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Results for “Cancer Biology”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2016

Metabolomic-based biomarker discovery for non-invasive lung cancer screening: A case study

BackgroundLung cancer (LC) is one of the leading lethal cancers worldwide, with an estimated 18.4% of all cancer deaths being attributed to the disease. Despite developments in cancer diagnosis and treatment over the previous thirty years, LC has seen little to no improvement in the overall five year survival rate after initial diagnosis.\n\nMethodsIn this paper, we extended a recent study which profiled the metabolites in sputum from patients with lung cancer and age-matched volunteers smoking controls using flow infusion electrospray ion mass spectrometry. We selected key metabolites for distinguishing between different classes of lung cancer, and employed artificial neural networks and leave-one-out cross-validation to evaluate the predictive power of the identified biomarkers.\n\nResultsThe neural network model showed excellent performance in clas sification between lung cancer and control groups with the area under the receiver operating characteristic curve of 0.99. The sensitivity and specificity of for detecting cancer from controls were 96% and 94% respectively. Furthermore, we have identified six putative metabolites that were able todiscriminate between sputum samples derived from patients suffering small cell lung cancer (SCLC) and non-small cell lung cancer. These metabolites achieved excellent cross validation performance with a sensitivity of 80% and specificity of 100% for predicting SCLC.\n\nConclusionsThese results indicate that sputum metabolic profiling may have potential for screening of lung cancer and lung cancer recurrence, and may greatly improve effectiveness of clinical intervention.

Cancer Biology↗

bioRxiv2016

The Influence of Lymphoid Reconstitution Kinetics on Clinical Outcomes in Allogeneic Stem Cell Transplantation

Lymphoid recovery following myeloablative stem cell transplantation (SCT) displays a logistic pattern of exponential growth followed by a plateau. Within this logistic framework, lymphoid recovery is characterized by the parameters R (slope of ascent), a (time of maximal rate of ascent) and K (plateau), the steady state lymphocyte count. A retrospective analysis of allogeneic SCT performed from 2008 to 2013 was undertaken to compare lymphoid recovery and clinical outcomes in 131 patients with acute myelogenous leukemia, acute lymphocytic leukemia and myelodysplastic syndromes. Using Prism software, a logistic curve was successfully fit to the absolute lymphocyte count recovery in all patients. Patients were classified according to the magnitude and rate of lymphoid recovery; pattern A achieved an ALC of > 1000/uL by day 45; pattern B an ALC 500 < x < 1000 /uL and pattern C, an ALC <500/uL. Pattern A was characterized by a higher mean K (p < 0.0001) compared with patterns B & C. Patients with patterns B and C were more likely to have mixed T cell chimerism at ninety days following SCT (p=0.01). There was a trend towards improved survival, (and relapse-free survival) in those with pattern A and B at one year compared to pattern C (p = 0.073). There was no difference in cGVHD (p=0.42) or relapse (p=0.45) between pattern types. CMV, aGVHD and relapse all were heralded by deviation from logistic behavior. Pattern C patients were more likely to require donor lymphocyte infusion (p=0.017). Weaning of Tacrolimus post-transplant was associated with a second, separate logistic expansion in some patients. This study demonstrated that lymphoid reconstitution follows a prototypical logistic recovery and that pattern observed correlates with T cell chimerism and need for DLI, and may influence survival.

Cancer Biology↗

bioRxiv2016

Discovery of Cancer Driver Long Noncoding RNAs across 1112 Tumour Genomes: New Candidates and Distinguishing Features.

Long noncoding RNAs (lncRNAs) represent a vast unexplored genetic space that may hold missing drivers of tumourigenesis, but few such \"driver lncRNAs\" are known. Until now, they have been discovered through changes in expression, leading to problems in distinguishing between causative roles and passenger effects. We here present a different approach for driver lncRNA discovery using mutational patterns in tumour DNA. Our pipeline, ExInAtor, identifies genes with excess load of somatic single nucleotide variants (SNVs) across panels of tumour genomes. Heterogeneity in mutational signatures between cancer types and individuals is accounted for using a simple local trinucleotide background model, which yields high precision and low computational demands. We use ExInAtor to predict drivers from the GENCODE annotation across 1112 entire genomes from 23 cancer types. Using a stratified approach, we identify 15 high-confidence candidates: 9 novel and 6 known cancer-related genes, including MALAT1, NEAT1 and SAMMSON. Both known and novel driver lncRNAs are distinguished by elevated gene length, evolutionary conservation and expression. We have presented a first catalogue of mutated lncRNA genes driving cancer, which will grow and improve with the application of ExInAtor to future tumour genome projects.

