Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 62,709 bioRxiv papers from 278,266 authors.
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in category cancer biology
2,029 results found. For more information, click each entry to expand.
1,591 downloads cancer biology
Yilong Zou, Michael J Palte, Amy A Deik, Haoxin Li, John K Eaton, Wenyu Wang, Yuen-Yi Tseng, Rebecca Deasy, Maria Alimova, Vlado Dančík, Elizaveta S Leshchiner, Vasanthi S Viswanathan, Sabina Signoretti, Toni K Choueiri, Jesse S Boehm, Bridget K Wagner, John Doench, Clary B Clish, Paul A Clemons, Stuart L Schreiber
Kidney cancers are characterized by extensive metabolic reprogramming and resistance to a broad range of anti-cancer therapies. By interrogating the Cancer Therapeutics Response Portal compound sensitivity dataset, we show that cells of clear-cell renal cell carcinoma (ccRCC) possess a lineage-specific vulnerability to ferroptosis that can be exploited by inhibiting glutathione peroxidase 4 (GPX4). Using genome-wide CRISPR screening and lipidomic profiling, we reveal that this vulnerability is driven by the HIF-2α - HILPDA pathway by inducing a polyunsaturated fatty acyl (PUFA)-lipid-enriched cell state that is dependent on GPX4 for survival and susceptible to ferroptosis. This cell state is developmentally primed by the HNF-1β - 1-acylglycerol-3-phosphate O-acyltransferase 3 (AGPAT3) axis in the renal lineage. In addition to PUFA metabolism, ferroptosis is facilitated by a phospholipid flippase TMEM30A involved in membrane topology. Our study uncovers an oncogenesis-associated vulnerability, delineates the underlying mechanisms and suggests targeting GPX4 to induce ferroptosis as a therapeutic opportunity in ccRCC.
1,567 downloads cancer biology
Cell growth and division are stochastic processes that exhibit significant amount of cell-to-cell variation and randomness. In order to connect single cell division dynamics with overall cell population, stochastic population models are needed. We summarize the basic concepts, computational approaches and discuss simple applications of this modeling approach to understanding cancer cell population growth as well as population fluctuations in experiments.
1,557 downloads cancer biology
Entropy rising within normal hematopoiesis is the core idea of the proposed thermodynamical model of malignancy in leukemia. Mathematically its description is supposed to be similar to the Lorenz system of ordinary differential equations for simplified processes of heat flow in fluids. The model provides description of remission and relapse in leukemia as two hierarchical and qualitatively different states of normal hematopoiesis with their own phase spaces. Phase space transition is possible through pitchfork bifurcation, which is considered as the common symmetrical scenario for relapse, induced remission and spontaneous remission of leukemia. Cytopenia is regarded as an adaptive reaction of hematopoiesis to entropy increase caused by leukemia clone. The following hypotheses are formulated: a) Percentage of leukemia cells in marrow as criterion of remission or relapse is not necessarily constant but a variable value; b) Probability of getting remission depends upon normal hematopoiesis reaching bifurcation; c) Duration of remission depends upon eradication of leukemia cells in induction or consolidation therapies; d) Excessively high doses of chemotherapy in consolidation might induce relapse.
1,538 downloads cancer biology
Extracellular vesicles (EVs) are recognized cancer biomarkers, however, clinical analysis has been difficult due to a lack of simple and sensitive assays. Here, we describe a bead-enhanced flow cytometry method, BEAD flow, using biotinylated EVs captured on streptavidin particles. With this method, we show analysis of patient-derived EVs using a panel of pancreatic cancer biomarkers. BEAD flow is easily translatable to any biomarker or cancer type and can be run with conventional flow cytometers, making it highly flexible and adaptable to diverse research and clinical needs.
