Global computational alignment of tumor and cell line transcriptional profiles

Bioinformatics approach combined with experimental verification reveals OAS3 gene implicated in paclitaxel resistance in head and neck cancer

BACKGROUND

This study aimed to identify a candidate gene associated with paclitaxel (PTX) resistance and to evaluate functionally its biological role in the PTX-resistant head and neck squamous cell carcinoma (HNSCC) cell lines and clinical specimens.

METHODS

Microarray data series containing samples of different types of cancers resistant to PTX were analyzed and then a candidate gene associated with PTX resistance was identified using various bioinformatics tools. After the suppression of the target gene expression, changes in cell viability and colony-forming ability were evaluated in PTX-resistant FaDu and SCC-9 cell lines.

RESULTS

Bioinformatics analyses of upregulated genes in PTX-resistant cancer cells indicated that OAS3 was associated with PTX resistance. The downregulation of OAS3 expression significantly reduced the viability and colony-forming capacity of PTX-resistant SCC-9 cells by inducing apoptosis and cell cycle arrest at G0/G1 phase.

CONCLUSIONS

The therapeutic targeting of OAS3 may resensitize PTX-resistant HNSCC cells with high OAS3 expression to PTX treatment.

Multi-tiered chemical proteomic maps of tryptoline acrylamide-protein interactions in cancer cells

Covalent chemistry is a versatile approach for expanding the ligandability of the human proteome. Activity-based protein profiling (ABPP) can infer the specific residues modified by electrophilic compounds through competition with broadly reactive probes. However, the extent to which such residue-directed platforms fully assess the protein targets of electrophilic compounds in cells remains unclear. Here we evaluate a complementary protein-directed ABPP method that identifies proteins showing stereoselective reactivity with alkynylated, chiral electrophilic compounds-termed stereoprobes. Integration of protein- and cysteine-directed data from cancer cells treated with tryptoline acrylamide stereoprobes revealed generally well-correlated ligandability maps and highlighted features, such as protein size and the proteotypicity of cysteine-containing peptides, that explain gaps in each ABPP platform. In total, we identified stereoprobe binding events for >300 structurally and functionally diverse proteins, including compounds that stereoselectively and site-specifically disrupt MAD2L1BP interactions with the spindle assembly checkpoint complex leading to delayed mitotic exit in cancer cells.

Prognostic genome and transcriptome signatures in colorectal cancers

Colorectal cancer is caused by a sequence of somatic genomic alterations affecting driver genes in core cancer pathways1. Here, to understand the functional and prognostic impact of cancer-causing somatic mutations, we analysed the whole genomes and transcriptomes of 1,063 primary colorectal cancers in a population-based cohort with long-term follow-up. From the 96 mutated driver genes, 9 were not previously implicated in colorectal cancer and 24 had not been linked to any cancer. Two distinct patterns of pathway co-mutations were observed, timing analyses identified nine early and three late driver gene mutations, and several signatures of colorectal-cancer-specific mutational processes were identified. Mutations in WNT, EGFR and TGFβ pathway genes, the mitochondrial CYB gene and 3 regulatory elements along with 21 copy-number variations and the COSMIC SBS44 signature correlated with survival. Gene expression classification yielded five prognostic subtypes with distinct molecular features, in part explained by underlying genomic alterations. Microsatellite-instable tumours divided into two classes with different levels of hypoxia and infiltration of immune and stromal cells. To our knowledge, this study constitutes the largest integrated genome and transcriptome analysis of colorectal cancer, and interlinks mutations, gene expression and patient outcomes. The identification of prognostic mutations and expression subtypes can guide future efforts to individualize colorectal cancer therapy.

Cell-intrinsic platinum response and associated genetic and gene expression signatures in ovarian cancer cell lines and isogenic models

Ovarian cancers are still largely treated with platinum-based chemotherapy as the standard of care, yet few biomarkers of clinical response have had an impact on clinical decision making as of yet. Two particular challenges faced in mechanistically deciphering platinum responsiveness in ovarian cancer have been the suitability of cell line models for ovarian cancer subtypes and the availability of information on comparatively how sensitive ovarian cancer cell lines are to platinum. We performed one of the most comprehensive profiles to date on 36 ovarian cancer cell lines across over seven subtypes and integrated drug response and multiomic data to improve on our understanding of the best cell line models for platinum responsiveness in ovarian cancer. RNA-seq analysis of the 36 cell lines in a single batch experiment largely conforms with the currently accepted subtyping of ovarian cancers, further supporting other studies that have reclassified cell lines and demonstrate that commonly used cell lines are poor models of high-grade serous ovarian carcinoma. We performed drug dose response assays in the 32 of these cell lines for cisplatin and carboplatin, providing a quantitative database of IC50s for these drugs. Our results demonstrate that cell lines largely fall either well above or below the equivalent dose of the clinical maximally achievable dose (Cmax) of each compound, allowing designation of cell lines as sensitive or resistant. We performed differential expression analysis for high-grade serous ovarian carcinoma cell lines to identify gene expression correlating with platinum-response. Further, we generated two platinum-resistant derivatives each for OVCAR3 and OVCAR4, as well as leveraged clinically-resistant PEO1/PEO4/PEO6 and PEA1/PEA2 isogenic models to perform differential expression analysis for seven total isogenic pairs of platinum resistant cell lines. While gene expression changes overall were heterogeneous and vast, common themes were innate immunity/STAT activation, epithelial to mesenchymal transition and stemness, and platinum influx/efflux regulators. In addition to gene expression analyses, we performed copy number signature analysis and orthogonal measures of homologous recombination deficiency (HRD) scar scores and copy number burden, which is the first report to our knowledge applying field-standard copy number signatures to ovarian cancer cell lines. We also examined markers and functional readouts of stemness that revealed that cell lines are poor models for examination of stemness contributions to platinum resistance, likely pointing to the fact that this is a transient state. Overall this study serves as a resource to determine the best cell lines to utilize for ovarian cancer research on certain subtypes and platinum response studies, as well as sparks new hypotheses for future study in ovarian cancer.

Building a translational cancer dependency map for The Cancer Genome Atlas

Cancer dependency maps have accelerated the discovery of tumor vulnerabilities that can be exploited as drug targets when translatable to patients. The Cancer Genome Atlas (TCGA) is a compendium of 'maps' detailing the genetic, epigenetic and molecular changes that occur during the pathogenesis of cancer, yet it lacks a dependency map to translate gene essentiality in patient tumors. Here, we used machine learning to build translational dependency maps for patient tumors, which identified tumor vulnerabilities that predict drug responses and disease outcomes. A similar approach was used to map gene tolerability in healthy tissues to prioritize tumor vulnerabilities with the best therapeutic windows. A subset of patient-translatable synthetic lethalities were experimentally tested, including PAPSS1/PAPSS12 and CNOT7/CNOT78, which were validated in vitro and in vivo. Notably, PAPSS1 synthetic lethality was driven by collateral deletion of PAPSS2 with PTEN and was correlated with patient survival. Finally, the translational dependency map is provided as a web-based application for exploring tumor vulnerabilities.