Cancer Biology↗

bioRxiv2016

Cancer treatment scheduling and dynamic heterogeneity in social dilemmas of tumour acidity and vasculature

BackgroundTumours are diverse ecosystems with persistent heterogeneity in various cancer hallmarks like self-sufficiency of growth factor production for angiogenesis and reprogramming of energy-metabolism for aerobic glycolysis. This heterogeneity has consequences for diagnosis, treatment, and disease progression.\n\nMethodsWe introduce the double goods game to study the dynamics of these traits using evolutionary game theory. We model glycolytic acid production as a public good for all tumour cells and oxygen from vascularization via VEGF production as a club good benefiting non-glycolytic tumour cells. This results in three viable phenotypic strategies: glycolytic, angiogenic, and aerobic non-angiogenic.\n\nResultsWe classify the dynamics into three qualitatively distinct regimes: (1) fully glycolytic, (2) fully angiogenic, or (3) polyclonal in all three cell types. The third regime allows for dynamic heterogeneity even with linear goods, something that was not possible in prior public good models that considered glycolysis or growth-factor production in isolation.\n\nConclusionThe cyclic dynamics of the polyclonal regime stress the importance of timing for antiglycolysis treatments like lonidamine. The existence of qualitatively different dynamic regimes highlights the order effects of treatments. In particular, we consider the potential of vascular renormalization as a neoadjuvant therapy before follow up with interventions like buffer therapy.

Cancer Biology↗

bioRxiv2016

CIViC: A knowledgebase for expert-crowdsourcing the clinical interpretation of variants in cancer.

CIViC is an expert crowdsourced knowledgebase for Clinical Interpretation of Variants in Cancer (www.civicdb.org) describing the therapeutic, prognostic, and diagnostic relevance of inherited and somatic variants of all types. CIViC is committed to open source code, open access content, public application programming interfaces (APIs), and provenance of supporting evidence to allow for the transparent creation of current and accurate variant interpretations for use in cancer precision medicine.

Cancer Biology↗

bioRxiv2016

Collateral sensitivity networks reveal evolutionary instability and novel treatment strategies in ALK mutated non-small cell lung cancer.

Drug resistance remains an elusive problem in cancer therapy, particularly with novel targeted therapy approaches. Much work is currently focused upon the development of an increasing arsenal of targeted therapies, towards oncogenic driver genes such as ALK-EML4, to overcome the inevitable resistance that develops as therapies are continued over time. The current clinical paradigm after failure of first line ALK TKI is to administer another drug in the same class. As to which drug however, the answer is uncertain, as clinical evidence is lacking. To address this shortcoming, we evolved resistance in an ALK rearranged non-small cell lung cancer line (H3122) to a panel of 4 ALK tyrosine kinase in-hibitors used in clinic, and performed a collateral sensitivity analysis to each of the other drugs. We found that all of the ALK inhibitor resistant cell lines displayed a significant cross-resistance to all other ALK inhibitors. To test for the stability of the resistance phenotypes, we evaluated the ALK-inhibitor sensitivities after drug holidays of varying length (1, 3, 7, 14, and 21 days). We found the resistance patterns to be stochastic and dynamic, with few conserved patterns. This unpredictability led us to an expanded search for treatment options for resistant cells. In this expansion, we tested a panel of 6 more anti-cancer agents for collateral sensitivity among the resistant cells, uncovering a multitude of possibilities for further treatment, including cross-sensitivity to several standard cytotoxic therapies as well as the HSP-90 inhibitors. Taken together, these results imply that resistance to targeted therapy in non-small cell lung cancer is truly a moving target; but also one where there are many opportunities to re-establish sensitivities where there was once resistance.

Cancer Biology↗

bioRxiv2016

Reduced CREB3L2 expression is associated with resistance to sorafenib and poor prognosis in ER-positive breast cancer

Sorafenib is a multikinase inhibitor that acts by inhibiting tumour growth and disrupting tumour microvasculature through anti-proliferative, anti-angiogenic, and pro-apoptotic effects. However, development of resistance to sorafenib often prevents its long-term efficacy. No validated biomarkers currently exist for appropriately selecting patients with cancer for sorafenib treatment. In the present study, we report that CREB3L2 expression in human breast cancer cell lines is a marker of response to sorafenib. Analysis of human breast cancer cell lines using Oncomine database and Genomics of Drug Sensitivity in Cancer (GDSC) database revealed an association between reduced expression of CREB3L2 and sensitivity to sorafenib. Wet lab experiment in five human breast cancer cell lines confirmed the association between reduced expression of CREB3L2 and sensitivity to sorafenib. Further, reduced expression of CREB3L2 was associated with poor Reccurence Free Survival in Luminal breast cancer. Our results suggest that CREB3L2 expression is a biomarker of response to sorafenib and outcome in breast cancer.