1,532 downloads cancer biology
Tumor-propagating glioblastoma (GBM) stem-like cells (GSCs) of the proneural and mesenchymal molecular subtypes have been described. However, it is unknown if these two GSC populations are sufficient to generate the spectrum of cellular heterogeneity observed in GBM. The lineage relationships and niche interactions of GSCs have not been fully elucidated. We perform single-cell RNA-sequencing (scRNA-seq) and matched exome sequencing of human GBMs (12 patients; >37,000 cells) to identify recurrent hierarchies of GSCs and their progeny. We map sequenced cells to tumor-anatomical structures and identify microenvironment interactions using reference atlases and quantitative immunohistochemistry. We find that all GSCs can be described by a single axis of variation, ranging from proneural to mesenchymal. Increasing mesenchymal GSC (mGSC) content, but not proneural GSC (pGSC) content, correlates with significantly inferior survival. All clonal expressed mutations are found in the GSC populations, with a greater representation of mutations found in mGSCs. While pGSCs upregulate markers of cell-cycle progression, mGSCs are largely quiescent and overexpress cytokines mediating the chemotaxis of myeloid-derived suppressor cells. We find mGSCs enriched in hypoxic regions while pGSCs are enriched in the tumor's invasive edge. We show that varying proportions of mGSCs, pGSCs, their progeny and stromal/immune cells are sufficient to explain the genetic and phenotypic heterogeneity observed in GBM. This study sheds light on a long-standing debate regarding the lineage relationships between GSCs and other glioma cell types.
1,521 downloads cancer biology
Ana C. deCarvalho, Hoon Kim, Laila M. Poisson, Mary E. Winn, Claudius Mueller, David Cherba, Julie Koeman, Sahil Seth, Alexei Protopopov, Michelle Felicella, Siyuan Zheng, Asha Multani, Yongying Jiang, Jianhua Zhang, Do-Hyun Nam, Emanuel F. Petricoin, Lynda Chin, Tom Mikkelsen, Roel GW Verhaak
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.
1,498 downloads cancer biology
Most cancers in humans are large, measuring centimeters in diameter, composed of many billions of cells. An equivalent mass of normal cells would be highly heterogeneous as a result of the mutations that occur during each cell division. What is remarkable about cancers is their homogeneity - virtually every neoplastic cell within a large cancer contains the same core set of genetic alterations, with heterogeneity confined to mutations that have emerged after the last clonal expansions. How such clones expand within the spatially-constrained three dimensional architecture of a tumor, and come to dominate a large, pre-existing lesion, has never been explained. We here describe a model for tumor evolution that shows how short-range migration and cell turnover can account for rapid cell mixing inside the tumor. With it, we show that even a small selective advantage of a single cell within a large tumor allows the descendants of that cell to replace the precursor mass in a clinically relevant time frame. We also demonstrate that the same mechanisms can be responsible for the rapid onset of resistance to chemotherapy. Our model not only provides novel insights into spatial and temporal aspects of tumor growth but also suggests that targeting short range cellular migratory activity could have dramatic effects on tumor growth rates.
1,498 downloads cancer biology
Anand Vasudevan, Prasamit S. Baruah, Joan C. Smith, Zihua Wang, Nicole M. Sayles, Peter Andrews, Jude Kendall, Justin E. Leu, Narendra Kumar Chunduri, Dan Levy, Michael Wigler, Zuzana Storchová, Jason M. Sheltzer
Most human tumors display chromosome-scale copy number alterations, and high levels of aneuploidy are frequently associated with advanced disease and poor patient prognosis. To examine the relationship between aneuploidy and cancer progression, we generated and analyzed a series of congenic human cell lines that harbor single extra chromosomes. We find that different aneuploidies can have distinct effects on invasive behavior: across 13 different cell lines, 12 trisomies suppressed invasiveness or were largely neutral, while a single trisomy increased metastatic behavior by triggering a partial epithelial-mesenchymal transition. In contrast, chromosomal instability, which can lead to the development of aneuploidy, uniformly suppressed cellular invasion. By analyzing genomic copy number and survival data from 10,133 cancer patients, we demonstrate that specific aneuploidies are associated with distinct clinical outcomes, and the acquisition of certain aneuploidies is in fact linked with a favorable prognosis. Thus, aneuploidy is not a uniform driver of malignancy, and different chromosome copy number changes can uniquely influence tumor progression. At the same time, the gain of a single chromosome is capable of inducing a profound cell state transition, underscoring how genomic plasticity can engender phenotypic plasticity and lead to the acquisition of enhanced metastatic properties.