Elucidating the Transcriptional States of Spermatogenesis-Joint Analysis of Germline and Supporting Cell, Mice and Human, Normal and Perturbed, Bulk and Single-Cell RNA-Seq

In studying the molecular underpinning of spermatogenesis, we expect to understand the fundamental biological processes better and potentially identify genes that may lead to novel diagnostic and therapeutic strategies toward precision medicine in male infertility. In this review, we emphasized our perspective that the path forward necessitates integrative studies that rely on complementary approaches and types of data. To comprehensively analyze spermatogenesis, this review proposes four axes of integration. First, spanning the analysis of spermatogenesis in the healthy state alongside pathologies. Second, the experimental analysis of model systems (in which we can deploy treatments and perturbations) alongside human data. Third, the phenotype is measured alongside its underlying molecular profiles using known markers augmented with unbiased profiles. Finally, the testicular cells are studied as ecosystems, analyzing the germ cells alongside the states observed in the supporting somatic cells. Recently, the study of spermatogenesis has been advancing using single-cell RNA sequencing, where scientists have uncovered the unique stages of germ cell development in mice, revealing new regulators of spermatogenesis and previously unknown cell subtypes in the testis. An in-depth analysis of meiotic and postmeiotic stages led to the discovery of marker genes for spermatogonia, Sertoli and Leydig cells and further elucidated all the other germline and somatic cells in the testis microenvironment in normal and pathogenic conditions. The outcome of an integrative analysis of spermatogenesis using advanced molecular profiling technologies such as scRNA-seq has already propelled our biological understanding, with additional studies expected to have clinical implications for the study of male fertility. By uncovering new genes and pathways involved in abnormal spermatogenesis, we may gain insights into subfertility or sterility.

State-Dependent tACS Effects Reveal the Potential Causal Role of Prestimulus Alpha Traveling Waves in Visual Contrast Detection

The intricate relationship between prestimulus alpha oscillations and visual contrast detection variability has been the focus of numerous studies. However, the causal impact of prestimulus alpha traveling waves on visual contrast detection remains largely unexplored. In our research, we sought to discern the causal link between prestimulus alpha traveling waves and visual contrast detection across different levels of mental fatigue. Using electroencephalography alongside a visual detection task with 30 healthy adults (13 females; 17 males), we identified a robust negative correlation between prestimulus alpha forward traveling waves (FTWs) and visual contrast threshold (VCT). Inspired by this correlation, we utilized 45/-45° phase-shifted transcranial alternating current stimulation (tACS) in a sham-controlled, double-blind, within-subject experiment with 33 healthy adults (23 females; 10 males) to directly modulate these alpha traveling waves. After the application of 45° phase-shifted tACS, we observed a substantial decrease in FTW and an increase in backward traveling waves, along with a concurrent increase in VCT, compared with the sham condition. These changes were particularly pronounced under a low fatigue state. The findings of state-dependent tACS effects reveal the potential causal role of prestimulus alpha traveling waves in visual contrast detection. Moreover, our study highlights the potential of 45/-45° phase-shifted tACS in cognitive modulation and therapeutic applications.

Molecular subtypes of neuroendocrine carcinomas: A cross-tissue classification framework based on five transcriptional regulators

Neuroendocrine carcinomas (NECs) are extremely lethal malignancies that can arise at almost any anatomic site. Characterization of NECs is hindered by their rarity and significant inter- and intra-tissue heterogeneity. Herein, through an integrative analysis of over 1,000 NECs originating from 31 various tissues, we reveal their tissue-independent convergence and further unveil molecular divergence driven by distinct transcriptional regulators. Pan-tissue NECs are therefore categorized into five intrinsic subtypes defined by ASCL1, NEUROD1, HNF4A, POU2F3, and YAP1. A comprehensive portrait of these subtypes is depicted, highlighting subtype-specific transcriptional programs, genomic alterations, evolution trajectories, therapeutic vulnerabilities, and clinicopathological presentations. Notably, the newly discovered HNF4A-dominated subtype-H exhibits a gastrointestinal-like signature, wild-type RB1, unique neuroendocrine differentiation, poor chemotherapeutic response, and prevalent large-cell morphology. The proposal of uniform classification paradigm illuminates transcriptional basis of NEC heterogeneity and bridges the gap across different lineages and cytomorphological variants, in which context-dependent prevalence of subtypes underlies their phenotypic disparities.

Genomic Characterization of Preclinical Prostate Cancer Cell Line Models

As we move into the era of precision medicine, the growing relevance of genetic alterations to prostate cancer (PCa) development and treatment demonstrates the importance of characterizing preclinical models at the genomic level. Our study investigated the genomic characterization of eight PCa cell lines to understand which models are clinically relevant. We designed a custom AmpliSeq DNA gene panel that encompassed key molecular pathways targeting AR signaling, apoptosis, DNA damage repair, and PI3K/AKT/PTEN, in addition to tumor suppressor genes. We examined the relationship between cell line genomic alterations and therapeutic response. In addition, using DepMap's Celligner tool, we identified which preclinical models are most representative of specific prostate cancer patient populations on cBioPortal. These data will help investigators understand the genetic differences in preclinical models of PCa and determine which ones are relevant for use in their translational research.

Pharmacological targeting of the cancer epigenome

Epigenetic dysregulation is increasingly appreciated as a hallmark of cancer, including disease initiation, maintenance and therapy resistance. As a result, there have been advances in the development and evaluation of epigenetic therapies for cancer, revealing substantial promise but also challenges. Three epigenetic inhibitor classes are approved in the USA, and many more are currently undergoing clinical investigation. In this Review, we discuss recent developments for each epigenetic drug class and their implications for therapy, as well as highlight new insights into the role of epigenetics in cancer.

