independent prognostic factors for metastasis free survival in AYA patients.

CONCLUSIONS

Our study highlights the importance of performing age-specific molecular profiling studies to identify risk stratification tools and targeted agents tailored for the clinical management of AYA patients.

Fusobacterium nucleatum infection modulates the transcriptome and epigenome of HCT116 colorectal cancer cells in an oxygen-dependent manner

Fusobacterium nucleatum, a gram-negative oral bacterium, has been consistently validated as a strong contributor to the progression of several types of cancer, including colorectal (CRC) and pancreatic cancer. While previous in vitro studies have shown that intracellular F. nucleatum enhances malignant phenotypes such as cell migration, the dependence of this regulation on features of the tumor microenvironment (TME) such as oxygen levels are wholly uncharacterized. Here we examine the influence of hypoxia in facilitating F. nucleatum invasion and its effects on host responses focusing on changes in the global epigenome and transcriptome. Using a multiomic approach, we analyze epigenomic alterations of H3K27ac and global transcriptomic alterations sustained within a hypoxia and normoxia conditioned CRC cell line HCT116 at 24 h following initial infection with F. nucleatum. Our findings reveal that intracellular F. nucleatum activates signaling pathways and biological processes in host cells similar to those induced upon hypoxia conditioning in the absence of infection. Furthermore, we show that a hypoxic TME favors F. nucleatum invasion and persistence and therefore infection under hypoxia may amplify malignant transformation by exacerbating the effects induced by hypoxia alone. These results motivate future studies to investigate host-microbe interactions in tumor tissue relevant conditions that more accurately define parameters for targeted cancer therapies.

Co-culture platform for tuning of cancer receptor density allows for evaluation of bispecific immune cell engagers

Cancer immunotherapy, where a patient's immune system is harnessed to eradicate cancer cells selectively, is a leading strategy for cancer treatment. However, successes with immune checkpoint inhibitors (ICI) are hampered by reported systemic and organ-specific toxicities and by two-thirds of the patients being non-responders or subsequently acquiring resistance to approved ICIs. Hence substantial efforts are invested in discovering novel targeted immunotherapies aimed at reduced side-effects and improved potency. One way is utilizing the dual targeting feature of bispecific antibodies, which have made them increasingly popular for cancer immunotherapy. Easy and predictive screening methods for activation ranking of candidate drugs in tumor contra non-tumor environments are however lacking. Herein, we present a cell-based assay mimicking the tumor microenvironment by co-culturing B cells with engineered human embryonic kidney 293 T cells (HEK293T), presenting a controllable density of platelet-derived growth factor receptor β (PDGFRβ). A target density panel with three different surface protein levels on HEK293T cells was established by genetic constructs carrying regulatory elements limiting RNA translation of PDGFRβ. We employed a bispecific antibody-affibody construct called an AffiMab capable of binding PDGFRβ on cancer cells and CD40 expressed by B cells as a model. Specific activation of CD40-mediated signaling of immune cells was demonstrated with the two highest receptor-expressing cell lines, Level 2/3 and Level 4, while low-to-none in the low-expressing cell lines. The concept of receptor tuning and the presented co-culture protocol may be of general utility for assessing and developing novel bi-specific antibodies for immuno-oncology applications.

Systems approach for congruence and selection of cancer models towards precision medicine

Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.

Dissecting metastasis using preclinical models and methods

Metastasis has long been understood to lead to the overwhelming majority of cancer-related deaths. However, our understanding of the metastatic process, and thus our ability to prevent or eliminate metastases, remains frustratingly limited. This is largely due to the complexity of metastasis, which is a multistep process that likely differs across cancer types and is greatly influenced by many aspects of the in vivo microenvironment. In this Review, we discuss the key variables to consider when designing assays to study metastasis: which source of metastatic cancer cells to use and where to introduce them into mice to address different questions of metastasis biology. We also examine methods that are being used to interrogate specific steps of the metastatic cascade in mouse models, as well as emerging techniques that may shed new light on previously inscrutable aspects of metastasis. Finally, we explore approaches for developing and using anti-metastatic therapies, and how mouse models can be used to test them.

Bv8 mediates myeloid cell migration and enhances malignancy of colorectal cancer

Colorectal cancer (CRC) is the third most predominant malignancy in the world. Although the importance of immune system in cancer development has been well established, the underlying mechanisms remain to be investigated further. Here we studied a novel protein prokineticin 2 (Prok2, also known as Bv8) as a key pro-tumoral factor in CRC progression in in vitro and ex vivo settings. Human colorectal tumor tissues, myeloid cell lines (U937 cells and HL60 cells) and colorectal cancer cell line (Caco-2 cells) were used for various studies. Myeloid cell infiltration (especially neutrophils) and Bv8 accumulation were detected in human colorectal tumor tissue with immunostaining. The chemotactic effects of Bv8 on myeloid cells were presented in the transwell assay and chemotaxis assy. Cultured CRC cells treated with myeloid cells or Bv8 produced reactive oxygen species (ROS) and vascular endothelial growth factor (VEGF). Furthermore, ROS and VEGF acted as pro-angiogenesis buffer in myeloid cell-infiltrated CRC microenvironment. Moreover, myeloid cells or Bv8 enhanced energy consumption of glycolysis ATP and mitochondria ATP of CRC cells. Interestingly, myeloid cells increased CRC cell viability, but CRC cells decreased the viability of myeloid cells. ERK signalling pathway in CRC cells was activated in the presence of Bv8 or co-cultured myeloid cells. In conclusion, our data indicated the vital roles of Bv8 in myeloid cell infiltration and CRC development, suggesting that Bv8 may be a potential therapeutic target for colorectal cancer-related immunotherapy.

Transcriptionally imprinted glycomic signatures of acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is a genetically and phenotypically heterogeneous disease that has been suffering from stagnant survival curves for decades. In the endeavor toward improved diagnosis and treatment, cellular glycosylation has emerged as an interesting focus area in AML. While mechanistic insights are still limited, aberrant glycosylation may affect intracellular signaling pathways of AML blasts, their interactions within the microenvironment, and even promote chemoresistance. Here, we performed a meta-omics study to portray the glycomic landscape of AML, thereby screening for potential subtypes and responsible glyco-regulatory networks.

RESULTS

Initially, by integrating comprehensive N-, O-, and glycosphingolipid (GSL)-glycomics of AML cell lines with transcriptomics from public databases, we were able to pinpoint specific glycosyltransferases (GSTs) and upstream transcription factors (TFs) associated with glycan phenotypes. Intriguingly, subtypes M5 and M6, as classified by the French-American-British (FAB) system, emerged with distinct glycomic features such as high (sialyl) Lewis^x/a ((s)Le^x/a) and high sialylation, respectively. Exploration of transcriptomics datasets of primary AML cells further substantiated and expanded our findings from cell lines as we observed similar gene expression patterns and regulatory networks that were identified to be involved in shaping AML glycan signatures.

CONCLUSIONS

Taken together, our data suggest transcriptionally imprinted glycomic signatures of AML, reflecting their differentiation status and FAB classification. This study expands our insights into the emerging field of AML glycosylation and paves the way for studies of FAB class-associated glycan repertoires of AML blasts and their functional implications.