1,497 downloads cancer biology
Joshua M Dempster, Clare Pacini, Sasha Pantel, Fiona M Behan, Thomas Green, John Krill-Burger, Charlotte M Beaver, Scott T Younger, Victor Zhivich, Hanna Najgebauer, Felicity Allen, Emanuel Gonçalves, Rebecca Shepherd, John G. Doench, Kosuke Yusa, Francisca Vazquez, Leopold Parts, Jesse S Boehm, Todd R. Golub, William C Hahn, David E Root, Mathew J Garnett, Francesco Iorio, Aviad Tsherniak
Genome-scale CRISPR-Cas9 viability screens performed in cancer cell lines provide a systematic approach to identify cancer dependencies and new therapeutic targets. As multiple large-scale screens become available, a formal assessment of the reproducibility of these experiments becomes necessary. We analyzed data from recently published pan-cancer CRISPR-Cas9 screens performed at the Broad and Sanger institutes. Despite significant differences in experimental protocols and reagents, we found that the screen results are highly concordant across multiple metrics with both common and specific dependencies jointly identified across the two studies. Furthermore, robust biomarkers of gene dependency found in one dataset are recovered in the other. Through further analysis and replication experiments at each institute, we found that batch effects are driven principally by two key experimental parameters: the reagent library and the assay length. These results indicate that the Broad and Sanger CRISPR-Cas9 viability screens yield robust and reproducible findings.
1,494 downloads cancer biology
Variation in the gut microbiome has been linked to colorectal cancer (CRC), as well as to host genetic variation. However, we do not know whether, in addition to baseline host genetics, somatic mutational profiles in CRC tumors interact with the surrounding tumor microbiome, and if so, whether these changes can be used to understand microbe-host interactions with potential functional biological relevance. Here, we characterized the association between CRC microbial communities and tumor mutations using microbiome profiling and whole-exome sequencing in 44 pairs of tumors and matched normal tissues. We found statistically significant associations between loss-of-function mutations in tumor genes and shifts in the abundances of specific sets of bacterial taxa, suggestive of potential functional interaction. This correlation allows us to statistically predict interactions between loss-of-function tumor mutations in cancer-related genes and pathways, including MAPK and Wnt signaling, solely based on the composition of the microbiome. These results can serve as a starting point for fine-grained exploration of the functional interactions between discrete alterations in tumor DNA and proximal microbial communities in CRC. In addition, these findings can lead to the development of improved microbiome-based CRC screening methods, as well as individualized microbiota-targeting therapies.
1,493 downloads cancer biology
Whole-chromosome aneuploidy is a hallmark of human malignancies. The prevalence of chromosome segregation errors in cancer - first noted more than 100 years ago - has led to the widespread belief that aneuploidy plays a crucial role in tumor development. Here, we set out to test this hypothesis. We transduced congenic euploid and trisomic fibroblasts with 14 different oncogenes or oncogene combinations, thereby creating genetically-matched cancer cell lines that differ only in karyotype. Surprisingly, nearly all aneuploid cell lines divided slowly in vitro, formed few colonies in soft agar, and grew poorly as xenografts, relative to matched euploid lines. Similar results were obtained when comparing a near-diploid human colorectal cancer cell line with derivatives of that line that harbored extra chromosomes. Only a few aneuploid lines grew at close to wild-type levels, and no aneuploid line exhibited greater tumorigenic capabilities than its euploid counterpart. These results demonstrate that rather than promoting tumorigenesis, aneuploidy, particularly single chromosome gains, can very often function as a tumor suppressor. Moreover, our results suggest one potential way that cancers can overcome the tumor suppressive effects of aneuploidy: rapidly-growing aneuploid cell lines that had evolved in vitro or in vivo demonstrated recurrent karyotype changes that were absent from their euploid counterparts. Thus, the genome-destabilizing effects of single-chromosome aneuploidy may facilitate the development of balanced, high-complexity karyotypes that are frequently found in advanced malignancies.
1,491 downloads cancer biology
The analysis of cell-free DNA (cfDNA) in plasma represents a rapidly advancing field in medicine. cfDNA consists predominantly of nucleosome-protected DNA shed into the bloodstream by cells undergoing apoptosis. We performed whole-genome sequencing (WGS) of plasma DNA and identified two discrete regions at transcription start sites (TSS) where the nucleosome occupancy results in different read-depth coverage patterns in expressed and silent genes. By employing machine learning for gene classification, we found that the plasma DNA read depth patterns from healthy donors reflected the expression signature of hematopoietic cells. In cancer patients with metastatic disease, we were able to classify expressed cancer driver genes in regions with somatic copy number gains with high accuracy. We could even determine the expressed isoform of genes with several TSSs as confirmed by RNA-Seq of the matching primary tumor. Our analyses provide functional information about the cells releasing their DNA into the circulation.