STAT3 is a genetic modifier of TGF-beta induced EMT in KRAS mutant pancreatic cancer

Oncogenic mutations in KRAS are among the most common in cancer. Classical models suggest that loss of epithelial characteristics and the acquisition of mesenchymal traits are associated with cancer aggressiveness and therapy resistance. However, the mechanistic link between these phenotypes and mutant KRAS biology remains to be established. Here, we identify STAT3 as a genetic modifier of TGF-beta-induced epithelial to mesenchymal transition. Gene expression profiling of pancreatic cancer cells identifies more than 200 genes commonly regulated by STAT3 and oncogenic KRAS. Functional classification of the STAT3-responsive program reveals its major role in tumor maintenance and epithelial homeostasis. The signatures of STAT3-activated cell states can be projected onto human KRAS mutant tumors, suggesting that they faithfully reflect characteristics of human disease. These observations have implications for therapeutic intervention and tumor aggressiveness.

CancerGPT for few shot drug pair synergy prediction using large pretrained language models

Large language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data. However, their ability to generalize to unseen tasks in more complex fields, such as biology and medicine has yet to be fully evaluated. LLMs can offer a promising alternative approach for biological inference, particularly in cases where structured data and sample size are limited, by extracting prior knowledge from text corpora. Here we report our proposed few-shot learning approach, which uses LLMs to predict the synergy of drug pairs in rare tissues that lack structured data and features. Our experiments, which involved seven rare tissues from different cancer types, demonstrate that the LLM-based prediction model achieves significant accuracy with very few or zero samples. Our proposed model, the CancerGPT (with ~ 124M parameters), is comparable to the larger fine-tuned GPT-3 model (with ~ 175B parameters). Our research contributes to tackling drug pair synergy prediction in rare tissues with limited data, and also advancing the use of LLMs for biological and medical inference tasks.

A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization

Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyze relationships between molecular markers and cancer dependencies derived from CRISPR-Cas9 screens. We identify dependency-associated gene expression markers beyond driver genes, and observe many gene addiction relationships driven by gain of function rather than synthetic lethal effects. By combining clinically informed dependency-marker associations with protein-protein interaction networks, we identify 370 anti-cancer priority targets for 27 cancer types, many of which have network-based evidence of a functional link with a marker in a cancer type. Mapping these targets to sequenced tumor cohorts identifies tractable targets in different cancer types. This target prioritization map enhances understanding of gene dependencies and identifies candidate anti-cancer targets for drug development.

Extracellular lipidosomes containing lipid droplets and mitochondria are released during melanoma cell division

BACKGROUND

The incidence of melanoma is increasing worldwide. Since metastatic melanoma is highly aggressive, it is important to decipher all the biological aspects of melanoma cells. In this context, we have previously shown that metastatic FEMX-I melanoma cells release small (< 150 nm) extracellular vesicles (EVs) known as exosomes and ectosomes containing the stem (and cancer stem) cell antigenic marker CD133. EVs play an important role in intercellular communication, which could have a micro-environmental impact on surrounding tissues.

RESULTS

We report here a new type of large CD133+ EVs released by FEMX-I cells. Their sizes range from 2 to 6 µm and they contain lipid droplets and mitochondria. Real-time video microscopy revealed that these EVs originate from the lipid droplet-enriched cell extremities that did not completely retract during the cell division process. Once released, they can be taken up by other cells. Silencing CD133 significantly affected the cellular distribution of lipid droplets, with a re-localization around the nuclear compartment. As a result, the formation of large EVs containing lipid droplets was severely compromised.

CONCLUSION

Given the biochemical effect of lipid droplets and mitochondria and/or their complexes on cell metabolism, the release and uptake of these new large CD133+ EVs from dividing aggressive melanoma cells can influence both donor and recipient cells, and therefore impact melanoma growth and dissemination.

Targeting tumor-stromal interactions in triple-negative breast cancer using a human vascularized micro-tumor model

Triple-negative breast cancer (TNBC) is highly aggressive with limited available treatments. Stromal cells in the tumor microenvironment (TME) are crucial in TNBC progression; however, understanding the molecular basis of stromal cell activation and tumor-stromal crosstalk in TNBC is limited. To investigate therapeutic targets in the TNBC stromal niche, we used an advanced human in vitro microphysiological system called the vascularized micro-tumor (VMT). Using single-cell RNA sequencing, we revealed that normal breast tissue stromal cells activate neoplastic signaling pathways in the TNBC TME. By comparing interactions in VMTs with clinical data, we identified therapeutic targets at the tumor-stromal interface with potential clinical significance. Combining treatments targeting Tie2 signaling with paclitaxel resulted in vessel normalization and increased efficacy of paclitaxel in the TNBC VMT. Dual inhibition of HER3 and Akt also showed efficacy against TNBC. These data demonstrate the potential of inducing a favorable TME as a targeted therapeutic approach in TNBC.

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.

Current advances in comprehensive omics data mining for oncology and cancer research

The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.

Comprehensively prognostic and immunological analysis of VRK Serine/Threonine Kinase 1 in pan-cancer and identification in hepatocellular carcinoma

BACKGROUND

VRK1 is a member of the vaccinia-related kinase (VRK) family of serine/threonine protein kinases, which is related to the occurrence and development of malignant tumors. The expression pattern, predictive value, and biological function of VRK1 in various cancers remain largely elusive and warrant further investigation.

METHODS

Public databases, such as TCGA, GTEx, and UCEC, were utilized to comprehensively analyze the expression of VRK1 across multiple cancer types. Prognostic significance was assessed through Univariate Cox regression and Kaplan-Meier analyses. Additionally, Spearman's correlation analysis was employed to explore the potential associations between VRK1 expression and various factors, including tumor microenvironment scores, immune cell infiltration, and immune-related genes. Moreover, to validate the findings, differential expression of VRK1 in HCC tissues and cell lines was further confirmed using qPCR, Western blot, and immunohistochemistry techniques.

RESULTS

The upregulation of VRK1 was observed in most cancer types, and was associated with worse prognosis in ACC, KICH, KIRP, LGG, LIHC, LUAD, MESO, and PCPG. In various cancers, VRK1 expression exhibited positive correlations with immune infiltrating cells, immune checkpoint-related genes, TMB, and MSI. Furthermore, the promoter methylation status of VRK1 varied across different tumor types, and this variation was associated with patient prognosis in certain cancers. In our experimental analyses, we observed significantly elevated expression of VRK1 in both HCC tissues and HCC cells. Functionally, we found that the downregulation of VRK1 had a profound impact on HCC cells, leading to a significant decrease in their proliferation, migration, and invasion capabilities.

CONCLUSION

The expression of VRK1 exerts a notable influence on the prognosis of several tumors and exhibits a strong correlation with tumor immune infiltration. Moreover, in the context of HCC, VRK1 may act as an oncogene, actively promoting tumor progression.

Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation

Cell lines are valuable resources as model for human biology and translational medicine. It is thus important to explore the concordance between the expression in various cell lines vis-à-vis human native and disease tissues. In this study, we investigate the expression of all human protein-coding genes in more than 1,000 human cell lines representing 27 cancer types by a genome-wide transcriptomics analysis. The cell line gene expression is compared with the corresponding profiles in various tissues, organs, single-cell types and cancers. Here, we present the expression for each cell line and give guidance for the most appropriate cell line for a given experimental study. In addition, we explore the cancer-related pathway and cytokine activity of the cell lines to aid human biology studies and drug development projects. All data are presented in an open access cell line section of the Human Protein Atlas to facilitate the exploration of all human protein-coding genes across these cell lines.

STAT3 is a genetic modifier of TGF-beta induced EMT in KRAS mutant pancreatic cancer

Oncogenic mutations in KRAS are among the most common in cancer. Classical models suggest that loss of epithelial characteristics and the acquisition of mesenchymal traits are associated with cancer aggressiveness and therapy resistance. However, the mechanistic link between these phenotypes and mutant KRAS biology remains to be established. Here we identify STAT3 as a genetic modifier of TGF-beta-induced epithelial to mesenchymal transition. Gene expression profiling of pancreatic cancer cells identifies more than 200 genes commonly regulated by STAT3 and oncogenic KRAS. Functional classification of STAT3 responsive program reveals its major role in tumor maintenance and epithelial homeostasis. The signatures of STAT3-activated cell states can be projected onto human KRAS mutant tumors, suggesting that they faithfully reflect characteristics of human disease. These observations have implications for therapeutic intervention and tumor aggressiveness.

Spatial topology of organelle is a new breast cancer cell classifier

Genomics and proteomics have been central to identify tumor cell populations, but more accurate approaches to classify cell subtypes are still lacking. We propose a new methodology to accurately classify cancer cells based on their organelle spatial topology. Herein, we developed an organelle topology-based cell classification pipeline (OTCCP), which integrates artificial intelligence (AI) and imaging quantification to analyze organelle spatial distribution and inter-organelle topology. OTCCP was used to classify a panel of human breast cancer cells, grown as 2D monolayer or 3D tumor spheroids using early endosomes, mitochondria, and their inter-organelle contacts. Organelle topology allows for a highly precise differentiation between cell lines of different subtypes and aggressiveness. These findings lay the groundwork for using organelle topological profiling as a fast and efficient method for phenotyping breast cancer function as well as a discovery tool to advance our understanding of cancer cell biology at the subcellular level.

Fibroblast-derived matrix models desmoplastic properties and forms a prognostic signature in cancer progression

The desmoplastic reaction observed in many cancers is a hallmark of disease progression and prognosis, particularly in breast and pancreatic cancer. Stromal-derived extracellular matrix (ECM) is significantly altered in desmoplasia, and as such plays a critical role in driving cancer progression. Using fibroblast-derived matrices (FDMs), we show that cancer cells have increased growth on cancer associated FDMs, when compared to FDMs derived from non-malignant tissue (normal) fibroblasts. We assess the changes in ECM characteristics from normal to cancer-associated stroma at the primary tumor site. Compositional, structural, and mechanical analyses reveal significant differences, with an increase in abundance of core ECM proteins, coupled with an increase in stiffness and density in cancer-associated FDMs. From compositional changes of FDM, we derived a 36-ECM protein signature, which we show matches in large part with the changes in pancreatic ductal adenocarcinoma (PDAC) tumor and metastases progression. Additionally, this signature also matches at the transcriptomic level in multiple cancer types in patients, prognostic of their survival. Together, our results show relevance of FDMs for cancer modelling and identification of desmoplastic ECM components for further mechanistic studies.

Evaluating cancer cell line and patient-derived xenograft recapitulation of tumor and non-diseased tissue gene expression profiles in silico

Preclinical models like cancer cell lines and patient-derived xenografts (PDXs) are vital for studying disease mechanisms and evaluating treatment options. It is essential that they accurately recapitulate the disease state of interest to generate results that will translate in the clinic. Prior studies have demonstrated that preclinical models do not recapitulate all biological aspects of human tissues, particularly with respect to the tissue of origin gene expression signatures. Therefore, it is critical to assess how well preclinical model gene expression profiles correlate with human cancer tissues to inform preclinical model selection and data analysis decisions. Here we evaluated how well preclinical models recapitulate human cancer and non-diseased tissue gene expression patterns in silico with respect to the full gene expression profile as well as subsetting by the most variable genes, genes significantly correlated with tumor purity, and tissue-specific genes by using publicly available gene expression profiles across multiple sources. We found that using the full gene set improves correlations between preclinical model and tissue global gene expression profiles, confirmed that GBM PDX global gene expression correlation to GBM tumor global gene expression outperforms GBM cell line to GBM tumor global gene expression correlations, and demonstrated that preclinical models in our study often failed to reproduce tissue-specific expression. While including additional genes for global gene expression comparison between cell lines and tissues decreases the overall correlation, it improves the relative rank between a cell line and its tissue of origin compared to other tissues. Our findings underscore the importance of using the full gene expression set measured when comparing preclinical models and tissues and confirm that tissue-specific patterns are better preserved in GBM PDX models than in GBM cell lines. Future studies can build on these findings to determine the specific pathways and gene sets recapitulated by particular preclinical models to facilitate model selection for a given study design or goal.

Lipid droplets are a metabolic vulnerability in melanoma

Melanoma exhibits numerous transcriptional cell states including neural crest-like cells as well as pigmented melanocytic cells. How these different cell states relate to distinct tumorigenic phenotypes remains unclear. Here, we use a zebrafish melanoma model to identify a transcriptional program linking the melanocytic cell state to a dependence on lipid droplets, the specialized organelle responsible for lipid storage. Single-cell RNA-sequencing of these tumors show a concordance between genes regulating pigmentation and those involved in lipid and oxidative metabolism. This state is conserved across human melanoma cell lines and patient tumors. This melanocytic state demonstrates increased fatty acid uptake, an increased number of lipid droplets, and dependence upon fatty acid oxidative metabolism. Genetic and pharmacologic suppression of lipid droplet production is sufficient to disrupt cell cycle progression and slow melanoma growth in vivo. Because the melanocytic cell state is linked to poor outcomes in patients, these data indicate a metabolic vulnerability in melanoma that depends on the lipid droplet organelle.

