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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2014

Differences in sensitivity to EGFR inhibitors could be explained by described biochemical differences between oncogenic Ras mutants

Emerging data suggest different activating Ras mutants may have different biological behaviors. The most striking example may be in colon cancer, where activating KRAS mutations generally indicate a lack of benefit to treatment with EGFR inhibitors, although the activating KRAS G13D mutation appears to be an exception. As KRAS G13D generally shares the same biochemical defects as the other oncogenic KRAS mutants, a mechanism for differential sensitivity is not apparent. Here, a previously developed mathematical model of Ras mutant signaling is used to investigate this problem. The purpose of the analysis is to determine whether differential response is consistent with known mechanisms of Ras signaling, and to determine if any known features of Ras mutants provide an explanation for differential sensitivity. Computational analysis of the mathematical model finds that differential response to upstream inhibition between cancers with different Ras mutants is indeed consistent with known mechanisms of Ras biology. Moreover, model analysis demonstrates that the subtle biochemical differences between G13D and G12D (and G12V) mutants are sufficient to enable differential response to upstream inhibition. Simulations suggest that wild-type Ras within the G13D mutant context is more effectively inhibited by upstream inhibitors than when it is in the G12D or G12V contexts. This difference is a consequence of an elevated Km for the G13D mutant. The identification of a single parameter that influences sensitivity is significant in that it suggests an approach to evaluate other, less common, Ras mutations for their anticipated response to upstream inhibition.

Cancer Biology↗

bioRxiv2017

An epithelial-mesenchymal-amoeboid transition gene signature reveals molecular subtypes of breast cancer progression and metastasis

Cancer cells within a tumor are known to display varying degrees of metastatic propensity but the molecular basis underlying such heterogeneity remains unclear. We analyzed genome-wide gene expression data obtained from primary tumors of lymph node-negative breast cancer patients using a novel metastasis biology-based Epithelial-Mesenchymal-Amoeboid Transition (EMAT) gene signature, and identified subtypes associated with distinct prognostic profiles. EMAT subtype status improved prognosis accuracy of clinical parameters and statistically outperformed traditional breast cancer intrinsic subtypes even after adjusting for treatment variables. Additionally, analysis of 3D spheroids from an in vitro isogenic model of breast cancer progression reveals that EMAT subtypes display progression from premalignant to malignant and pre-invasive to invasive cancer. EMAT classification is a biologically informed method to assess metastasis risk in early stage, lymph node-negative breast cancer patients.

cancer biology↗

bioRxiv2018

Somatic mutations in early metazoan genes disrupt regulatory links between unicellular and multicellular genes in cancer

Extensive transcriptional alterations are observed in cancer, many of which activate core biological processes established in unicellular organisms or suppress differentiation pathways formed in metazoans. Through rigorous, integrative analysis of genomics data from a range of solid tumours, we show many transcriptional changes in tumours are tied to mutations disrupting regulatory interactions between unicellular and multicellular genes within human gene regulatory networks (GRNs). Recurrent point mutations were enriched in regulator genes linking unicellular and multicellular subnetworks, while copy-number alterations affected downstream target genes in distinctly unicellular and multicellular regions of the GRN. Our results depict drivers of tumourigenesis as genes that created key regulatory links during the evolution of early multicellular life, whose dysfunction creates widespread dysregulation of primitive elements of the GRN. Several genes we identified as important in this process were associated with drug response, demonstrating the potential clinical value of our approach.

cancer biology↗

bioRxiv2018

Targeted RNA NextGenSeq profiling in oncology using single molecule molecular inversion probes.

Hundreds of biology-based precision drugs are available that neutralize aberrant molecular pathways in cancer. Molecular heterogeneity and the lack of reliable companion diagnostic biomarkers for many drugs makes targeted treatment of cancer inaccurate for many individuals, leading to futile overtreatment. To acquire a comprehensive insight in aberrant actionable biological pathways in individual cancers we applied a cost-effective targeted RNA next generation sequencing (NGS) technique. The test allows NGS-based measurement of transcript levels and splice variants of hundreds of genes with established roles in the biological behavior in many cancer types. We here present proof of concept that the technique generates a correct molecular diagnosis and a prognosis for glioma patients. The test not only confirmed known brain cancer-associated molecular aberrations but also identified aberrant expression levels of actionable genes and mutations that are associated with other cancer types. Targeted RNA-NGS is therefore a highly attractive method to guide precision therapy for the individual patient based on pathway analysis.