Impact of Washing Processes on RNA Quantity and Quality in Patient-Derived Colorectal Cancer Tissues

Background: Colorectal cancer (CRC) is a common and lethal cancer worldwide. Extraction of high-quality RNA from CRC samples plays a key role in scientific research and translational medicine. Specimen collection and washing methods that do not compromise RNA quality or quantity are needed to ensure high quality specimens for gene expression analysis and other RNA-based downstream applications. We investigated the effect of tissue specimen collection and different preparation processes on the quality and quantity of RNA extracted from surgical CRC tissues. Materials and Methods: After surgical resection, tissues were harvested and prepared with various washing processes in a room adjacent to the operating room. One hundred fourteen tissues from 36 CRC patients were separately washed in either cold phosphate-buffered saline reagent (n = 34) or Dulbecco's modified Eagle's medium (DMEM; n = 34) for 2-3 minutes until the stool was removed, and unwashed specimens served as controls (n = 34). Six tissue specimens were washed and immersed in DMEM for up to 1 hour at 4°C. Before RNA extraction, all specimens were kept in the stabilizing reagent for 3 months at -80°C. RNA was extracted, and the concentration per milligram of tissue was measured. RNA quality was assessed using the RNA integrity number (RIN) value. Results: Different washing processes did not result in significant differences in RNA quantity or RIN values. In the six tissues that were washed and immersed in DMEM for 1 hour, RIN values significantly decreased. The quality of the extracted RNA from most specimens was excellent with the average RIN greater than 7. Conclusions: RNA is stable in specimens washed in different processes for short periods, but RIN values may decrease with prolonged wash times.

An in vitro model to monitor natural killer cell effector functions against breast cancer cells derived from human tumor tissue

Adoptive natural killer (NK) cell-based immunotherapy poses a promising treatment approach in cancer. Despite minimal toxicities associated with NK cell infusion, the potential of NK cell therapy is inhibited by the immunosuppressive tumor microenvironment (TME). Multiple approaches to improve anti-cancer NK cell effector functions are being investigated. While much of this preclinical research is currently performed with commercially available tumor cell lines, this approach lacks the influence of the TME and heterogeneity of the primary tumor in patients. Here, we describe a comprehensive protocol for NK cell cytotoxicity- and degranulation assays against tumor cells derived from primary breast cancer tissue. Treatments to boost NK cell anti-tumor effector functions can be implemented in this model. Moreover, by using culture supernatants in follow up assays or by including additional cell types in the co-culture system, other NK cell effector mechanisms that further orchestrate innate and adaptive immunity could be studied.

Harnessing preclinical models for the interrogation of ovarian cancer

Ovarian cancer (OC) is a heterogeneous malignancy with various etiology, histopathology, and biological feature. Despite accumulating understanding of OC in the post-genomic era, the preclinical knowledge still undergoes limited translation from bench to beside, and the prognosis of ovarian cancer has remained dismal over the past 30 years. Henceforth, reliable preclinical model systems are warranted to bridge the gap between laboratory experiments and clinical practice. In this review, we discuss the status quo of ovarian cancer preclinical models which includes conventional cell line models, patient-derived xenografts (PDXs), patient-derived organoids (PDOs), patient-derived explants (PDEs), and genetically engineered mouse models (GEMMs). Each model has its own strengths and drawbacks. We focus on the potentials and challenges of using these valuable tools, either alone or in combination, to interrogate critical issues with OC.

Novel Ex Vivo Models of Epithelial Ovarian Cancer: The Future of Biomarker and Therapeutic Research

Epithelial ovarian cancer (EOC) is a heterogenous disease associated with variations in presentation, pathology and prognosis. Advanced EOC is typified by frequent relapse and a historical 5-year survival of less than 30% despite improvements in surgical and systemic treatment. The advent of next generation sequencing has led to notable advances in the field of personalised medicine for many cancer types. Success in achieving cure in advanced EOC has however been limited, although significant prolongation of survival has been demonstrated. Development of novel research platforms is therefore necessary to address the rapidly advancing field of early diagnostics and therapeutics, whilst also acknowledging the significant tumour heterogeneity associated with EOC. Within available tumour models, patient-derived organoids (PDO) and explant tumour slices have demonstrated particular promise as novel ex vivo systems to model different cancer types including ovarian cancer. PDOs are organ specific 3D tumour cultures that can accurately represent the histology and genomics of their native tumour, as well as offer the possibility as models for pharmaceutical drug testing platforms, offering timing advantages and potential use as prospective personalised models to guide clinical decision-making. Such applications could maximise the benefit of drug treatments to patients on an individual level whilst minimising use of less effective, yet toxic, therapies. PDOs are likely to play a greater role in both academic research and drug development in the future and have the potential to revolutionise future patient treatment and clinical trial pathways. Similarly, ex vivo tumour slices or explants have also shown recent renewed promise in their ability to provide a fast, specific, platform for drug testing that accurately represents in vivo tumour response. Tumour explants retain tissue architecture, and thus incorporate the majority of tumour microenvironment making them an attractive method to re-capitulate in vivo conditions, again with significant timing and personalisation of treatment advantages for patients. This review will discuss the current treatment landscape and research models for EOC, their development and new advances towards the discovery of novel biomarkers or combinational therapeutic strategies to increase treatment options for women with ovarian cancer.

Integrated N- and O-Glycomics of Acute Myeloid Leukemia (AML) Cell Lines

Acute myeloid leukemia (AML) is characterized by a dysregulated expansion of poorly differentiated myeloid cells. Although patients are usually treated effectively by chemotherapy, a high rate of relapsed or refractory disease poses a major hurdle in its treatment. Recently, several studies have proposed implications of protein glycosylation in the pathobiology of AML including chemoresistance. Accordingly, associations have been found between specific glycan epitopes and the outcome of the disease. To advance this poorly studied field, we performed an exploratory glycomics study characterizing 21 widely used AML cell lines. Exploiting the benefits of porous graphitized carbon chromatography coupled to tandem mass spectrometry (PGC nano-LC-MS²), we qualitatively and quantitatively profiled N- and O-linked glycans. AML cell lines exhibited distinct glycan fingerprints differing in relevant glycan traits correlating with their cellular phenotype as classified by the FAB system. By implementing transcriptomics data, specific glycosyltransferases and hematopoietic transcription factors were identified, which are candidate drivers of the glycan phenotype of these cells. In conclusion, we report the varying expression of glycan structures across a high number of AML cell lines, including those associated with poor prognosis, identified underlying glycosyltransferases and transcription factors, and provide insights into the regulation of the AML glycan repertoire.

Kinomics platform using GBM tissue identifies BTK as being associated with higher patient survival

BTK is a dominant bioactive kinase expressed within both cancer and immune cells of GBM tissue. Complex cell co-cultures might better model the impact of kinase inhibitors as therapeutics in GBM.