Identification and panoramic analysis of drug response-related genes in triple negative breast cancer using as an example NVP-BEZ235

Taking NVP-BEZ235 (BEZ235) as an example to screen drug response-related genes (DRRGs) and explore their potential value in triple-negative breast cancer (TNBC). Through high-throughput technique, multidimensional transcriptome expression data (mRNA, miRNA and lncRNA) of BEZ235-treated and -untreated MDA-MB-468 cell lines were obtained. Combined with transcriptome data of the MDA-MB-468 cells and TCGA-TNBC tissues, differential gene expression analysis and WGCNA were performed to identify DRRGs associated with tumor trait by simulating the drug response microenvironment (DRM) of BEZ235-treated patients. Based on DRRGs, we constructed a ceRNA network and verified the expression levels of three key molecules by RT-qPCR, which not only demonstrated the successful construction of a BEZ235-treated cell line model but also explained the antitumor mechanism of BEZ235. Four molecular subtypes related to the DRM with survival difference were proposed using cluster analysis, namely glycolysis subtype, proliferation depression subtype, immune-suppressed subtype, and immune-activated subtype. A novel prognostic signature consisting of four DRRGs was established by Lasso-Cox analysis, which exhibited outstanding performance in predicting overall survival compared with several excellent reported signatures. The high- and low-risk groups were characterized by enrichment of metabolism-related pathways and immune-related pathways, respectively. Of note, the low-risk group had a better response to immune checkpoint blockade. Besides, pRRophetic analysis found that patients in the low-risk group were more sensitive to methotrexate and cisplation, whereas more resistant to BEZ235, docetaxel and paclitaxel. In conclusion, the DRRGs exemplified by BEZ235 are potential biomarkers for TNBC molecular typing, prognosis prediction and targeted therapy. The novel DRRGs-guided strategy for predicting the subtype, survival and therapy efficacy, might be also applied to more cancers and drugs other than TNBC and BEZ235.

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.

Translating transcriptomic findings from cancer model systems to humans through joint dimension reduction

Model systems are an essential resource in cancer research. They simulate effects that we can infer into humans, but come at a risk of inaccurately representing human biology. This inaccuracy can lead to inconclusive experiments or misleading results, urging the need for an improved process for translating model system findings into human-relevant data. We present a process for applying joint dimension reduction (jDR) to horizontally integrate gene expression data across model systems and human tumor cohorts. We then use this approach to combine human TCGA gene expression data with data from human cancer cell lines and mouse model tumors. By identifying the aspects of genomic variation joint-acting across cohorts, we demonstrate how predictive modeling and clinical biomarkers from model systems can be improved.

OncoLoop: A Network-Based Precision Cancer Medicine Framework

Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide.

SIGNIFICANCE

OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available. This article is highlighted in the In This Issue feature, p. 247.

Patient-derived cell-based pharmacogenomic assessment to unveil underlying resistance mechanisms and novel therapeutics for advanced lung cancer

BACKGROUND

A pharmacogenomic platform using patient-derived cells (PDCs) was established to identify the underlying resistance mechanisms and tailored treatment for patients with advanced or refractory lung cancer.

METHODS

Drug sensitivity screening and multi-omics datasets were acquired from lung cancer PDCs (n = 102). Integrative analysis was performed to explore drug candidates according to genetic variants, gene expression, and clinical profiles.

RESULTS

PDCs had genomic characteristics resembled with those of solid lung cancer tissues. PDC molecular subtyping classified patients into four groups: (1) inflammatory, (2) epithelial-to-mesenchymal transition (EMT)-like, (3) stemness, and (4) epithelial growth factor receptor (EGFR)-dominant. EGFR mutations of the EMT-like subtype were associated with a reduced response to EGFR-tyrosine kinase inhibitor therapy. Moreover, although RB1/TP53 mutations were significantly enriched in small-cell lung cancer (SCLC) PDCs, they were also present in non-SCLC PDCs. In contrast to its effect in the cell lines, alpelisib (a PI3K-AKT inhibitor) significantly inhibited both RB1/TP53 expression and SCLC cell growth in our PDC model. Furthermore, cell cycle inhibitors could effectively target SCLC cells. Finally, the upregulation of transforming growth factor-β expression and the YAP/TAZ pathway was observed in osimertinib-resistant PDCs, predisposing them to the EMT-like subtype. Our platform selected XAV939 (a WNT-TNKS-β-catenin inhibitor) for the treatment of osimertinib-resistant PDCs. Using an in vitro model, we further demonstrated that acquisition of osimertinib resistance enhances invasive characteristics and EMT, upregulates the YAP/TAZ-AXL axis, and increases the sensitivity of cancer cells to XAV939.

CONCLUSIONS

Our PDC models recapitulated the molecular characteristics of lung cancer, and pharmacogenomics analysis provided plausible therapeutic candidates.

Synthetic lethal interactions of DEAD/H-box helicases as targets for cancer therapy

DEAD/H-box helicases are implicated in virtually every aspect of RNA metabolism, including transcription, pre-mRNA splicing, ribosomes biogenesis, nuclear export, translation initiation, RNA degradation, and mRNA editing. Most of these helicases are upregulated in various cancers and mutations in some of them are associated with several malignancies. Lately, synthetic lethality (SL) and synthetic dosage lethality (SDL) approaches, where genetic interactions of cancer-related genes are exploited as therapeutic targets, are emerging as a leading area of cancer research. Several DEAD/H-box helicases, including DDX3, DDX9 (Dbp9), DDX10 (Dbp4), DDX11 (ChlR1), and DDX41 (Sacy-1), have been subjected to SL analyses in humans and different model organisms. It remains to be explored whether SDL can be utilized to identity druggable targets in DEAD/H-box helicase overexpressing cancers. In this review, we analyze gene expression data of a subset of DEAD/H-box helicases in multiple cancer types and discuss how their SL/SDL interactions can be used for therapeutic purposes. We also summarize the latest developments in clinical applications, apart from discussing some of the challenges in drug discovery in the context of targeting DEAD/H-box helicases.

Establishment and characterization of NCC-PLPS2-C1: a novel cell line of pleomorphic liposarcoma

Pleomorphic liposarcoma (PLPS) is a highly malignant subtype of liposarcoma. It is histologically characterized by the presence of pleomorphic lipoblasts and can be accompanied by morphological foci that demonstrate differentiation to other histological lineages. PLPS is rare and accounts for only 5% of all liposarcomas. PLPS exhibits poor prognosis; distant metastases develop in 30-50% of patients after curative surgical resection, tumor-associated mortality occurs in up to 50% of patients, and effective chemotherapies for PLPS have not been established. The histological accompaniment of other morphological foci is an important prognostic factor for PLPS, and the development of chemotherapies for PLPS considering the histological morphology is necessary. Patient-derived cancer cell lines are critical tools for basic and pre-clinical research to understand diseases and develop chemotherapies. However, only two PLPS-derived cell lines have been reported, and their donor tumor specimens did not histologically accompany morphological foci other than lipoblasts. Thus, there is a need to establish patient-derived PLPS cell lines from various histological morphologies. Here, we report a novel PLPS cell line from a tumor specimen that histologically accompanied pleomorphic and bone-forming foci, and named it NCC-PLPS2-C1. NCC-PLPS2-C1 cells demonstrated constant proliferation, spheroid formation, and invasion capability in vitro. Screening of antitumor agents in NCC-PLPS2-C1 cells showed that bortezomib, romidepsin, and trabectedin were effective against NCC-PLPS2-C1. In conclusion, we report the first PLPS cell line from a tumor specimen that was morphologically accompanied by pleomorphic and born-forming foci. We believe that NCC-PLPS2-C1 will be useful for the development of novel chemotherapies for PLPS.