cancer biology↗

bioRxiv2017

Integrative Statistical Inferences for Drug Sensitivity Biomarkers in Cancer

Personal medicine has been associated with different patient responses to different anti-cancer therapies. Recently, scientists are looking not only for new biomarkers associated with a disease such as cancer but also identifying biomarkers that predict patients who are most likely to respond to a particular cancer treatment. Orderly endeavors to relate cancer mutational information with biological conditions may encourage the interpretation of somatic mutation indexes into significant biomarkers for patient stratification.\n\nWe have screened and incorporated a board of cancer cell lines from Genomics of Drug Sensitivity in Cancer (GDSC) database to recognize genomic highlights related with drug sensitivity. We used mutation, DNA copy number variation, and gene expression information from Catalogue of Somatic Mutations in Cancer (COSMIC) and The Cancer Genome ATLAS (TCGA) for cell lines with their reactions to associate focused and cytotoxic treatments with approved drugs and drugs under clinical and preclinical examination.\n\nWe discovered mutated cancer genes were related with cell reaction to, mostly accessible, cancer medications and some mutated genes were related with sensitivity to an expansive scope of therapeutic agents. By connecting drug activity to the useful many-sided quality of cancer genomes, efficient pharmacogenomic profiling in tumor cell lines gives an intense biomarker revelation stage to guide balanced malignancy remedial systems.\n\nOur study highlights that gene ANK2 amplification, and gene CELSER1 amplification and deletion are highly associated with anti-leukemic drug candidate LFM-A13. It also highlights that gene NUP214 and ROS1 copy number and gene NSD1 amplification are as a group highly associated with the parkinson drug Nilotinib. Finally, our study confirms that gene BRAF mutation is interacting with the BRAF-selective inhibitors drugs PLX4720 and SB590885. On the other hand, our study provides two open source analysis packages: bastah for the multitask-association analysis, and UNGeneAnno for automatic annotation of the variants.

genomics↗

bioRxiv2017

DNA methylation differences at regulatory elements are associated with the cancer risk factor age in normal breast tissue

BackgroundThe underlying biological mechanisms through which epidemiologically defined breast cancer risk factors contribute to disease risk remain poorly understood. Identification of the molecular changes associated with cancer risk factors in normal tissues may aid in determining the earliest events of carcinogenesis and informing cancer prevention strategies.\n\nResultsHere we investigated the impact cancer risk factors have on the normal breast epigenome by analyzing DNA methylation genome-wide (Infinium 450K array) in cancer-free women from the Susan G. Komen Tissue Bank (n = 100). We tested the relation of established breast cancer risk factors: age, body mass index, parity, and family history of disease with DNA methylation adjusting for potential variation in cell-type proportions. We identified 787 CpG sites that demonstrated significant associations (Q-value < 0.01) with subject age. Notably, DNA methylation was not strongly associated with the other evaluated breast cancer risk factors. Age-related DNA methylation changes are primarily increases in methylation enriched at breast epithelial cell enhancer regions (P = 7.1E-20), and binding sites of chromatin remodelers (MYC and CTCF). We validated the age-related associations in two independent populations of normal breast tissue (n = 18) and normal-adjacent to tumor tissue (n = 97). The genomic regions classified as age-related were more likely to be regions altered in cancer in both pre-invasive (n = 40, P=3.0E-03) and invasive breast tumors (n = 731, P=1.1E-13).\n\nConclusionsDNA methylation changes with age occur at regulatory regions, and are further exacerbated in cancer suggesting that age influences breast cancer risk in part through its contribution to epigenetic dysregulation in normal breast tissue.

epidemiology↗

bioRxiv2018

Mitochondrial Fusion Suppresses Pancreatic Cancer Growth via Reduced Oxidative Metabolism