Better understanding of GBM signalling networks in-vivo would help develop more physiologically relevant ex vivo models to support therapeutic discovery. A “functional proteomics” screen was undertaken to measure the specific activity of a set of protein kinases in a two-step cell-free biochemical assay to define dominant kinase activities to identify potentially novel drug targets that may have been overlooked in studies interrogating GBM-derived cell lines. A dominant kinase activity derived from the tumour tissue, but not patient-derived GBM stem-like cell lines, was Bruton tyrosine kinase (BTK). We demonstrate that BTK is expressed in more than one cell type within GBM tissue; SOX2-positive cells, CD163-positive cells, CD68-positive cells, and an unidentified cell population which is SOX2-negative CD163-negative and/or CD68-negative. The data provide a strategy to better mimic GBM tissue ex vivo by reconstituting more physiologically heterogeneous cell co-culture models including BTK-positive/negative cancer and immune cells. These data also have implications for the design and/or interpretation of emerging clinical trials using BTK inhibitors because BTK expression within GBM tissue was linked to longer patient survival.

SARS-CoV-2 Disrupts Proximal Elements in the JAK-STAT Pathway

SARS-CoV-2 can infect multiple organs, including lung, intestine, kidney, heart, liver, and brain. The molecular details of how the virus navigates through diverse cellular environments and establishes replication are poorly defined. Here, we generated a panel of phenotypically diverse, SARS-CoV-2-infectible human cell lines representing different body organs and performed longitudinal survey of cellular proteins and pathways broadly affected by the virus. This revealed universal inhibition of interferon signaling across cell types following SARS-CoV-2 infection. We performed systematic analyses of the JAK-STAT pathway in a broad range of cellular systems, including immortalized cells and primary-like cardiomyocytes, and found that SARS-CoV-2 targeted the proximal pathway components, including Janus kinase 1 (JAK1), tyrosine kinase 2 (Tyk2), and the interferon receptor subunit 1 (IFNAR1), resulting in cellular desensitization to type I IFN. Detailed mechanistic investigation of IFNAR1 showed that the protein underwent ubiquitination upon SARS-CoV-2 infection. Furthermore, chemical inhibition of JAK kinases enhanced infection of stem cell-derived cultures, indicating that the virus benefits from inhibiting the JAK-STAT pathway. These findings suggest that the suppression of interferon signaling is a mechanism widely used by the virus to evade antiviral innate immunity, and that targeting the viral mediators of immune evasion may help block virus replication in patients with COVID-19. IMPORTANCE SARS-CoV-2 can infect various organs in the human body, but the molecular interface between the virus and these organs remains unexplored. In this study, we generated a panel of highly infectible human cell lines originating from various body organs and employed these cells to identify cellular processes commonly or distinctly disrupted by SARS-CoV-2 in different cell types. One among the universally impaired processes was interferon signaling. Systematic analysis of this pathway in diverse culture systems showed that SARS-CoV-2 targets the proximal JAK-STAT pathway components, destabilizes the type I interferon receptor though ubiquitination, and consequently renders the infected cells resistant to type I interferon. These findings illuminate how SARS-CoV-2 can continue to propagate in different tissues even in the presence of a disseminated innate immune response.

Biomarkers and cell-based models to predict the outcome of neoadjuvant therapy for rectal cancer patients

Rectal cancer constitutes approximately one-third of all colorectal cancers and contributes to considerable mortality globally. In contrast to colon cancer, the standard treatment for localized rectal cancer often involves neoadjuvant chemoradiotherapy. Tumour response rates to treatment show substantial inter-patient heterogeneity, indicating a need for treatment stratification. Consequently researchers have attempted to establish new means for predicting tumour response in order to assist in treatment decisions. In this review we have summarized published findings regarding potential biomarkers to predict neoadjuvant treatment response for rectal cancer tumours. In addition, we describe cell-based models that can be utilized both for treatment prediction and for studying the complex mechanisms involved.

Pharmacogenomics of Impaired Tyrosine Kinase Inhibitor Response: Lessons Learned From Chronic Myelogenous Leukemia

The use of small molecules became one key cornerstone of targeted anti-cancer therapy. Among them, tyrosine kinase inhibitors (TKIs) are especially important, as they were the first molecules to proof the concept of targeted anti-cancer treatment. Since 2001, TKIs can be successfully used to treat chronic myelogenous leukemia (CML). CML is a hematologic neoplasm, predominantly caused by reciprocal translocation t(9;22)(q34;q11) leading to formation of the so-called BCR-ABL1 fusion gene. By binding to the BCR-ABL1 kinase and inhibition of downstream target phosphorylation, TKIs, such as imatinib or nilotinib, can be used as single agents to treat CML patients resulting in 80 % 10-year survival rates. However, treatment failure can be observed in 20-25 % of CML patients occurring either dependent or independent from the BCR-ABL1 kinase. Here, we review approved TKIs that are indicated for the treatment of CML, their side effects and limitations. We point out mechanisms of TKI resistance focusing either on BCR-ABL1-dependent mechanisms by summarizing the clinically observed BCR-ABL1-mutations and their implications on TKI binding, as well as on BCR-ABL1-independent mechanisms of resistances. For the latter, we discuss potential mechanisms, among them cytochrome P450 implications, drug efflux transporter variants and expression, microRNA deregulation, as well as the role of alternative signaling pathways. Further, we give insights on how TKI resistance could be analyzed and what could be learned from studying TKI resistance in CML in vitro.

An Overview on Diffuse Large B-Cell Lymphoma Models: Towards a Functional Genomics Approach

Simple Summary

Lymphoma research is a paradigm of integrating basic and applied research within the fields of molecular marker-based diagnosis and therapy. In recent years, major advances in next-generation sequencing have substantially improved the understanding of the genomics underlying diffuse large B-cell lymphoma (DLBCL), the most frequent type of B-cell lymphoma. This review addresses the various approaches that have helped unveil the biology and intricate alterations in this pathology, from cell lines to more sophisticated last-generation experimental models, such as organoids. We also provide an overview of the most recent findings in the field, their potential relevance for designing targeted therapies and the corresponding applicability to personalized medicine.

Abstract

Lymphoma research is a paradigm of the integration of basic and clinical research within the fields of diagnosis and therapy. Clinical, phenotypic, and genetic data are currently used to predict which patients could benefit from standard treatment. However, alternative therapies for patients at higher risk from refractoriness or relapse are usually empirically proposed, based on trial and error, without considering the genetic complexity of aggressive B-cell lymphomas. This is primarily due to the intricate mosaic of genetic and epigenetic alterations in lymphomas, which are an obstacle to the prediction of which drug will work for any given patient. Matching a patient’s genes to drug sensitivity by directly testing live tissues comprises the “precision medicine” concept. However, in the case of lymphomas, this concept should be expanded beyond genomics, eventually providing better treatment options for patients in need of alternative therapeutic approaches. We provide an overview of the most recent findings in diffuse large B-cell lymphomas genomics, from the classic functional models used to study tumor biology and the response to experimental treatments using cell lines and mouse models, to the most recent approaches with spheroid/organoid models. We also discuss their potential relevance and applicability to daily clinical practice.