NetCellMatch: Multiscale Network-Based Matching of Cancer Cell Lines to Patients Using Graphical Wavelets

Cancer cell lines serve as model in vitro systems for investigating therapeutic interventions. Recent advances in high-throughput genomic profiling have enabled the systematic comparison between cell lines and patient tumor samples. The highly interconnected nature of biological data, however, presents a challenge when mapping patient tumors to cell lines. Standard clustering methods can be particularly susceptible to the high level of noise present in these datasets and only output clusters at one unknown scale of the data. In light of these challenges, we present NetCellMatch, a robust framework for network-based matching of cell lines to patient tumors. NetCellMatch first constructs a global network across all cell line-patient samples using their genomic similarity. Then, a multi-scale community detection algorithm integrates information across topologically meaningful (clustering) scales to obtain Network-Based Matching Scores (NBMS). NBMS are measures of cluster robustness which map patient tumors to cell lines. We use NBMS to determine representative "avatar" cell lines for subgroups of patients. We apply NetCellMatch to reverse-phase protein array data obtained from The Cancer Genome Atlas for patients and the MD Anderson Cell Line Project for cell lines. Along with avatar cell line identification, we evaluate connectivity patterns for breast, lung, and colon cancer and explore the proteomic profiles of avatars and their corresponding top matching patients. Our results demonstrate our framework's ability to identify both patient-cell line matches and potential proteomic drivers of similarity. Our methods are general and can be easily adapted to other'omic datasets.

Multi-omics analysis based on 3D-bioprinted models innovates therapeutic target discovery of osteosarcoma

Current in vitro models for osteosarcoma investigation and drug screening, including two-dimensional (2D) cell culture and tumour spheroids (i.e. cancer stem-like cells), lack extracellular matrix (ECM). Therefore, results from traditional models may not reflect real pathological processes in genuine osteosarcoma histological structures. Here, we report a three-dimensional (3D) bioprinted osteosarcoma model (3DBPO) that contains osteosarcoma cells and shrouding ECM analogue in a 3D frame. Photo-crosslinkable bioinks composed of gelatine methacrylamide and hyaluronic acid methacrylate mimicked tumour ECM. We performed multi-omics analysis, including transcriptomics and DNA methylomics, to determine differences between the 3DBPO model and traditional models. Compared with 2D models and tumour spheroids, our 3DBPO model showed significant changes in cell cycle, metabolism, adherens junctions, and other pathways associated with epigenetic regulation. The 3DBPO model was more sensitive to therapies targeted to the autophagy pathway. We showed that simulating ECM yielded different osteosarcoma cell metabolic characteristics and drug sensitivity in the 3DBPO model compared with classical models. We suggest 3D printed osteosarcoma models can be used in osteosarcoma fundamental and translational research, which may contribute to novel therapeutic strategy discovery.

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3DBPO model behaved better than traditional 2D and CSC models in simulating in vivo osteosarcoma microenvironment.

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3DBPO model showed significant changes in many signaling pathways associated with epigenetic regulation.

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3DBPO model was particularly sensitive to autophagy-related drugs.

VRK1 as a synthetic lethal target in VRK2 promoter-methylated cancers of the nervous system

Collateral lethality occurs when loss of a gene/protein renders cancer cells dependent on its remaining paralog. Combining genome-scale CRISPR/Cas9 loss-of-function screens with RNA sequencing in over 900 cancer cell lines, we found that cancers of nervous system lineage, including adult and pediatric gliomas and neuroblastomas, required the nuclear kinase vaccinia-related kinase 1 (VRK1) for their survival in vivo. VRK1 dependency was inversely correlated with expression of its paralog VRK2. VRK2 knockout sensitized cells to VRK1 loss, and conversely, VRK2 overexpression increased cell fitness in the setting of VRK1 loss. DNA methylation of the VRK2 promoter was associated with low VRK2 expression in human neuroblastomas and adult and pediatric gliomas. Mechanistically, depletion of VRK1 reduced barrier-to-autointegration factor phosphorylation during mitosis, resulting in DNA damage and apoptosis. Together, these studies identify VRK1 as a synthetic lethal target in VRK2 promoter-methylated adult and pediatric gliomas and neuroblastomas.

A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening

Accurate and robust prediction of patient-specific responses to a new compound is critical to personalized drug discovery and development. However, patient data are often too scarce to train a generalized machine learning model. Although many methods have been developed to utilize cell-line screens for predicting clinical responses, their performances are unreliable owing to data heterogeneity and distribution shift. Here we have developed a novel context-aware deconfounding autoencoder (CODE-AE) that can extract intrinsic biological signals masked by context-specific patterns and confounding factors. Extensive comparative studies demonstrated that CODE-AE effectively alleviated the out-of-distribution problem for the model generalization and significantly improved accuracy and robustness over state-of-the-art methods in predicting patient-specific clinical drug responses purely from cell-line compound screens. Using CODE-AE, we screened 59 drugs for 9,808 patients with cancer. Our results are consistent with existing clinical observations, suggesting the potential of CODE-AE in developing personalized therapies and drug response biomarkers.

Transition to a mesenchymal state in neuroblastoma confers resistance to anti-GD2 antibody via reduced expression of ST8SIA1

Immunotherapy with anti-GD2 antibodies has advanced the treatment of children with high-risk neuroblastoma, but nearly half of patients relapse, and little is known about mechanisms of resistance to anti-GD2 therapy. Here, we show that reduced GD2 expression was significantly correlated with the mesenchymal cell state in neuroblastoma and that a forced adrenergic-to-mesenchymal transition (AMT) conferred downregulation of GD2 and resistance to anti-GD2 antibody. Mechanistically, low-GD2-expressing cell lines demonstrated significantly reduced expression of the ganglioside synthesis enzyme ST8SIA1 (GD3 synthase), resulting in a bottlenecking of GD2 synthesis. Pharmacologic inhibition of EZH2 resulted in epigenetic rewiring of mesenchymal neuroblastoma cells and re-expression of ST8SIA1, restoring surface expression of GD2 and sensitivity to anti-GD2 antibody. These data identify developmental lineage as a key determinant of sensitivity to anti-GD2 based immunotherapies and credential EZH2 inhibitors for clinical testing in combination with anti-GD2 antibody to enhance outcomes for children with neuroblastoma.