Cancer Biology↗

bioRxiv2016

A molecular portrait of microsatellite instability across multiple cancers

Microsatellite instability (MSI) refers to the hypermutability of the cancer genome due to impaired DNA mismatch repair. Although MSI has been studied for decades, the large amount of sequencing data now available allows us to examine the molecular fingerprints of MSI in greater detail. Here, we analyze ~8000 exome and ~1000 whole-genome pairs across 23 cancer types. Our pan-cancer analysis reveals that the prevalence of MSI events is highly variable within and across tumor types including some in which MSI is not typically examined. We also identify genes in DNA repair and oncogenic pathways recurrently subject to MSI and uncover non-coding loci that frequently display MSI events. Finally, we propose an exomebased predictive model for the MSI phenotype that achieves high sensitivity and specificity. These results advance our understanding of the genomic drivers and consequences of MSI, and a comprehensive catalog of tumor-type specific MSI loci we have generated enables efficient panel-based MSI testing to identify patients who are likely to benefit from immunotherapy.

Cancer Biology↗

bioRxiv2016

E-scape: Interactive visualization of single cell phylogenetics and spatio-temporal evolution in cancer

Inference of clonal dynamics and tumour evolution has fundamental importance in understanding the major clinical endpoints in cancer: development of treatment resistance, relapse and metastasis. DNA sequencing technology has made measuring clonal dynamics through mutation analysis accessible at scale, facilitating computational inference of informative patterns of interest. However, currently no tools allow for biomedical experts to meaningfully interact with the often complex and voluminous dataset to inject domain knowledge into the inference process. We developed an interactive, web-based visual analytics software suite called E-scape which supports dynamically linked, multi-faceted views of cancer evolution data. Developed using R and javascript d3.js libraries, the suite includes three tools: TimeScape and MapScape for visualizing population dynamics over time and space, respectively, and CellScape for visualizing evolution at single cell resolution. The tool suite integrates phylogenetic, clonal prevalence, mutation and imaging data to generate intuitive, dynamically linked views of data which update in real time as a function of user actions. The system supports visualization of both point mutation and copy number alterations, rendering how mutations distribute in clones in both bulk and single cell experiment data in multiple representations including phylogenies, heatmaps, growth trajectories, spatial distributions and mutation tables. E-scape is open source and is freely available to the community at large.

cancer biology↗

bioRxiv2016

Cell Dynamics in the Wound Healing Process in Tumor Environment After Treatments

Although the failure of cancers treatments has been mostly linked with the existence of resistant cells or cancer stem cells, new findings show a significant correlation between circulating inflammatory biomarkers and treatment failures. Most cancer treatments cause necrotic cell deaths in the tumor microenvironment. Necrotic cells send signals to the immune cells to start the wound healing process in the tissue. Therefore, we assume after stopping treatments there is a wound that needs to be healed. The stochastic simulations of epithelial cell dynamics after a treatment, which only kills cells without changing the tumors inflammatory environment, show that higher fitness of cancer cells causes earlier relapses. Moreover, the tumor returns even if a single cancer cell with high fitness remains in the wounds boundary after such treatments. Although the involvement of cancer cells in the wound healing after treatments lead to the fast relapse, the cancer cells outside of the wound can also cause a slow recurrence of the tumor. Therefore, the absence of relapse after such treatments implies a slow-developing tumor that might not reach an observable size in the patients life time. Conversely, a large solid tumor in a young patient suggests the presence of high fitness cancer cells and therefore a high likelihood of relapse after conventional therapies. Additionally, the location of remaining cancer cells after treatments is a very important factor in the recurrence time. The fastest recurrence happens when a high fitness cancer cell is located in the middle of the wound. However, the longest time to recurrence corresponds to cancer cells located outside of the wounds boundary.