Pancreatic cancer is a highly lethal disease whose aggressive biology that is driven by mitochondrial oxidative metabolism. Mitochondria normally form a network of fused organelles, but we find that patient-derived and genetically engineered murine pancreatic cancer cells exhibit highly fragmented mitochondria with robust oxygen consumption rates (OCR). When mitochondrial fusion was activated by the genetic or pharmacological inhibition Drp1, the morphology and metabolism of human and murine pancreatic cancer cells more closely resembled that of normal pancreatic epithelial cells. This reduced metabolism was correlated with slower tumor growth, fewer metastases, and enhanced survival in a syngeneic orthotopic model. Similarly, directly activating mitochondrial fusion by overexpression of Mfn2 also reduced tumor growth and metastases. Mitochondrial fusion in pancreatic cancer cells was associated with reduced mitochondrial mass and Complex I expression and function. Thus, these data suggest that enhancing mitochondrial fusion through Drp1 inhibition or enhanced Mfn2 expression or function has strong tumor suppressive activity against pancreatic cancer and may thus represent a highly novel and efficacious therapeutic target.

cancer biology↗

bioRxiv2017

PI3K Activation In Neural Stem Cells Drives Tumorigenesis Which Can Be Suppressed By Targeting CREB

Hyperactivation of the PI3K signaling is common in human cancers, including gliomas, but the precise role of the pathway in glioma biology remains to be determined. Some limited understanding of PI3K signaling in brain cancer come from studies on neural stem/progenitor cells (NSPCs) where signals transmitted via the PI3K pathway cooperate with other intracellular pathways and downstream transcription factors to regulate NSPC proliferation. To investigate the role for the PI3K pathway in glioma initiation and development, we generated a mouse model targeting the inducible expression of a Pik3caH1047A oncogenic mutation and simultaneous deletion of the PI3K negative regulator, Pten, in NSPCs. We show that the expression of a Pik3caH1047A was sufficient to initiate tumorigenesis but that simultaneous loss of Pten, was required for the development of invasive, high-grade glioma. Mutant NSPCs exhibited enhanced neurosphere forming capacity which correlated with increased Wnt signaling. We also show that loss of CREB in Pik3ca-PTEN tumors led to a longer symptom-free survival in mice. Taken together, our findings present a novel mouse model for high-grade glioma with which we demonstrate that the PI3K pathway is important for initiation of tumorigenesis and that disruption of downstream CREB signaling attenuates tumor expansion.

cancer biology↗

bioRxiv2018

Proteogenomic view of cancer epigenetics: the impact of DNA methylation on the cancer proteome

Aberrant DNA methylation disrupts normal gene expression in cancer and broadly contributes to oncogenesis. We previously developed MethylMix, a model-based algorithmic approach to identify epigenetically regulated driver genes. MethylMix identifies genes where methylation likely executes a functional role by using transcriptomic data to select only methylation events that can be linked to changes in gene expression. However, given that proteins more closely link genotype to phenotype recent high-throughput proteomic data provides an opportunity to more accurately identify functionally relevant abnormal methylation events. Here we present ProteoMix, which refines nominations for epigenetic driver genes by leveraging quantitative high-throughput proteomic data to select only genes where DNA methylation is predictive of protein abundance. Applying our algorithm across three cancer cohorts we find that ProteoMix narrows candidate nominations, where the effect of DNA methylation is often buffered at the protein level. Next, we find that ProteoMix genes are enriched for biological processes involved in cancer including functions involved in epithelial and mesenchymal transition. ProteoMix results are also enriched for tumor markers which are predictive of clinical features like tumor stage and we find clustering on ProteoMix genes captures cancer subtypes.

bioinformatics↗

bioRxiv2017

Mathematical modeling of tumor-tumor distant interactions supports a systemic control of tumor growth

Interactions between different tumors within the same organism have major clinical implications, especially in the context of surgery and metastatic disease. Three main explanatory theories (competition, angiogenesis inhibition and proliferation inhibition) have been proposed but precise determinants of the phenomenon remain poorly understood. Here we formalized these theories into mathematical models and performed biological experiments to test them with empirical data. In syngeneic mice bearing two simultaneously implanted tumors, growth of only one of the tumors was significantly suppressed (61% size reduction at day 15, p<0.05). The competition model had to be rejected while the angiogenesis inhibition and proliferation inhibition models were able to describe the data. Additional models including a theory based on distant cytotoxic log-kill effects were unable to fit the data. The proliferation inhibition model was identifiable and minimal (4 parameters), and its descriptive power was validated against the data, including consistency in predictions of single tumor growth when no secondary tumor was present. This theory may also shed new light on single cancer growth insofar as it offers a biologically translatable picture of how local and global action may combine to control local tumor growth, and in particular, the role of tumor-tumor inhibition. This model offers a depiction of concomitant resistance that provides an improved theoretical basis for tumor growth control and may also find utility in therapeutic planning to avoid post-surgery metastatic acceleration.