Computational estimation of quality and clinical relevance of cancer cell lines

Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome‐wide editing screenings have facilitated the discovery of clinically relevant gene–drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine‐learning‐based directions that could resolve some of the arising discrepancies.

CNpare: matching DNA copy number profiles

SUMMARY

Selecting the optimal cancer cell line for an experiment can be challenging given the diversity of lines available. Here, we present CNpare, which identifies similar cell line models based on genome-wide DNA copy number.

AVAILABILITY

CNpare is available as an R package at https://github.com/macintyrelab/CNpare. All analysis performed in the manuscript can be reproduced via the code found at https://github.com/macintyrelab/CNpare_analyses.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Intrinsic transcriptional heterogeneity in neuroblastoma guides mechanistic and therapeutic insights

Cell state is controlled by master transcription factors (mTFs) that determine the cellular gene expression program. Cancer cells acquire dysregulated gene expression programs by mutational and non-mutational processes. Intratumoral heterogeneity can result from cells displaying distinct mTF-regulated cell states, which co-exist within the tumor. One archetypal tumor associated with transcriptionally regulated heterogeneity is high-risk neuroblastoma (NB). Patients with NB have poor overall survival despite intensive therapies, and relapsed patients are commonly refractory to treatment. The cellular populations that comprise NB are marked by different cohorts of mTFs and differential sensitivity to conventional therapies. Recent studies have highlighted mechanisms by which NB cells dynamically shift the cell state with treatment, revealing new opportunities to control the cellular response to treatment by manipulating cell-state-defining transcriptional programs. Here, we review recent advances in understanding transcriptionally defined cancer heterogeneity. We offer challenges to the field to encourage translation of basic science into clinical benefit.

Bi-order multimodal integration of single-cell data

Integration of single-cell multiomics profiles generated by different single-cell technologies from the same biological sample is still challenging. Previous approaches based on shared features have only provided approximate solutions. Here, we present a novel mathematical solution named bi-order canonical correlation analysis (bi-CCA), which extends the widely used CCA approach to iteratively align the rows and the columns between data matrices. Bi-CCA is generally applicable to combinations of any two single-cell modalities. Validations using co-assayed ground truth data and application to a CAR-NK study and a fetal muscle atlas demonstrate its capability in generating accurate multimodal co-embeddings and discovering cellular identity.

A 3D-printed scaffold-based osteosarcoma model allows to investigate tumor phenotypes and pathogenesis in an in vitro bone-mimicking niche

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Similar mechanical and structural properties as cortical bones.

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Recapitulating osteosarcoma phenotypes in morphology, proliferation, cytoskeleton re-organization and energy expenditure.

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A remarkable potential of ECM remodeling.

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The transcriptomic analysis to uncover distinct signaling pathways and molecules.

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Similar expression patterns of biomarkers as clinical trials.

Medicinal plants show remarkable antiproliferative potential in human cancer cell lines

Molecules isolated and identified from plant origin are used to manufacture most chemotherapeutic drugs for cancer treatment. We assumed that these plant extracts contain prolific bioactive compounds with potent antiproliferative activities and could be effective against different human cancer cells. Ethanolic extracts were prepared from Chelidonium majus, Myrica cerifera, Fumaria indica, Nigella sativa, and Silybum marianum, and the antiproliferative assay was performed in HepG2 and HeLa human cancer cell lines. All plants extract exhibited antiproliferative potential against studied cancer cell lines in the dose and time-dependent manner. Chelidonium majus and Silybum marianum have shown promising results against HepG2 and HeLa cells, respectively, followed by Myrica cerifera, Fumaria indica, and Nigella sativa. Results indicated that utilization of whole plant extract as anticancer compounds could be of great value in generating novel chemotherapeutic drugs.

Application of Proteomics in Pancreatic Ductal Adenocarcinoma Biomarker Investigations: A Review

Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive malignancy with a poor prognosis is usually detected at the advanced stage of the disease. The only US Food and Drug Administration-approved biomarker that is available for PDAC, CA 19-9, is most useful in monitoring treatment response among PDAC patients rather than for early detection. Moreover, when CA 19-9 is solely used for diagnostic purposes, it has only a recorded sensitivity of 79% and specificity of 82% in symptomatic individuals. Therefore, there is an urgent need to identify reliable biomarkers for diagnosis (specifically for the early diagnosis), ascertain prognosis as well as to monitor treatment response and tumour recurrence of PDAC. In recent years, proteomic technologies are growing exponentially at an accelerated rate for a wide range of applications in cancer research. In this review, we discussed the current status of biomarker research for PDAC using various proteomic technologies. This review will explore the potential perspective for understanding and identifying the unique alterations in protein expressions that could prove beneficial in discovering new robust biomarkers to detect PDAC at an early stage, ascertain prognosis of patients with the disease in addition to monitoring treatment response and tumour recurrence of patients.

Cautions should be taken when using cell models for gastric cancer research

Gastric cancer (GC)-derived cell lines were generally used in basic cancer research and drug screening. However, it is always concerned about the difference between cultured cells and primary tumor by oncologists. To address this question, we compared differentially expressed genes (DEGs) in primary cancers, healthy tissues, and cell lines both in vitro and in silico. Seven reported genes with decreased expression in GCs by DNA methylation were analyzed in our cohort studies and experimentally validation. Selected datasets from TCGA (The Cancer Genome Atlas), CCLE (The Broad Institute Cancer Cell Line Encyclopedia), and GTEx (The Genotype-Tissue Expression project) were used to represent GCs, GC-derived cell lines, and healthy tissues respectively in the in silico analysis. Thirty gastric tissues together with six cell lines were used for validations. Unexpectedly, we experimentally found that reported cancer-related downregulated genes were only found in cancer cell lines but not in biopsies. The unchanged gene expressions in primary GCs were generally consistent with our cohort study, using information from cancerous (TCGA) and healthy tissues (GETx). Substantial differences were also found between DEGs of cancer tissues (TGCA)/ healthy tissues (GTEx) pair and cell lines (CCLE)/ healthy tissues (GTEx) pair, which confirmed the significant differences between primary cancer and cancer cell lines. Moreover, elevated expression of YWHAQ (14-3-3 δ) and THBS1 were observed in the GC biopsies, which might be potential biomarkers for GC diagnosis, considering the increased YWHAQ and THBS1 associated with poor survival rates in gastric cancer patients. In sum, it is suggested that cautions should be taken when using GC cell lines to study genes that show great differences between cell lines and tissues.