1,487 downloads cancer biology
Pancreatic cancers are typically diagnosed at late stage where disease prognosis is poor as exemplified by a 5-year survival rate of 8.2%. Earlier diagnosis would be beneficial by enabling surgical resection or earlier application of therapeutic regimens. We investigated the detection of pancreatic ductal adenocarcinoma (PDAC) in a non-invasive manner by interrogating changes in 5-hydroxymethylation cytosine status (5hmC) of circulating cell free DNA in the plasma of a PDAC cohort (n=51) in comparison with a non-cancer cohort (n=41). We found that 5hmC sites are enriched in a disease and stage specific manner in eons, 3'UTRs, transcription termination sites. Our data show that the 5hmC density in H3K4me3 sites is reduced in progressive disease suggesting increased transcriptional activity. 5hmC density is differentially represented in thousands of genes, and a stringently filtered set of the most significant genes exhibited biology related to pancreas (GATA4, GATA6, PROX1, ONECUT1) and/or cancer development (YAP1, TEAD1, PROX1, ONECUT1, ONECUT2, IGF1 and IGF2). Regularized regression models were built using 5hmC densities in statistically filtered genes or a comprehensive set of highly variable gene counts and performed with an AUC = 0.94-0.96 on training data. We were able to test the ability to classify PDAC and non-cancer samples with the elastic net and lasso models on two external pancreatic cancer 5hmC data sets and found validation performance to be AUC = 0.74-0.97. The findings suggest that 5hmC changes enable classification of PDAC patients with a high fidelity and are worthy of further investigation on larger cohorts of patient samples.
1,468 downloads cancer biology
Johanna Klughammer, Barbara Kiesel, Thomas Roetzer, Nikolaus Fortelny, Amelie Kuchler, Nathan C. Sheffield, Paul Datlinger, Nadine Peter, Karl-Heinz Nenning, Julia Furtner, Martha Nowosielski, Marco Augustin, Mario Mischkulnig, Thomas Ströbel, Patrizia Moser, Christian F. Freyschlag, Johannes Kerschbaumer, Claudius Thomé, Astrid E. Grams, Günther Stockhammer, Melitta Kitzwoegerer, Stefan Oberndorfer, Franz Marhold, Serge Weis, Johannes Trenkler, Johanna Buchroithner, Josef Pichler, Johannes Haybaeck, Stefanie Krassnig, Kariem Madhy Ali, Gord von Campe, Franz Payer, Camillo Sherif, Julius Preiser, Thomas Hauser, Peter A. Winkler, Waltraud Kleindienst, Franz Würtz, Tanisa Brandner-Kokalj, Martin Stultschnig, Stefan Schweiger, Karin Dieckmann, Matthias Preusser, Georg Langs, Bernhard Baumann, Engelbert Knosp, Georg Widhalm, Christine Marosi, Johannes A. Hainfellner, Adelheid Woehrer, Christoph Bock
Glioblastoma is characterized by widespread genetic and transcriptional heterogeneity, yet little is known about the role of the epigenome in glioblastoma disease progression. Here, we present genome-scale maps of the DNA methylation dynamics in matched primary and recurring glioblastoma tumors, based on a national population registry and a comprehensively annotated clinical cohort. We demonstrate the feasibility of DNA methylation mapping in a large set of routinely collected formalin-fixed paraffin-embedded (FFPE) samples, and we validate bisulfite sequencing as a multi-purpose assay that allowed us to infer a range of different genetic, epigenetic, and transcriptional tumor characteristics. Based on these data, we identified characteristic differences between primary and recurring tumors, links between DNA methylation and the tumor microenvironment, and an association of epigenetic tumor heterogeneity with patient survival. In summary, this study provides a resource for dissecting DNA methylation heterogeneity in genetically diverse and heterogeneous tumors, and it demonstrates the feasibility of integrating epigenomics, radiology, and digital pathology in a representative national cohort, leveraging samples and data collected as part of routine clinical practice.