An integrated landscape of protein expression in human cancer

Using 11 proteomics datasets, mostly available through the PRIDE database, we assembled a reference expression map for 191 cancer cell lines and 246 clinical tumour samples, across 13 lineages. We found unique peptides identified only in tumour samples despite a much higher coverage in cell lines. These were mainly mapped to proteins related to regulation of signalling receptor activity. Correlations between baseline expression in cell lines and tumours were calculated. We found these to be highly similar across all samples with most similarity found within a given sample type. Integration of proteomics and transcriptomics data showed median correlation across cell lines to be 0.58 (range between 0.43 and 0.66). Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis focusing on cancer-related public proteomics datasets. We therefore also highlight shortcomings and limitations of such studies. All data is available through PRIDE dataset identifier PXD013455 and in Expression Atlas.

Organoids of the Female Reproductive Tract: Innovative Tools to Study Desired to Unwelcome Processes

The pelviperineal organs of the female reproductive tract form an essential cornerstone of human procreation. The system comprises the ectodermal external genitalia, the Müllerian upper-vaginal, cervical, endometrial and oviductal derivatives, and the endodermal ovaries. Each of these organs presents with a unique course of biological development as well as of malignant degeneration. For many decades, various preclinical in vitro models have been employed to study female reproductive organ (patho-)biology, however, facing important shortcomings of limited expandability, loss of representativeness and inadequate translatability to the clinic. The recent emergence of 3D organoid models has propelled the field forward by generating powerful research tools that in vitro replicate healthy as well as diseased human tissues and are amenable to state-of-the-art experimental interventions. Here, we in detail review organoid modeling of the different female reproductive organs from healthy and tumorigenic backgrounds, and project perspectives for both scientists and clinicians.

Comparison of Proteomics Profiles Between Xenografts Derived from Cell Lines and Primary Tumors of Thyroid Carcinoma

Patient-consistent xenograft model is a challenge for all cancers but particularly for thyroid cancer, which shows some of the greatest genetic divergence between human tumors and cell lines. In this study, proteomic profiles of tumor tissues from patients, included anaplastic thyroid carcinoma (ATC) and papillary thyroid carcinoma, and xenografts (8305C, 8505C, FRO, BAPAP and IHH4) were obtained using HPLC-tandem mass spectrometry and compared based on all proteins detected (3,961), cancer-related proteins and druggable proteins using pairwise Pearson's correlation analysis. The human tissue showed low proteomic similarity to the ATC cell lines (8305C, r = 0.344-0.416; 8505C, 0.47-0.579; FRO, 0.267-0.307) and to PTC cell lines (BCPAP, 0.303-0.468; IHH4, 0.262-0.509). Human tissue showed the following similarity to cell lines at the level of 135 cancer-related pathways. The ATC cell lines contained 47.4% of the cancer-related pathways (19.26%-33.33%), while the PTC cell lines contained 40% (BCPAP, 25.93%; IHH4, 28.89%). In patient tumor tissues, 44-60 of 76 and 52-53 of 93 druggable proteins were identified in ATC and PTC tumors, respectively. Ten and 29 druggable proteins were not identified in any of the ATC and PTC xenografts, respectively. We provide a reference for CDX selecting in in vivo studies of thyroid cancer.

aScan: A Novel Method for the Study of Allele Specific Expression in Single Individuals

In diploid organisms, two copies of each allele are normally inherited from parents. Paternal and maternal alleles can be regulated and expressed unequally, which is referred to as allele-specific expression (ASE). In this work, we present aScan, a novel method for the identification of ASE from the analysis of matched individual genomic and RNA sequencing data. By performing extensive analyses of both real and simulated data, we demonstrate that aScan can correctly identify ASE with high accuracy and sensitivity in different experimental settings. Additionally, by applying our method to a small cohort of individuals that are not included in publicly available databases of human genetic variation, we outline the value of possible applications of ASE analysis in single individuals for deriving a more accurate annotation of "private" low-frequency genetic variants associated with regulatory effects on transcription. All in all, we believe that aScan will represent a beneficial addition to the set of bioinformatics tools for the analysis of ASE. Finally, while our method was initially conceived for the analysis of RNA-seq data, it can in principle be applied to any quantitative NGS assay for which matched genotypic and expression data are available. AVAILABILITY: aScan is currently available in the form of an open source standalone software package at: https://github.com/Federico77z/aScan/. aScan version 1.0.3, available at https://github.com/Federico77z/aScan/releases/tag/1.0.3, has been used for all the analyses included in this manuscript. A Docker image of the tool has also been made available at https://github.com/pmandreoli/aScanDocker.

Transcriptionally active enhancers in human cancer cells

The growth of human cancer cells is driven by aberrant enhancer and gene transcription activity. Here, we use transient transcriptome sequencing (TT-seq) to map thousands of transcriptionally active putative enhancers in fourteen human cancer cell lines covering seven types of cancer. These enhancers were associated with cell type-specific gene expression, enriched for genetic variants that predispose to cancer, and included functionally verified enhancers. Enhancer-promoter (E-P) pairing by correlation of transcription activity revealed ~ 40,000 putative E-P pairs, which were depleted for housekeeping genes and enriched for transcription factors, cancer-associated genes, and 3D conformational proximity. The cell type specificity and transcription activity of target genes increased with the number of paired putative enhancers. Our results represent a rich resource for future studies of gene regulation by enhancers and their role in driving cancerous cell growth.

Identification of Tamoxifen-Resistant Breast Cancer Cell Lines and Drug Response Signature

Breast cancer cell lines are frequently used to elucidate the molecular mechanisms of the disease. However, a large proportion of cell lines are affected by problems such as mislabeling and cross-contamination. Therefore, it is of great clinical significance to select optimal breast cancer cell lines models. Using tamoxifen survival-related genes from breast cancer tissues as the gold standard, we selected the optimal cell line model to represent the characteristics of clinical tissue samples. Moreover, using relative expression orderings of gene pairs, we developed a gene pair signature that could predict tamoxifen therapy outcomes. Based on 235 consistently identified survival-related genes from datasets GSE17705 and GSE6532, we found that only the differentially expressed genes (DEGs) from the cell line dataset GSE26459 were significantly reproducible in tissue samples (binomial test, p = 2.13E-07). Finally, using the consistent DEGs from cell line dataset GSE26459 and tissue samples, we used the transcriptional qualitative feature to develop a two-gene pair (TOP2A, SLC7A5; NMU, PDSS1) for predicting clinical tamoxifen resistance in the training data (logrank p = 1.98E-07); this signature was verified using an independent dataset (logrank p = 0.009909). Our results indicate that the cell line model from dataset GSE26459 provides a good representation of the characteristics of clinical tissue samples; thus, it will be a good choice for the selection of drug-resistant and drug-sensitive breast cancer cell lines in the future. Moreover, our signature could predict tamoxifen treatment outcomes in breast cancer patients.

