Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 64,934 bioRxiv papers from 287,775 authors.
Most downloaded bioRxiv papers, all time
in category cancer biology
2,109 results found. For more information, click each entry to expand.
1,318 downloads cancer biology
AM Frankell, Sriganesh Jammula, X Li, Gianmarco Contino, Sarah S Killcoyne, S Abbas, Juliane Perner, Lawrence Bower, Ginny Devonshire, E Ococks, N Grehan, J Mok, M O’Donovan, S MacRae, M Eldridge, S Tavare, RC Fitzgerald, the Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium
Esophageal Adenocarcinoma (EAC) is a poor prognosis cancer type with rapidly rising incidence. Our understanding of genetic events which drive EAC development is limited and there are few molecular biomarkers for prognostication or therapeutics. We have accumulated a cohort of 551 genomically characterised EACs (73% WGS and 27% WES) with clinical annotation and matched RNA-seq. Using a variety of driver gene detection methods, we discover 77 EAC driver genes (73% novel) and 21 non-coding driver elements (95% novel), and describe mutation and CNV types with specific functional impact. We identify a mean of 4.4 driver events per case derived from both copy number events and mutations. We compare driver mutation rates to the exome-wide mutational excess calculated using Non-synonymous vs Synonymous mutation rates (dNdS). We observe mutual exclusivity or co-occurrence of events within and between a number of EAC pathways (GATA factors, Core Cell cycle genes, TP53 regulators and the SWI/SNF complex) suggestive of important functional relationships. These driver variants correlate with tumour differentiation, sex and prognosis. Poor prognostic indicators (SMAD4, GATA4) are verified in independent cohorts with significant predictive value. Over 50% of EACs contain sensitising events for CDK4/6 inhibitors which are highly correlated with clinically relevant sensitivity in a panel EAC cell lines and organoids.
1,313 downloads cancer biology
Activating BRAF mutations are thought to drive melanoma tumorigenesis and metastasis by constitutively activating MEK and ERK. Small molecule inhibitors (SMIs) of BRAF or MEK have shown promise as melanoma therapeutics. However, the development of resistance to these inhibitors in both the short- and long-term is common; warranting investigation into how these SMIs influence ERK signaling dynamics. Quantitative single cell imaging of ERK activity in living cells reveals both intra- and inter-cell heterogeneity in this activity in isogenic melanoma populations harboring a BRAFV600E mutation. This heterogeneity is largely due to a cell-cycle dependent bifurcation of ERK activity. Moreover, we show there are also cell-cycle dependent responses in ERK activity following BRAF or MEK inhibition. Prior to, but not following, CDK4/6-mediated passage through the Restriction Point (RP) ERK activity is sensitive to BRAF and MEK inhibitors. In contrast, for cells that have passed the RP, ERK activity will remain elevated even in the presence of BRAF or MEK inhibition until mitosis. Our results show that ERK activity - even in the presence of activating BRAF mutations - is regulated by both positive and negative feedback loops that are engaged in cell-cycle dependent fashions. CDK4/6 inhibition sensitizes ERK activity to BRAF or MEK inhibition by preventing passage the transition from a BRAF/MEK dependent to independent state. Our results have implications for the use of MEK and BRAF inhibitors as melanoma therapeutics, and offer a rational basis for the use of these inhibitors in combination with CDK4/6 inhibition during cancer therapy.
1,310 downloads cancer biology
Genevieve Stein-O’Brien, Luciane T Kagohara, Sijia Li, Manjusha Thakar, Ruchira Ranaweera, Hiroyuki Ozawa, Haixia Cheng, Michael Considine, Sandra Schmitz, Alexander V Favorov, Ludmila V Danilova, Joseph A Califano, Evgeny Izumchenko, Daria A Gaykalova, Christine H Chung, Elana J. Fertig
BACKGROUND: Targeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model. RESULTS: An unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically. CONCLUSIONS: Genes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.
1,308 downloads cancer biology
Treatment of advanced cancers has benefited from new agents that supplement or bypass conventional therapies. However, even effective therapies fail as cancer cells deploy a wide range of resistance strategies. We propose that evolutionary dynamics ultimately determine survival and proliferation of resistant cells, therefore evolutionary strategies should be used with conventional therapies to delay or prevent resistance. Using an agent-based framework to model spatial competition among sensitive and resistant populations, we apply anti-proliferative drug treatments to varying ratios of sensitive and resistant cells. We compare a continuous maximum tolerated dose schedule with an adaptive schedule aimed at tumor control through competition between sensitive and resistant cells. We find that continuous treatment cures mostly sensitive tumors, but with any resistant cells, recurrence is inevitable. We identify two adaptive strategies that control heterogeneous tumors: dose modulation controls most tumors with less drug, while a more vacation-oriented schedule can control more invasive tumors.