cancer biology↗

bioRxiv2016

A Hypothesis to Explain Cancers in Confined Colonies of Naked Mole Rats

Naked mole rats (NMRs) are subterranean eusocial mammals, known for their virtual absence of aging in their first 20 to 30 years of life, and their apparent resistance to cancer development. As such, this species has become an important biological model for investigating the physiological and molecular mechanisms behind cancer resistance. Two recent studies have discovered middle and late-aged worker (that is, non-breeding) NMRs in captive populations exhibiting neoplasms, consistent with cancer development, challenging the claim that NMRs are cancer resistant. These cases are possibly artefacts of inbreeding or certain rearing conditions in captivity, but they are also consistent with evolutionary theory.\n\nWe present field data showing that worker NMRs live on average for 1 to 2 years. This, together with considerable knowledge about the biology of this species, provides the basis for an evolutionary explanation for why debilitating cancers in NMRs should be rare in captive populations and absent in the wild. Whereas workers are important for maintaining tunnels, colony defence, brood care, and foraging, they are highly vulnerable to predation. However, surviving workers either replace dead breeders, or assume other less active functions whilst preparing for possible dispersal. These countervailing forces (selection resulting in aging due to early-life investments in worker function, and selection for breeder longevity) along with the fact that all breeders derive from the worker morph, can explain the low levels of cancer observed by these recent studies in captive colonies. Because workers in the field typically never reach ages where cancer becomes a risk to performance or mortality, those rare observations of neoplastic growth should be confined to the artificial environments where workers survive to ages rarely if ever occurring in the wild. Thus, we predict that the worker phenotype fortuitously benefits from anti-aging and cancer protection in captive populations.

Cancer Biology↗

bioRxiv2018

Cross-cancer pleiotropic associations with lung cancer risk in African Americans

BackgroundIdentifying genetic variants with pleiotropic associations across multiple cancers can reveal shared biologic pathways. Prior pleiotropic studies have primarily focused on European descent individuals. Yet population-specific genetic variation can occur and potential pleiotropic associations among diverse racial/ethnic populations could be missed. We examined cross-cancer pleiotropic associations with lung cancer risk in African Americans.\n\nMethodsWe conducted a pleiotropic analysis among 1,410 African American lung cancer cases and 2,843 controls. We examined 36,958 variants previously associated (or in linkage disequilibrium) with cancer in prior genome-wide association studies. Logistic regression analyses were conducted, adjusting for age, sex, global ancestry, study site, and smoking status.\n\nResultsWe identified three novel genomic regions significantly associated (FDR-corrected p-value < 0.10) with lung cancer risk (rs336958 on 5q14.3, rs7186207 on 16q22.2, and rs11658063 on 17q12). On chromosome16q22.2, rs7186207 was significantly associated with increased risk (OR=1.24, 95% CI: 1.12-1.38) and functional annotation using GTEx showed rs7186207 modifies DHODH gene expression. The risk allele at rs336958 on 5q14.3 was associated with reduced lung cancer risk (OR=0.68, 95% CI: 0.56-0.82), while the risk allele at rs11658063 on 17q12 was associated with increased risk (OR=1.24, 95% CI: 1.11-1.39).\n\nConclusionWe identified novel associations on chromosomes 5q14.3, 16q22.2, and 17q12, which contain HNF1B, DHODH, and HAPLN1 genes, respectively. SNPs within these regions have been previously associated with multiple cancers. This is the first study to examine cross-cancer pleiotropic associations for lung cancer in African Americans.\n\nImpactOur findings demonstrate novel cross-cancer pleiotropic associations with lung cancer risk in African Americans.

genetics↗

bioRxiv2017

Massive expression of germ cell specific genes is a hallmark of cancer and a potential target for novel treatment development