1,465 downloads cancer biology
Allen W Zhang, Andrew McPherson, Katy Milne, David R. Kroeger, Phineas T Hamilton, Alex Miranda, Tyler Funnell, Sonya Laan, Dawn R. Cochrane, Jamie LP Lim, Winnie Yang, Andrew Roth, Maia A Smith, Camila de Souza, Julie Ho, Kane Tse, Thomas Zeng, Inna Shlafman, Michael R. Mayo, Richard Moore, Henrik Failmezger, Andreas Heindl, Yi Kan Wang, Ali Bashashati, Scott D. Brown, Daniel Lai, Adrian N. C. Wan, Cydney B. Nielsen, Alexandre Bouchard-Côté, Yinyin Yuan, Wyeth W. Wasserman, C. Blake Gilks, Anthony N. Karnezis, Samuel Aparicio, Jessica N. McAlpine, David G. Huntsman, Robert A Holt, Brad H Nelson, Sohrab P Shah
High-grade serous ovarian cancer exhibits extensive intratumoral heterogeneity coupled with widespread intraperitoneal disease. Despite this, metastatic spread of tumor clones is non-random, implying the existence of local microenvironmental factors that shape tumor progression. We interrogated the molecular interface between tumor-infiltrating lymphocytes (TIL) and cancer cells in 143 samples from 21 patients using whole-genome sequencing, immunohistochemistry, histologic image analysis, gene expression profiling, and T- and B-cell receptor sequencing. We identify 3 immunologic response categories, which frequently co-exist within individual patients. Furthermore, epithelial CD8+ TIL were inversely associated with malignant cell diversity, evidenced by subclonal neoepitope elimination and spatial tracking between tumor and T-cell clones. Intersecting mutational signatures and immune analysis showed that foldback inversion genomic aberrations lead to worse outcomes even in the presence of cytotoxic TIL (n=433). Thus, regional variation in immune contexture mirrors the pattern of intraperitoneal malignant spread, provoking new perspectives for treatment of this challenging disease.
1,462 downloads cancer biology
Breast cancer has long been classified into a number of molecular subtypes that predict prognosis and therefore influence clinical treatment decisions. Cellular heterogeneity is also evident in breast cancers and plays a key role in the development, evolution and metastatic progression of many cancers. How clinical heterogeneity relates to cellular heterogeneity is poorly understood, so we approached this question using single cell gene expression analysis of well established in vitro and in vivo models of disease. To explore the cellular heterogeneity in breast cancer we first examined a panel of genes that define the PAM50 classifier of molecular subtype. Five breast cancer cell line models (MCF7, BT474, SKBR3, MDA-MB-231, and MDA-MB-468) were selected as representatives of the intrinsic molecular subtypes (luminal A and B, basal-like, and Her2-enriched). Single cell multiplex RT-PCR was used to isolate and quantify the gene expression of single cells from each of these models, and the PAM50 classifier applied. Using this approach, we identified heterogeneity of intrinsic subtypes at single-cell level, indicating that cells with different subtypes exist within a cell line. Using the Chromium 10X system, this study was extended into thousands of cells from the MCF7 cell-line and an ER+ patient derived xenograft (PDX) model and again identified significant intra-tumour heterogeneity of molecular subtype. Estrogen Receptor (ER) is an important driver and therapeutic target in many breast cancers. It is heterogeneously expressed in a proportion of clinical cases but the significance of this to ER activity is unknown. Significant heterogeneity in the transcriptional activation of ER regulated genes was observed within tumours. This differential activation of the ER cistrome aligned with expression of two known transcriptional co-regulatory factors of ER (FOXA1 and PGR). To examine the degree of heterogeneity for other important phenotypic traits, we used an unsupervised clustering approach to identify cellular sub-populations with diverse cancer associated transcriptional properties, such as: proliferation; hypoxia; and treatment resistance. In particular, we show that we can identify two distinct sub-populations of cells that may have de-novo resistance to endocrine therapies in a treatment naive PDX model of ER+ breast cancer. One of these consists of cells with a non-proliferative transcriptional phenotype that is enriched for transcriptional properties of ERBB2 tumours. The other is heavily enriched for components of the primary cilia. Gene regulatory networks were used to identify transcription factor regulons that are active in each cell, leading us to identify potential transcriptional drivers (such as E2F7, MYB and RFX3) of the cilia associated endocrine resistant cells. This rare subpopulation of cells also has a highly heterogenous mix of intrinsic subtypes highlighting a potential role of intra-tumour subtype heterogeneity in endocrine resistance and metastatic potential. Overall, These results suggest a high degree of cellular heterogeneity within breast cancer models, even cell lines, that can be functionally dissected into sub-populations of cells with transcriptional phenotypes of potential clinical relevance.