A Clinical Genomics-Guided Prioritizing Strategy Enables Selecting Proper Cancer Cell Lines for Biomedical Research

Selecting appropriate cell lines to represent a disease is crucial for the success of biomedical research, because the usage of less relevant cell lines could deliver misleading results. However, systematic guidance on cell line selection is unavailable. Here we developed a clinical Genomics-guided Prioritizing Strategy for Cancer Cell Lines (CCL-cGPS) and help to guide this process. Statistical analyses revealed CCL-cGPS selected cell lines were among the most appropriate models. Moreover, we observed a linear correlation between the drug response and CCL-cGPS score of cell lines for breast and thyroid cancers. Using RT4 cells selected by CCL-GPS, we identified mebendazole and digitoxin as candidate drugs against bladder cancer and validate their promising anticancer effect through in vitro and in vivo experiments. Additionally, a web tool was developed. In conclusion, CCL-cGPS bridges the gap between tumors and cell lines, presenting a helpful guide to select the most suitable cell line models.

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Cell lines were ranked by the resemblance of transcriptional signatures to tumors

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Among 44 tumor subtypes, CCL-cGPS provides proper cell lines for each subtype

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CCL-cGPS was verified by the computational analysis, in vitro and in vivo assays

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A web tool was developed to guide the selection of the most suitable cell lines

SARS-CoV-2 desensitizes host cells to interferon through inhibition of the JAK-STAT pathway

SARS-CoV-2 can infect multiple organs, including lung, intestine, kidney, heart, liver, and brain. The molecular details of how the virus navigates through diverse cellular environments and establishes replication are poorly defined. Here, we performed global proteomic analysis of the virus-host interface in a newly established panel of phenotypically diverse, SARS-CoV-2-infectable human cell lines representing different body organs. This revealed universal inhibition of interferon signaling across cell types following SARS-CoV-2 infection. We performed systematic analyses of the JAK-STAT pathway in a broad range of cellular systems, including immortalized cell lines and primary-like cardiomyocytes, and found that several pathway components were targeted by SARS-CoV-2 leading to cellular desensitization to interferon. These findings indicate that the suppression of interferon signaling is a mechanism widely used by SARS-CoV-2 in diverse tissues to evade antiviral innate immunity, and that targeting the viral mediators of immune evasion may help block virus replication in patients with COVID-19.

Discovery of a potential biomarker for immunotherapy of melanoma: PLAC1 as an emerging target

BACKGROUND

Melanoma has increased in incidence worldwide prompting investigators to search for new biomarkers for targeted immunotherapy of this disease. Placenta specific 1 (PLAC1) is a new member of cancer-testis antigens with widespread expression in many types of cancer. Here, we aimed to study for the first time the expression pattern of PLAC1 in skin cancer samples including cutaneous melanoma, basal cell carcinoma (BCC), squamous cell carcinoma (SCC) in comparison to normal skin and nevus tissues and potential therapeutic effect of anti-PLAC1 antibody in melanoma cancer cell lines in vitro.

MATERIALS AND METHODS

Polyclonal and monoclonal antibodies were applied for immunohistochemical profiling of PLAC1 expression using tissue microarray. The cytotoxic action of anti-PLAC1 antibody alone or as an antibody drug conjugate (with anti-neoplastic agent SN38) was investigated in melanoma cell lines.

RESULTS

We observed that 100% (39 of 39) of melanoma tissues highly expressed PLAC1 with both cytoplasmic and surface expression pattern. Investigation of PLAC1 expression in BCC (n = 110) samples showed negative results. Cancer cells in SCC samples (n = 66) showed very weak staining. Normal skin tissues and nevus samples including congenital melanocytic nevus failed to express PLAC1. Anti-PLAC1-SN38 exerted a specific pattern of cytotoxicity in a dose- and time-dependent manner in melanoma cells expressing surface PLAC1.

CONCLUSIONS

Our findings re-inforce the concept of re-expression of embryonic/placental tissue antigens in cancer and highlight the possibility of melanoma targeted therapy by employing anti-PLAC1 antibodies. The data presented here should lead to the future research on targeted immunotherapy of patients with melanoma.

Data reduction and data visualization for automatic diagnosis using gene expression and clinical data

Accurate diagnoses of specific diseases require, in general, the review of the whole medical history of a patient. Currently, even though many advances have been made for disease monitoring, domain experts are still requested to perform direct analyses in order to get a precise classification, thus implying significant efforts and costs. In this work we present a framework for automated diagnosis based on high-dimensional gene expression and clinical data. Given that high-dimensional data can be difficult to analyze and computationally expensive to process, we first perform data reduction to transform high-dimensional representations of data into a lower dimensional space, yet keeping them meaningful for our purposes. We used then different data visualization techniques to embed complex pieces of information in 2-D images, that are in turn used to perform diagnosis relying on deep learning approaches. Experimental analyses show that the proposed method achieves good performance, featuring a prediction Recall value between 91% and 99%.

Human-Derived Model Systems in Gynecological Cancer Research

The human female reproductive tract (FRT) is a complex system that combines series of organs, including ovaries, fallopian tubes, uterus, cervix, vagina, and vulva; each of which possesses unique cellular characteristics and functions. This versatility, in turn, allows for the development of a wide range of epithelial gynecological cancers with distinct features. Thus, reliable model systems are required to better understand the diverse mechanisms involved in the regional pathogenesis of the reproductive tract and improve treatment strategies. Here, we review the current human-derived model systems available to study the multitude of gynecological cancers, including ovarian, endometrial, cervical, vaginal, and vulvar cancer, and the recent advances in the push towards personalized therapy.

The LL-100 Cell Lines Panel: Tool for Molecular Leukemia-Lymphoma Research

Certified cell line models provide ideal experimental platforms to answer countless scientific questions. The LL-100 panel is a cohort of cell lines that are broadly representative of all leukemia–lymphoma entities (including multiple myeloma and related diseases), rigorously authenticated and validated, and comprehensively annotated. The process of the assembly of the LL-100 panel was based on evidence and experience. To expand the genetic characterization across all LL-100 cell lines, we performed whole-exome sequencing and RNA sequencing. Here, we describe the conception of the panel and showcase some exemplary applications with a focus on cancer genomics. Due diligence was paid to exclude cross-contaminated and non-representative cell lines. As the LL-100 cell lines are so well characterized and readily available, the panel will be a valuable resource for identifying cell lines with mutations in cancer genes, providing superior model systems. The data also add to the current knowledge of the molecular pathogenesis of leukemia–lymphoma. Additional efforts to expand the breadth of available high-quality cell lines are clearly warranted.

Interplay of transcriptional signaling by progesterone, cyclic AMP, and inflammation in myometrial cells: implications for the control of human parturition

Parturition involves cellular signaling changes driven by the complex interplay between progesterone (P4), inflammation, and the cyclic adenosine monophosphate (cAMP) pathway. To characterize this interplay, we performed comprehensive transcriptomic studies utilizing eight treatment combinations on myometrial cell lines and tissue samples from pregnant women. We performed genome-wide RNA-sequencing on the hTERT-HM${}^{A/B}$ cell line treated with all combinations of P4, forskolin (FSK) (induces cAMP), and interleukin-1$\beta$ (IL-1$\beta$). We then performed gene set enrichment and regulatory network analyses to identify pathways commonly, differentially, or synergistically regulated by these treatments. Finally, we used tissue similarity index (TSI) to characterize the correspondence between cell lines and tissue phenotypes. We observed that in addition to their individual anti-inflammatory effects, P4 and cAMP synergistically blocked specific inflammatory pathways/regulators including STAT3/6, CEBPA/B, and OCT1/7, but not NF$\kappa$B. TSI analysis indicated that FSK + P4- and IL-1$\beta$-treated cells exhibit transcriptional signatures highly similar to non-laboring and laboring term myometrium, respectively. Our results identify potential therapeutic targets to prevent preterm birth and show that the hTERT-HM${}^{A/B}$ cell line provides an accurate transcriptional model for term myometrial tissue.