Cancer cells have been found to frequently express genes that are normally restricted to the testis, often referred to as cancer/testis (CT) antigens or genes1, 2. Because germ cell specific antigens are not recognized as \"self\" by the innate immune system3, CT-genes have previously been suggested as ideal candidate targets for cancer therapy4. The use of CT- genes in cancer therapy has thus far been unsuccessful, most likely because their identification has relied on gene expression in whole testis, including the testicular somatic cells, precluding the detection of true germ cell specific genes. By comparing the transcriptomes of micro-dissected germ cell subtypes, representing the main developmental stages of human spermatogenesis5, with the publicly accessible transcriptomes of 2.617 samples from 49 different healthy somatic tissues6 and 9.232 samples from 33 tumor types7, we here discover hundreds of true germ cell specific cancer expressed genes. Strikingly, we found these germ cell cancer genes (GC-genes) to be widely expressed in all analyzed tumors. Many GC-genes appeared to be involved in processes that are likely to actively promote tumor viability, proliferation and metastasis. Targeting these true GC-genes thus has the potential to inhibit tumor growth with infertility being the only possible side effect. Moreover, we identified a subset of GC-genes that are not expressed in spermatogonial stem cells. Targeting of this GC-gene subset is predicted to only lead to temporary infertility, as untargeted spermatogonial stem cells can recover spermatogenesis after treatment. Our GC- gene dataset enables improved understanding of tumor biology and provides multiple novel targets for cancer treatment.

cancer biology↗

bioRxiv2018

Condition-adaptive fused graphical lasso (CFGL): an adaptive procedure for inferring condition-specific gene co-expression network

Co-expression network analysis provides useful information for studying gene regulation in biological processes. Examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. One challenge in this type of analysis is that the sample sizes in each condition are usually small, making the statistical inference of co-expression patterns highly underpowered. A joint network construction that borrows information from related structures across conditions has the potential to improve the power of the analysis.\n\nOne possible approach to constructing the co-expression network is to use the Gaussian graphical model. Though several methods are available for joint estimation of multiple graphical models, they do not fully account for the heterogeneity between samples and between co-expression patterns introduced by condition specificity. Here we develop the condition-adaptive fused graphical lasso (CFGL), a data-driven approach to incorporate condition specificity in the estimation of co-expression networks. We show that this method improves the accuracy with which networks are learned. The application of this method on a rat multi-tissue dataset and The Cancer Genome Atlas (TCGA) breast cancer dataset provides interesting biological insights. In both analyses, we identify numerous modules enriched for Gene Ontology functions and observe that the modules that are upregulated in a particular condition are often involved in condition-specific activities. Interestingly, we observe that the genes strongly associated with survival time in the TCGA dataset are less likely to be network hubs, suggesting that genes associated with cancer progression are likely to govern specific functions, rather than regulating a large number of biological processes. Additionally, we observed that the tumor-specific hub genes tend to have few shared edges with normal tissue, revealing tumor-specific regulatory mechanism.\n\nAuthor summaryGene co-expression networks provide insights into the mechanism of cellular activity and gene regulation. Condition-specific mechanisms may be identified by constructing and comparing co-expression networks of multiple conditions. We propose a novel statistical method to jointly construct co-expression networks for gene expression profiles from multiple conditions. By using a data-driven approach to capture condition-specific co-expression patterns, this method is effective in identifying both co-expression patterns that are specific to a condition and that are common across conditions. The application of this method on real datasets reveals interesting biological insights.

bioinformatics↗

bioRxiv2015

Hilbert-Schmidt and Sobol sensitivity indices for static and time series Wnt signaling measurements in colorectal cancer - Part A

Ever since the accidental discovery of Wingless [Sharma R.P., Drosophila information service, 1973, 50, p 134], research in the field of Wnt signaling pathway has taken significant strides in wet lab experiments and various cancer clinical trials augmented by recent developments in advanced computational modeling of the pathway. Information rich gene expression profiles reveal various aspects of the signaling pathway at work and help in studying different issues simultaneously. Hitherto, not many computational studies exist which incorporate the simultaneous study of these issues. This manuscript is an endeavour to * explore the strength of contributing factors in the signaling pathway, * analyze the existing causal relations among the inter/extracellular factors effecting the pathway based on prior biological knowledge and * investigate the deviations in fold changes in the recently found prevalence of psychophysical laws working in the pathway. To achieve this goal, local and global sensitivity analysis is conducted on the (non)linear responses between the factors obtained from static and time series expression profiles using the density (Hilbert-Schmidt Information Criterion) and variance (Sobol) based sensitivity indices. The results show the superiority of the density based indices in comparison to the use of variance based indices mainly due to the former's employment of distance measures using the kernel trick via Reproducing kernel Hilbert space (RKHS) that capture nonlinear relations among various intra/extracellular factors of the pathway in a higher dimensional space. In time series data, using these indices it is now possible to observe where in time, which factors get influenced & contribute to the pathway, as changes in concentration of the other factors are made. This synergy of prior biological knowledge, sensitivity analysis and representations in higher dimensional spaces can facilitate in time based administration of target therapeutic drugs and reveal hidden biological information within colorectal cancer samples. Code has been made available at Google drive on https://drive.google.com/folderview?id=0B7Kkv8wlhPU-Q2NBZGt1ZERrSVE&usp=sharing