1,452 downloads cancer biology
Cancer cell metabolism is heavily influenced by microenvironmental factors, including nutrient availability. Therefore, knowledge of microenvironmental nutrient levels is essential to understand tumor metabolism. To measure the extracellular nutrient levels available to tumors, we developed a quantitative metabolomics method to measure the absolute concentrations of >118 metabolites in plasma and tumor interstitial fluid, the extracellular fluid that perfuses tumors. Comparison of nutrient levels in tumor interstitial fluid and plasma revealed that the nutrients available to tumors differ from those present in circulation. Further, by comparing interstitial fluid nutrient levels between autochthonous and transplant models of murine pancreatic and lung adenocarcinoma, we found that tumor type, anatomical location and animal diet affect local nutrient availability. These data provide a comprehensive characterization of the nutrients present in the tumor microenvironment of widely used models of lung and pancreatic cancer and identify factors that influence metabolite levels in tumors.
1,445 downloads cancer biology
Autophagy and senescence are both well-established responses to chemotherapy and radiation that often occur in parallel, contributing to growth arrest in tumor cells. However, it has not been established whether this growth arrest is reversible. This question was addressed using non-small cell lung cancer models exposed to the cancer chemotherapeutic drug, etoposide. Senescent cells that were sorted, identified by β-galactosidase staining and alterations in morphology, isolated by flow cytometric cell sorting based on C12FDG staining, and real-time live microscopy were found to be capable of recovering proliferative capacity. Autophagy, monitored by vacuole formation, SQSTM1/p62 degradation, and LC3BII generation did not interfere with either the senescence arrest or proliferative recovery and was nonprotective in function (i.e. autophagy inhibition via both pharmacological and genetic strategies had negligible impact on the response to etoposide). These observations argue against the premise that (chemotherapy-induced) senescence is irreversible and indicate that therapy-induced senescence may ultimately be a transient process in that at least a subpopulation of tumor cells can and will remain metabolically active and recover proliferative capacity independently of autophagic turnover. We therefore propose that dormant tumor cells may be capable of prolonged survival in a state of autophagy/senescence and that disease recurrence may reflect escape from this senescence-arrested state.
1,425 downloads cancer biology
Heterogeneity in strategies for survival and proliferation among the cells which constitute a tumour is a driving force behind the evolution of resistance to cancer therapy. The rules mapping the tumour's strategy distribution to the fitness of individual strategies can be represented as an evolutionary game. We develop a game assay to measure effective evolutionary games in co-cultures of alectinib-sensitive and alectinib-resistant non-small cell lung cancer. The games are not only quantitatively different between different environments, but targeted therapy and cancer associated fibroblasts qualitatively switch the type of game being played by the in-vitro population from Leader to Deadlock. This observation provides the first direct empirical confirmation of a central theoretical postulate of evolutionary game theory in oncology: we can treat not only the player, but also the game. Although we concentrate on measuring games played by cancer cells, the measurement methodology we develop can be used to advance the study of games in other microscopic systems by providing a quantitative description of non-cell-autonomous effects.
1,421 downloads cancer biology
Although combination therapy has been a mainstay of cancer treatment for decades, it remains challenging, both to identify novel effective combinations of drugs and to determine the optimal combination for a particular patient's tumor. While there have been several recent efforts to test drug combinations in vitro, examining the immense space of possible combinations is far from being feasible. Thus, it is crucial to develop data-driven techniques to computationally identify the optimal drug combination for a patient. We introduce TreeCombo, an extreme gradient boosted tree-based approach to predict synergy of novel drug combinations, using chemical and physical properties of drugs and gene expression levels of cell lines as features. We find that TreeCombo significantly outperforms three other state-of-the-art approaches, including the recently developed DeepSynergy, which uses the same set of features to predict synergy using deep neural networks. Moreover, we found that the predictions from our approach were interpretable, with genes having well-established links to cancer serving as important features for prediction of drug synergy.
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