Colon adenocarcinoma-derived cells that express induced-pluripotent stem cell markers possess stem cell function

AIMS

Much work has been done to find markers of cancer stem cells (CSCs) that distinguish them from the tumor bulk cells and normal cells. Recent CSC research has applied the induced pluripotent stem cell (iPSC) concept. In this study, we investigated the expression of a panel of iPSC markers in primary colon adenocarcinoma (CA)-derived cell lines.

MATERIALS AND METHODS

Expression of iPSC markers by CA-derived primary cell lines was interrogated using immunocytochemistry, western blotting and RT-qPCR. The stem cell function of these cells was then assessed in vitro using differentiation and tumorsphere assays.

RESULTS

Expression of iPSC markers OCT4, SOX2, NANOG, KLF4 and c-MYC was more widespread in high-grade CA (HGCA) cell lines than low-grade CA (LGCA) cell lines, as demonstrated by western blotting and RT-qPCR. These cells could be induced to differentiate down the three embryonic lineages. Cells derived from HGCA were more capable of forming tumorspheres than those derived from LGCA. EpCAM sorting revealed that a population enriched for EpCAMHigh cells formed larger tumorspheres than EpCAMLow cells. Pluripotency markers, SSEA4 and TRA-1-60, were co-expressed by a small subpopulation of cells that also co-expressed SOX2 in 75% and OCT4 in 50% of the cell lines.

CONCLUSIONS

CA-derived primary cell lines contain tumorsphere-forming cells which express key pluripotency genes and can differentiate down 3 embryonic lineages, suggesting a pluripotent CSC-like phenotype. There appear to be two iPSC-like subpopulations, one with high EpCAM expression which forms larger tumorspheres than another with low EpCAM expression. Furthermore, these cells can be characterized based on iPSC marker expression, as we have previously demonstrated in the original CA tumor tissues.

Novel Aptamers Selected on Living Cells for Specific Recognition of Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a high heterogeneous group of tumors with a distinctly aggressive nature and high rates of relapse. So far, the lack of any known targetable proteins has not allowed a specific anti-tumor treatment. Therefore, the identification of novel agents for specific TNBC targeting and treatment is desperately needed. Here, by integrating cell-SELEX (Systematic Evolution of Ligands by EXponential enrichment) for the specific recognition of TNBC cells with high-throughput sequencing technology, we identified a panel of 2′-fluoropyrimidine-RNA aptamers binding to TNBC cells and their cisplatin- and doxorubicin-resistant derivatives at low nanomolar affinity. These aptamers distinguish TNBC cells from both non-malignant and non-TNBC breast cancer cells and are able to differentiate TNBC histological specimens. Importantly, they inhibit TNBC cell capacity of growing in vitro as mammospheres, indicating they could also act as anti-tumor agents. Therefore, our newly identified aptamers are a valuable tool for selectively dealing with TNBC.

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Six 2′FPy-RNA aptamers were obtained by TNBC Cell-SELEX/NGS

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They distinguish TNBC cells from non-malignant and non-TNBC breast cancer cells

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They differentiate TNBC histological specimens by aptamer-based staining

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They inhibit TNBC cell lines capacity of growing in vitro as mammospheres

Cancer Cell Lines Are Useful Model Systems for Medical Research

Cell lines are in vitro model systems that are widely used in different fields of medical research, especially basic cancer research and drug discovery. Their usefulness is primarily linked to their ability to provide an indefinite source of biological material for experimental purposes. Under the right conditions and with appropriate controls, authenticated cancer cell lines retain most of the genetic properties of the cancer of origin. During the last few years, comparing genomic data of most cancer cell lines has corroborated this statement and those that were observed studying the tumoral tissue equivalents included in the The Cancer Genome Atlas (TCGA) database. We are at the disposal of comprehensive open access cell line datasets describing their molecular and cellular alterations at an unprecedented level of accuracy. This aspect, in association with the possibility of setting up accurate culture conditions that mimic the in vivo microenvironment (e.g., three-dimensional (3D) coculture), has strengthened the importance of cancer cell lines for continuing to sustain medical research fields. However, it is important to consider that the appropriate use of cell lines needs to follow established guidelines for guaranteed data reproducibility and quality, and to prevent the occurrence of detrimental events (i.e., those that are linked to cross-contamination and mycoplasma contamination).

An Ex-Vivo Culture System of Ovarian Cancer Faithfully Recapitulating the Pathological Features of Primary Tumors

The success rate of establishing human cancer cell lines is not satisfactory and the established cell lines often do not preserve the molecular and histological features of the original tissues. In this study, we developed a novel culture method which can support proliferation of almost all primary epithelial ovarian cancer cells, as well as primary normal human oviductal epithelial cells. Cancer cells from fresh or frozen specimens were enriched by the anti-EpCAM antibody-conjugated magnetic beads, plated on Matrigel-coated plate and cultivated under the optimized culture conditions. Seventeen newly established ovarian cancer cell lines, which included all four major histotypes of ovarian cancer, were confirmed to express histotype-specific markers in vitro. Some of the cell lines from all the four histotypes, except mucinous type, generated tumors in immune-deficient mice and the xenograft tumor tissues recapitulated the corresponding original tissues faithfully. Furthermore, with poorly tumorigenic cell lines including mucinous type, we developed a novel xenograft model which could reconstruct the original tissue architecture through forced expression of a set of oncogenes followed by its silencing. With combination of the novel culture method and cell-derived xenograft system, virtually every epithelial ovarian cancer can be reconstituted in mice in a timely fashion.

The LL-100 panel: 100 cell lines for blood cancer studies

For many years, immortalized cell lines have been used as model systems for cancer research. Cell line panels were established for basic research and drug development, but did not cover the full spectrum of leukemia and lymphoma. Therefore, we now developed a novel panel (LL-100), 100 cell lines covering 22 entities of human leukemia and lymphoma including T-cell, B-cell and myeloid malignancies. Importantly, all cell lines are unequivocally authenticated and assigned to the correct tissue. Cell line samples were proven to be free of mycoplasma and non-inherent virus contamination. Whole exome sequencing and RNA-sequencing of the 100 cell lines were conducted with a uniform methodology to complement existing data on these publicly available cell lines. We show that such comprehensive sequencing data can be used to find lymphoma-subtype-characteristic copy number aberrations, mRNA isoforms, transcription factor activities and expression patterns of NKL homeobox genes. These exemplary studies confirm that the novel LL-100 panel will be useful for understanding the function of oncogenes and tumor suppressor genes and to develop targeted therapies.