Nanocatalyst-Mediated Chemodynamic Tumor Therapy

Traditional tumor treatments, including chemotherapy, radiotherapy, photodynamic therapy, and photothermal therapy, are developed and used to treat different types of cancer. Recently, chemodynamic therapy (CDT) has been emerged as a novel cancer therapeutic strategy. CDT utilizes Fenton or Fenton-like reaction to generate highly cytotoxic hydroxyl radicals (•OH) from endogenous hydrogen peroxide (H₂ O₂ ) to kill cancer cells, which displays promising therapeutic potentials for tumor treatment. However, the low catalytic efficiency and off-target side effects of Fenton reaction limit the biomedical application of CDT. In this regard, various strategies are implemented to potentiate CDT against tumor, including retrofitting the tumor microenvironment (e.g., increasing H₂ O₂ level, decreasing reductive substances, and reducing pH), enhancing the catalytic efficiency of nanocatalysts, and other strategies. This review aims to summarize the development of CDT and summarize these recent progresses of nanocatalyst-mediated CDT for antitumor application. The future development trend and challenges of CDT are also discussed.

Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq data

We present Beyondcell, a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq data and proposing cancer-specific treatments. Our method calculates an enrichment score in a collection of drug signatures, delineating therapeutic clusters (TCs) within cellular populations. Additionally, Beyondcell determines the therapeutic differences among cell populations and generates a prioritised sensitivity-based ranking in order to guide drug selection. We performed Beyondcell analysis in five single-cell datasets and demonstrated that TCs can be exploited to target malignant cells both in cancer cell lines and tumour patients. Beyondcell is available at: https://gitlab.com/bu_cnio/beyondcell .

Uncovering drug repurposing candidates for head and neck cancers: insights from systematic pharmacogenomics data analysis

Effective treatment options for head and neck squamous cell carcinoma (HNSCC) are currently lacking. We exploited the drug response and genomic data of the 28 HNSCC cell lines, screened with 4,518 compounds, from the PRISM repurposing dataset to uncover repurposing drug candidates for HNSCC. A total of 886 active compounds, comprising of 418 targeted cancer, 404 non-oncology, and 64 chemotherapy compounds were identified for HNSCC. Top classes of mechanism of action amongst targeted cancer compounds included PI3K/AKT/MTOR, EGFR, and HDAC inhibitors. We have shortlisted 36 compounds with enriched killing activities for repurposing in HNSCC. The integrative analysis confirmed that the average expression of EGFR ligands (AREG, EREG, HBEGF, TGFA, and EPGN) is associated with osimertinib sensitivity. Novel putative biomarkers of response including those involved in immune signalling and cell cycle were found to be associated with sensitivity and resistance to MEK inhibitors respectively. We have also developed an RShiny webpage facilitating interactive visualization to fuel further hypothesis generation for drug repurposing in HNSCC. Our study provides a rich reference database of HNSCC drug sensitivity profiles, affording an opportunity to explore potential biomarkers of response in prioritized drug candidates. Our approach could also reveal insights for drug repurposing in other cancers.

Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning

Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents.

Functional Impact of Genomic Complexity on the Transcriptome of Multiple Myeloma

PURPOSE

Multiple myeloma is a biologically heterogenous plasma-cell disorder. In this study, we aimed at dissecting the functional impact on transcriptome of gene mutations, copy-number abnormalities (CNA), and chromosomal rearrangements (CR). Moreover, we applied a geno-transcriptomic approach to identify specific biomarkers for personalized treatments.

EXPERIMENTAL DESIGN

We analyzed 514 newly diagnosed patients from the IA12 release of the CoMMpass study, accounting for mutations in multiple myeloma driver genes, structural variants, copy-number segments, and raw-transcript counts. We performed an in silico drug sensitivity screen (DSS), interrogating the Cancer Dependency Map (DepMap) dataset after anchoring cell lines to primary tumor samples using the Celligner algorithm.

RESULTS

Immunoglobulin translocations, hyperdiploidy and chr(1q)gain/amps were associated with the highest number of deregulated genes. Other CNAs and specific gene mutations had a lower but very distinct impact affecting specific pathways. Many recurrent genes showed a hotspot (HS)-specific effect. The clinical relevance of double-hit multiple myeloma found strong biological bases in our analysis. Biallelic deletions of tumor suppressors and chr(1q)-amplifications showed the greatest impact on gene expression, deregulating pathways related to cell cycle, proliferation, and expression of immunotherapy targets. Moreover, our in silico DSS showed that not only t(11;14) but also chr(1q)gain/amps and CYLD inactivation predicted differential expression of transcripts of the BCL2 axis and response to venetoclax.

CONCLUSIONS

The multiple myeloma genomic architecture and transcriptome have a strict connection, led by CNAs and CRs. Gene mutations impacted especially with HS-mutations of oncogenes and biallelic tumor suppressor gene inactivation. Finally, a comprehensive geno-transcriptomic analysis allows the identification of specific deregulated pathways and candidate biomarkers for personalized treatments in multiple myeloma.

The chemotherapeutic CX-5461 primarily targets TOP2B and exhibits selective activity in high-risk neuroblastoma

Survival in high-risk pediatric neuroblastoma has remained around 50% for the last 20 years, with immunotherapies and targeted therapies having had minimal impact. Here, we identify the small molecule CX-5461 as selectively cytotoxic to high-risk neuroblastoma and synergistic with low picomolar concentrations of topoisomerase I inhibitors in improving survival in vivo in orthotopic patient-derived xenograft neuroblastoma mouse models. CX-5461 recently progressed through phase I clinical trial as a first-in-human inhibitor of RNA-POL I. However, we also use a comprehensive panel of in vitro and in vivo assays to demonstrate that CX-5461 has been mischaracterized and that its primary target at pharmacologically relevant concentrations, is in fact topoisomerase II beta (TOP2B), not RNA-POL I. This is important because existing clinically approved chemotherapeutics have well-documented off-target interactions with TOP2B, which have previously been shown to cause both therapy-induced leukemia and cardiotoxicity-often-fatal adverse events, which can emerge several years after treatment. Thus, while we show that combination therapies involving CX-5461 have promising anti-tumor activity in vivo in neuroblastoma, our identification of TOP2B as the primary target of CX-5461 indicates unexpected safety concerns that should be examined in ongoing phase II clinical trials in adult patients before pursuing clinical studies in children.