cancer biology↗

bioRxiv2016

Extrachromosal DNA elements can drive disease evolution in glioblastoma

To understand how genomic heterogeneity of glioblastoma (GBM) contributes to the poor response to therapy, which is characteristic of this disease, we performed DNA and RNA sequencing on GBM tumor samples and the neurospheres and orthotopic xenograft models derived from them. We used the resulting data set to show that somatic driver alterations including single nucleotide variants, focal DNA alterations, and oncogene amplification in extrachromosomal DNA (ecDNA) elements were in majority propagated from tumor to model systems. In several instances, ecDNAs and chromosomal alterations demonstrated divergent inheritance patterns and clonal selection dynamics during cell culture and xenografting. Longitudinal patient tumor profiling showed that oncogenic ecDNAs are frequently retained after disease recurrence. Our analysis shows that extrachromosomal elements increase the genomic heterogeneity during tumor evolution of glioblastoma, independent of chromosomal DNA alterations.

cancer biology↗

bioRxiv2016

CancerGD: a resource for identifying and interpreting genetic dependencies in cancer

Genes whose function is selectively essential in the presence of cancer associated genetic aberrations represent promising targets for the development of precision therapeutics. Here we present CancerGD (www.cancergd.org), a resource that integrates genotypic profiling with large-scale loss-of-function genetic screens in tumor cell lines to identify such genetic dependencies. CancerGD provides tools for searching, visualizing, and interpreting these genetic dependencies through the integration of functional interaction networks.

cancer biology↗

bioRxiv2016

Mice deficient of Myc super-enhancer region reveal a differential control mechanism between normal and pathological growth

The gene desert upstream of the Myc oncogene on chromosome 8q24 contains susceptibility loci for several major forms of human cancer, including cancers of breast, prostate, and colon. The region shows high conservation between human and mouse and contains multiple MYC enhancers that are activated in tumor cells. However, the role of this region in normal development has not been addressed. Here we show that a 538 kb deletion of the entire MYC upstream super-enhancer region in mice results in 50 to 80% decrease in MYC expression in multiple tissues. The mice are viable and show no overt phenotype. However, they are resistant to tumorigenesis, and most normal cells isolated from them grow slowly in culture. Consistently, deletion of the 8q24 super-enhancer region perturbs Myc targets only in cultured cells, but not in vivo. These results reveal that only cells whose Myc activity is increased by serum or oncogenic driver mutations depend on the 8q24 super-enhancer region, and indicate that targeting the activity of this element is a promising strategy of cancer chemoprevention and therapy.

cancer biology↗

bioRxiv2016

High-risk human papillomavirus in oral cavity squamous cell carcinoma

PurposeThe prevalence of human papillomavirus (HPV) in oral cavity squamous cell carcinoma (OSCC) varies significantly based on assay sensitivity and patient geography. Accurate detection is essential to understand the role of HPV in disease prognosis and management of patients with OSCC.\n\nMethodsWe generated and integrated data from multiple analytes (HPV DNA, HPV RNA, and p16), assays (immunohistochemistry, PCR, qPCR and digital PCR) and molecular changes (somatic mutations and DNA methylation) from 153 OSCC patients to correlate p16 expression, HPV DNA, and HPV RNA with HPV incidence and patient survival.\n\nResultsHigh prevalence (33-58%) of HPV16/18 DNA did not correlate with the presence of transcriptionally active viral genomes (15%) in tumors. Eighteen percent of the tumors were p16 positive. and only 6% were both HPV DNA and RNA positive. Most tumors with relatively high-copy HPV DNA, and/or HPV RNA, but not with HPV DNA alone (irrespective of copy number), were wild-type for TP53 and CASP8 genes. In our study, p16 protein, HPV DNA and HPV RNA, either alone or in combinations, did not correlate with patient survival. Nine HPV-associated genes stratified the virus +ve from the -ve tumor group with high confidence (p<0.008) when HPV DNA copy number and/or HPV RNA were considered to define HPV positivity and not HPV DNA alone irrespective of their copy number (p < 0.2).\n\nConclusionsIn OSCC, the presence of both HPV RNA and p16 are rare. HPV DNA alone is not an accurate measure of HPV positivity and therefore not informative. Moreover, HPV DNA, RNA or p16 dont correlate with outcome.

cancer biology↗

bioRxiv2016

Optimal structure of heterogeneous stem cell niche: The importance of cell migration in delaying tumorigenesis