Evaluating cell lines as models for metastatic breast cancer through integrative analysis of genomic data

Cell lines are widely-used models to study metastatic cancer although the extent to which they recapitulate the disease in patients remains unknown. The recent accumulation of genomic data provides an unprecedented opportunity to evaluate the utility of them for metastatic cancer research. Here, we reveal substantial genomic differences between breast cancer cell lines and metastatic breast cancer patient samples. We also identify cell lines that more closely resemble the different subtypes of metastatic breast cancer seen in the clinic and show that surprisingly, MDA-MB-231 cells bear little genomic similarities to basal-like metastatic breast cancer patient samples. Further comparison suggests that organoids more closely resemble the transcriptome of metastatic breast cancer samples compared to cell lines. Our work provides a guide for cell line selection in the context of breast cancer metastasis and highlights the potential of organoids in these studies.

Relevance of neuroendocrine tumours models assessed by kinomic profiling

Although there is evidence of a significant rise of neuroendocrine tumours (NETs) incidence, current treatments are largely insufficient due to somewhat poor knowledge of these tumours. Despite many efforts achieved to expose driver oncogene mutations in NETs, the genetic landscape of NETs is characterized by relatively few mutations and chromosomal aberrations per tumour compared with other tumour types. In addition, NETs display few actionable mutations providing compelling rationale for targeted therapies. Recent works aiming at characterizing currently used NETs in vitro models at the genomic level raised concerns on their reliability as bona fide tools to study NETs biology. However, the lack of actionable mutation in NETs implies that sole use of genomic is not sufficient to describe these models and establish appropriate therapeutic strategies. Several kinases and kinase-involving signalling pathways have been demonstrated as abnormally regulated in NETs. Yet, kinases have only been investigated regardless of their involvement in large intracellular signalling networks. In order to assess the validity of in vitro NETs models to study NETs biology, "next-generation" high throughput functional technologies based on "kinome-wide activity" will demonstrate the similarities between signalling pathways in NETs models and patients' samples. These approaches will significantly assist in identifying actionable alterations in NETs signalling pathways and guide patient stratification into early-phase clinical trials based on kinase inhibition targeted therapies.

The contribution and perspectives of proteomics to uncover ovarian cancer tumor markers

Despite all the advances in understanding the mechanisms involved in ovarian cancer (OC) development, many aspects still need to be unraveled and understood. Tumor markers (TMs) are of special interest in this disease. Some aspects of clinical management of OC might be improved by the use of validated TMs, such as differentiating subtypes, defining the most appropriate treatment, monitoring the course of the disease, or predicting clinical outcome. The Food and Drug Administration (FDA) has approved a few TMs for OC: CA125 (cancer antigen 125; monitoring), HE4 (Human epididymis protein; monitoring), ROMA (Risk Of Malignancy Algorithm; HE4+CA125; prediction of malignancy) and OVA1 (Vermillion's first-generation Multivariate Index Assay [MIA]; prediction of malignancy). Proteomics can help advance the research in the field of TMs for OC. A variety of biological materials are being used in proteomic analysis, among them tumor tissues, interstitial fluids, tumor fluids, ascites, plasma, and ovarian cancer cell lines. However, the discovery and validation of new TMs for OC is still very challenging. The enormous heterogeneity of histological types of samples and the individual variability of patients (lifestyle, comorbidities, drug use, and family history) are difficult to overcome in research protocols. In this work, we sought to gather relevant information regarding TMs, OC, biological samples for proteomic analysis, as well as markers and algorithms approved by the FDA for use in clinical routine.

Constructing tissue-specific transcriptional regulatory networks via a Markov random field

BACKGROUND

Recent advances in sequencing technologies have enabled parallel assays of chromatin accessibility and gene expression for major human cell lines. Such innovation provides a great opportunity to decode phenotypic consequences of genetic variation via the construction of predictive gene regulatory network models. However, there still lacks a computational method to systematically integrate chromatin accessibility information with gene expression data to recover complicated regulatory relationships between genes in a tissue-specific manner.

RESULTS

We propose a Markov random field (MRF) model for constructing tissue-specific transcriptional regulatory networks via integrative analysis of DNase-seq and RNA-seq data. Our method, named CSNets (cell-line specific regulatory networks), first infers regulatory networks for individual cell lines using chromatin accessibility information, and then fine-tunes these networks using the MRF based on pairwise similarity between cell lines derived from gene expression data. Using this method, we constructed regulatory networks specific to 110 human cell lines and 13 major tissues with the use of ENCODE data. We demonstrated the high quality of these networks via comprehensive statistical analysis based on ChIP-seq profiles, functional annotations, taxonomic analysis, and literature surveys. We further applied these networks to analyze GWAS data of Crohn's disease and prostate cancer. Results were either consistent with the literature or provided biological insights into regulatory mechanisms of these two complex diseases. The website of CSNets is freely available at http://bioinfo.au.tsinghua.edu.cn/jianglab/CSNETS/ .

CONCLUSIONS

CSNets demonstrated the power of joint analysis on epigenomic and transcriptomic data towards the accurate construction of gene regulatory network. Our work provides not only a useful resource of regulatory networks to the community, but also valuable experiences in methodology development for multi-omics data integration.

Human ex vivo 3D bone model recapitulates osteocyte response to metastatic prostate cancer

Prostate cancer (PCa) is the second leading cause of cancer deaths among American men. Unfortunately, there is no cure once the tumor is established within the bone niche. Although osteocytes are master regulators of bone homeostasis and remodeling, their role in supporting PCa metastases remains poorly defined. This is largely due to a lack of suitable ex vivo models capable of recapitulating the physiological behavior of primary osteocytes. To address this need, we integrated an engineered bone tissue model formed by 3D-networked primary human osteocytes, with conditionally reprogrammed (CR) primary human PCa cells. CR PCa cells induced a significant increase in the expression of fibroblast growth factor 23 (FGF23) by osteocytes. The expression of the Wnt inhibitors sclerostin and dickkopf-1 (Dkk-1), exhibited contrasting trends, where sclerostin decreased while Dkk-1 increased. Furthermore, alkaline phosphatase (ALP) was induced with a concomitant increase in mineralization, consistent with the predominantly osteoblastic PCa-bone metastasis niche seen in patients. Lastly, we confirmed that traditional 2D culture failed to reproduce these key responses, making the use of our ex vivo engineered human 3D bone tissue an ideal platform for modeling PCa-bone interactions.

CellExpress: a comprehensive microarray-based cancer cell line and clinical sample gene expression analysis online system

Database URL

http://cellexpress.cgm.ntu.edu.tw/.

An Organoid-Based Preclinical Model of Human Gastric Cancer

BACKGROUND & AIMS

Our goal was to develop an initial study for the proof of concept whereby gastric cancer organoids are used as an approach to predict the tumor response in individual patients.

METHODS

Organoids were derived from resected gastric cancer tumors (huTGOs) or normal stomach tissue collected from sleeve gastrectomies (huFGOs). Organoid cultures were treated with standard-of-care chemotherapeutic drugs corresponding to patient treatment: epirubicin, oxaliplatin, and 5-fluorouracil. Organoid response to chemotherapeutic treatment was correlated with the tumor response in each patient from whom the huTGOs were derived. HuTGOs were orthotopically transplanted into the gastric mucosa of NOD scid gamma mice.