Systems Biology↗

bioRxiv2018

ClusterMine: a Knowledge-integrated Clustering Approach based on Expression Profiles of Gene Sets

MotivationClustering analysis is essential for understanding complex biological data. In widely used methods such as hierarchical clustering (HC) and consensus clustering (CC), expression profiles of all genes are often used to assess similarity between samples for clustering. These methods output sample clusters, but are not able to provide information about which gene sets (functions) contribute most to the clustering. So interpretability of their results is limited. We hypothesized that integrating prior knowledge of annotated biological processes would not only achieve satisfying clustering performance but also, more importantly, enable potential biological interpretation of clusters.\n\nResultsHere we report ClusterMine, a novel approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets, e.g., in Gene Ontology. In addition to outputting cluster membership of each sample as conventional approaches do, it outputs gene sets that are most likely to contribute to the clustering, a feature facilitating biological interpretation. Using three cancer datasets, two single cell RNA-sequencing based cell differentiation datasets, one cell cycle dataset and two datasets of cells of different tissue origins, we found that ClusterMine achieved similar or better clustering performance and that top-scored gene sets prioritized by ClusterMine are biologically relevant.\n\nImplementation and availabilityClusterMine is implemented as an R package and is freely available at: www.genemine.org/clustermine.php\n\nContactjxwang@csu.edu.cn\n\nSupplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2015

From concept to clinic: mathematically informed immunotherapy

Introduction Introduction Mathematical oncology Immunotherapy Modeling tumor-immune... Modeling treatment outcomes Adoptive T-cell and cytokine... Dendritic cell vaccines Preventative vaccines Cancer-specific treatments Combination therapies Discussion Summary References Mathematical modeling has become a valuable tool in the continued effort to understand, predict and ultimately treat a wide range of cancers in recent years [1,2]. By describing biological phenomena in the concise and formal language of mathematics, it is possible to elucidate key components of complex systems and ultimately develop tools capable of quantifying and ...

Cancer Biology↗

bioRxiv2016

IMPORT AND EXPORT OF GOLD NANOPARTICLES: EXCHANGE RATE IN CANCER CELLS AND FIBROBLASTS

Cancer is one of the leading causes of death. Radiation therapy is an important modality used in cancer treatment being highly cost-effective. Major flaw of radiotherapy is lack of selectivity between cancerous and healthy tissues. Amelioration of radiotherapy by using high-Z nanoparticles as radiation enhancers is one of potential solutions. Gold nanoparticles (AuNPs) are commonly used as radioenhancers. Understanding the interaction between cancer cells and AuNPs is essential in order to achieve best possible radioenhancing effects, while sparing healthy tissues. This work aims to elucidate interactions of ultrasmall (core size: 2.4 nm and hydrodynamic diameter (Dh): 4.5 nm) fluorescently labeled AuNPs with various human cell lines. In this perspective we measured uptake dynamics, characterized route of internalization and time of intracellular retention in various cancer cell lines and fibroblasts. Our results show that uptake dynamics and internalization pathways are strongly cell line-dependant. We also demonstrate that higher proportion of internalized nanoparticles resides in cancer cells, compared to fibroblasts, in in vitro conditions. This work highlights great complexity of cancerous cells and underlines the necessity for excellent knowledge of biological behaviour for each type of cancer. It also emphasizes the major effort needed for efficient cancer treatments and makes an appeal for further development of highly selective nanoparticles in order to hasten their utilization in clinical conditions.

cell biology↗