Studying the stem cell niche architecture is a crucial step for investigating the process of oncogenesis and obtaining an effective stem cell therapy for various cancers. Recently, it has been observed that there are two groups of stem cells in the stem cell niche collaborating with each other to maintain tissue homeostasis. One group comprises the border stem cells, which is responsible to control the number of non-stem cells as well as stem cells. The other group, central stem cells, regulates the stem cell niche. In the present study, we develop a bi-compartmental stochastic model for the stem cell niche to study the spread of mutants within the niche. The analytic calculations and numeric simulations, which are in perfect agreement, reveal that in order to delay the spread of mutants in the stem cell niche, a small but non-zero number of stem cell proliferations must occur in the central stem cell compartment. Moreover, the migration of border stem cells to the central stem cell compartment delays the spread of mutants. Furthermore, the fixation probability of mutants in the stem cell niche is independent of types of stem cell division as long as all stem cells do not divide fully asymmetrically. Additionally, the progeny of central stem cells have a much higher chance than the progeny of border stem cells to take over the entire niche.

cancer biology↗

bioRxiv2016

Interplay of Driver, Mini-Driver, and Deleterious Passenger Mutations on Cancer Progression

Cancer is caused by the accumulation of a critical number of somatic mutations (drivers) that offer fitness advantage to tumor cells. Moderately deleterious passengers, suppressing cancer progression, and mini-drivers, mildly beneficial to tumors, can profoundly alter the cancer evolutionary landscape. This observation prompted us to develop a stochastic evolutionary model intended to probe the interplay of drivers, mini-drivers and deleterious passengers in tumor growth over a broad range of fitness values and mutation rates. Below a (small) threshold number of drivers tumor growth exhibits a plateau (dormancy) with large burst occurring when a driver achieves fixation, reminiscent of intermittency in dissipative dynamical systems. The predictions of the model, in particular the relationship between the average number of passenger mutations versus drivers in a tumor, is in accord with clinical data on several cancers. When deleterious drivers are included, we predict a non-monotonic growth of tumors as the mutation rate is varied with shrinkage and even reversal occurring at very large mutation rates. This surprising finding explains the paradoxical observation that high chromosomal instability (CIN) correlates with improved prognosis in a number of cancers compared with intermediate CIN.

cancer biology↗

bioRxiv2016

An evolving cancer instigates clonally unrelated neighboring cells to form distant metastases.

Based on the clonal evolution theory of cancer formation, a single cell within a tissue gains a cancer-driving mutation and thus a growth advantage. From this expanded cellular mass, another cell gains a new mutation allowing this newly mutated cell to gain new competitive advantage and to expand in number (thus clonal expansion). Another clone then emerges. Eventually all required mutations are gained, and a cancer forms. Consequently, while a primary lesion may harbor divergent subclones, all the subclones within the primary cancer as well as all metastatic growths in secondary organs share at least the very first oncogenic mutation that initiates the primary cancer. However, by tracking genetically marked mammary epithelial cells that suffered the initiating oncogenic mutation--and their neighboring mammary cells that did not-in several mouse models of human breast cancer, we found that genetically unrelated mammary epithelial cells can be colluded by neighboring mutated cells to disseminate, and that they can even undergo de novo tumorigenic transformation and form distant metastases. Therefore, clonally unrelated epithelial cells may contribute to cancer progression and to the heterogeneity of the systemic disease. The non-linear cancer spread has important implications in cancer prevention, treatment, and therapeutic resistance.

cancer biology↗

bioRxiv2016

Harnessing the lymphocyte meta-phenotype to optimize adoptive cell therapy

There is an urgent need for reliable effective therapy for patients with metastatic sarcoma. Approaches that manipulate the immune system have shown promise for patients with advanced, widely disseminated malignancies. One of these approaches is adoptive cell therapy (ACT), where tumor-infiltrating lymphocytes (TIL) are isolated from the tumor, expanded ex vivo, and then transferred back to the patient. This approach has shown great promise in melanoma, leading to an objective response in approximately half of treated patients [14]. Standard protocols involve characterization of TIL populations with respect to adaptive CD4+ and CD8+ T-lymphocytes, but neglect the possible role of the innate lymphoid repertoire. Due to toxicity and the high cost associated with ACT, the IFN-{gamma} release assay is currently used as a proxy to identify suitable TIL isolates for ACT. Efforts in TIL-ACT for sarcoma, which are pre-clinical and pioneered at Moffitt Cancer Center, have shown that only a minority of the TIL cultures show tumor specific activity in ex vivo IFN-{gamma} assays. Surprisingly, internal melanoma trial data reveal a lack of correlation between IFN-{gamma} assay and clinical outcomes, highlighting the need for a more reliable proxy. We hypothesize the existence of a predictable TIL meta-phenotype that leads to optimal tumor response. Here, we describe preliminary efforts to integrate prospective and existing patient data with mathematical models to optimize the TIL meta-phenotype prior to re-injection.

cancer biology↗