RESULTS

Whereas huFGOs exhibited a half maximal inhibitory concentration that was similar among organoid lines, divergent responses and varying half maximal inhibitory concentration values among the huTGO lines were observed in response to chemotherapeutic drugs. HuTGOs that were sensitive to treatment were derived from a patient with a near complete tumor response to chemotherapy. However, organoids resistant to treatment were derived from patients who exhibited no response to chemotherapy. Orthotropic transplantation of organoids resulted in the engraftment and development of human adenocarcinoma. RNA sequencing revealed that huTGOs closely resembled the patient's native tumor tissue and not commonly used gastric cancer cell lines and cell lines derived from the organoid cultures.

CONCLUSIONS

The treatment of patient-derived organoids alongside patients from whom cultures were derived will ultimately test their usefulness to predict individual therapy response and patient outcome.

Proteomic analysis identifies highly expressed plasma membrane proteins for detection and therapeutic targeting of specific breast cancer subtypes

In recent years, there has been an emphasis on personalizing breast cancer treatment in order to avoid the debilitating side effects caused by broad-spectrum chemotherapeutic drug treatment. Development of personalized medicine requires the identification of proteins that are expressed by individual tumors. Herein, we reveal the identity of plasma membrane proteins that are overexpressed in estrogen receptor α-positive, HER2-positive, and triple negative breast cancer cells. The proteins we identified are involved in maintaining protein structure, intracellular homeostasis, and cellular architecture; enhancing cell proliferation and invasion; and influencing cell migration. These proteins may be useful for breast cancer detection and/or treatment.

Pattern fusion analysis by adaptive alignment of multiple heterogeneous omics data

Motivation

Integrating different omics profiles is a challenging task, which provides a comprehensive way to understand complex diseases in a multi-view manner. One key for such an integration is to extract intrinsic patterns in concordance with data structures, so as to discover consistent information across various data types even with noise pollution. Thus, we proposed a novel framework called 'pattern fusion analysis' (PFA), which performs automated information alignment and bias correction, to fuse local sample-patterns (e.g. from each data type) into a global sample-pattern corresponding to phenotypes (e.g. across most data types). In particular, PFA can identify significant sample-patterns from different omics profiles by optimally adjusting the effects of each data type to the patterns, thereby alleviating the problems to process different platforms and different reliability levels of heterogeneous data.

Results

To validate the effectiveness of our method, we first tested PFA on various synthetic datasets, and found that PFA can not only capture the intrinsic sample clustering structures from the multi-omics data in contrast to the state-of-the-art methods, such as iClusterPlus, SNF and moCluster, but also provide an automatic weight-scheme to measure the corresponding contributions by data types or even samples. In addition, the computational results show that PFA can reveal shared and complementary sample-patterns across data types with distinct signal-to-noise ratios in Cancer Cell Line Encyclopedia (CCLE) datasets, and outperforms over other works at identifying clinically distinct cancer subtypes in The Cancer Genome Atlas (TCGA) datasets.

Availability and implementation

PFA has been implemented as a Matlab package, which is available at http://www.sysbio.ac.cn/cb/chenlab/images/PFApackage_0.1.rar .

Contact

lnchen@sibs.ac.cn , liujuan@whu.edu.cn or zengtao@sibs.ac.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Extensive and systematic rewiring of histone post-translational modifications in cancer model systems

Histone post-translational modifications (PTMs) generate a complex combinatorial code that regulates gene expression and nuclear functions, and whose deregulation has been documented in different types of cancers. Therefore, the availability of relevant culture models that can be manipulated and that retain the epigenetic features of the tissue of origin is absolutely crucial for studying the epigenetic mechanisms underlying cancer and testing epigenetic drugs. In this study, we took advantage of quantitative mass spectrometry to comprehensively profile histone PTMs in patient tumor tissues, primary cultures and cell lines from three representative tumor models, breast cancer, glioblastoma and ovarian cancer, revealing an extensive and systematic rewiring of histone marks in cell culture conditions, which includes a decrease of H3K27me2/me3, H3K79me1/me2 and H3K9ac/K14ac, and an increase of H3K36me1/me2. While some changes occur in short-term primary cultures, most of them are instead time-dependent and appear only in long-term cultures. Remarkably, such changes mostly revert in cell line- and primary cell-derived in vivo xenograft models. Taken together, these results support the use of xenografts as the most representative models of in vivo epigenetic processes, suggesting caution when using cultured cells, in particular cell lines and long-term primary cultures, for epigenetic investigations.

The neuroendocrine phenotype, genomic profile and therapeutic sensitivity of GEPNET cell lines

Experimental models of neuroendocrine tumour disease are scarce, and no comprehensive characterisation of existing gastroenteropancreatic neuroendocrine tumour (GEPNET) cell lines has been reported. In this study, we aimed to define the molecular characteristics and therapeutic sensitivity of these cell lines. We therefore performed immunophenotyping, copy number profiling, whole-exome sequencing and a large-scale inhibitor screening of seven GEPNET cell lines. Four cell lines, GOT1, P-STS, BON-1 and QGP-1, displayed a neuroendocrine phenotype while three others, KRJ-I, L-STS and H-STS, did not. Instead, these three cell lines were identified as lymphoblastoid. Characterisation of remaining authentic GEPNET cell lines by copy number profiling showed that GOT1, among other chromosomal alterations, harboured losses on chromosome 18 encompassing the SMAD4 gene, while P-STS had a loss on 11q. BON-1 had a homozygous loss of CDKN2A and CDKN2B, and QGP-1 harboured amplifications of MDM2 and HMGA2 Whole-exome sequencing revealed both disease-characteristic mutations (e.g. ATRX mutation in QGP-1) and, for patient tumours, rare genetic events (e.g. TP53 mutation in P-STS, BON-1 and QGP-1). A large-scale inhibitor screening showed that cell lines from pancreatic NETs to a greater extent, when compared to small intestinal NETs, were sensitive to inhibitors of MEK. Similarly, neuroendocrine NET cells originating from the small intestine were considerably more sensitive to a group of HDAC inhibitors. Taken together, our results provide a comprehensive characterisation of GEPNET cell lines, demonstrate their relevance as neuroendocrine tumour models and explore their therapeutic sensitivity to a broad range of inhibitors.

Tissue Engineering Platforms to Replicate the Tumor Microenvironment of Multiple Myeloma

We described here the manufacturing and implementation of two prototype perfusion culture devices designed primarily for the cultivation of difficult-to-preserve primary patient-derived multiple myeloma cells (MMC). The first device consists of an osteoblast (OSB)-derived 3D tissue scaffold constructed in a perfused microfluidic environment. The second platform is a 96-well plate-modified perfusion culture device that can be utilized to reconstruct several tissue and tumor microenvironments utilizing both primary human and murine cells. This culture device was designed and fabricated specifically to: (1) enable the preservation of primary MMC for downstream use in biological studies and chemosensitivity analyses and, (2) provide a high-throughput format that is compatible with plate readers specifically seeing that this system is built on an industry standard 96-well tissue culture plate.