1,307 downloads cancer biology
Immune heterogeneity within the tumor microenvironment undoubtedly adds several layers of complexity to our understanding of drug sensitivity and patient prognosis across various cancer types. Within the tumor microenvironment, immunogenicity is a favorable clinical feature in part driven by the antitumor activity of CD8+ T cells. However, tumors often inhibit this antitumor activity by exploiting the suppressive function of Regulatory T cells (Tregs), thus suppressing the adaptive immune response. Despite the seemingly intuitive immunosuppressive biology of Tregs, prognostic studies have produced contradictory results regarding the relationship between Treg enrichment and survival. We therefore analyzed RNA-seq data of Treg-enriched tumor samples to derive a pan-cancer gene signature able to help reconcile the inconsistent results of Treg studies, by better understanding the variable clinical association of Tregs across alternative tumor contexts. We show that increased expression of a 32-gene signature in Treg-enriched tumor samples (n=135) is able to distinguish a cohort of patients associated with chemosensitivity and overall survival This cohort is also enriched for CD8+ T cell abundance, as well as the antitumor M1 macrophage subtype. With a subsequent validation in a larger TCGA pool of Treg-enriched patients (n = 626), our results reveal a gene signature able to produce unsupervised clusters of Treg-enriched patients, with one cluster of patients uniquely representative of an immunogenic tumor microenvironment. Ultimately, these results support the proposed gene signature as a putative biomarker to identify certain Treg-enriched patients with immunogenic tumors that are more likely to be associated with features of favorable clinical outcome.
1,297 downloads cancer biology
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. 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 predicting system behavior under given conditions. When these tools are applied as a complement to the detailed understanding of cancer biology provided by biological scientists and clinicians, new insights can be gained into the mechanisms and first-order principles of cancer development and control. To date, although mathematical tools have been applied extensively in understanding tumor growth and dynamic interactions between cancer and host, studies involving the theoretical modeling of patient response to treatment and the contribution of such findings to the development of clinically-actionable therapeutic protocols remain strikingly limited. In particular, despite the rising emergence of immunotherapy as a promising cancer treatment, knowledge gained from mathematical modeling of tumor-immune interactions often still eludes application to the clinic. The currently underutilized potential of such techniques to forecast response to treatment, aid the development of immunotherapeutic regimes and ultimately streamline the transition from innovative concept to clinical practice is hence the focus of this review.
1,295 downloads cancer biology
Ralph Francescone, Débora Barbosa Vendramini-Costa, Janusz Franco-Barraza, Jessica Wagner, Alexander Muir, Allison N Lau, Linara Gabitova, Tatiana Pazina, Sapna Gupta, Tiffany Luong, Neelima Shah, Dustin Rollins, Ruchi Malik, Roshan Thapa, Diana Restifo, Yan Zhou, Kathy Q Cai, Harvey H Hensley, Yinfei Tan, Warren D Kruger, Karthik Devarajan, Siddharth Balachandran, Andres J Klein-Szanto, Wafik S. El-Deiry, Matthew G Vander Heiden, Suraj Peri, Kerry S. Campbell, Igor Astsaturov, Edna Cukierman
Pancreatic ductal adenocarcinoma (PDAC) has a poor 5-year survival rate and lacks effective therapeutics. Therefore, it is of paramount importance to identify new targets. Using multi-plex data from patient tissue, three-dimensional co-culturing in vitro assays, and orthotopic murine models, we identified Netrin G1 (NetG1) and Netrin G1 ligand (NGL-1) as promoters of PDAC tumorigenesis. NetG1+ cancer-associated fibroblasts (CAFs) supported PDAC survival, through a NetG1/NGL-1 mediated effect on glutamate/glutamine metabolism. NetG1+ CAFs were intrinsically immunosuppressive and inhibited NK cell mediated killing of tumor cells. These functions were partially mediated by vesicular glutamate transporter 1 and glutamine synthetase. This study uncovered an important link between CAF driven metabolism and its immunosuppressive capacity, suggesting NetG1 and NGL-1 as potential targets in PDAC.
1,289 downloads cancer biology
Bo Tu, Jun Yao, Sammy Ferri-Borgogno, Jun Zhao, Shujuan Chen, Qiuyun Wang, Liang Yan, Xin Zhou, Cihui Zhu, Seungmin Bang, Qing Chang, Christopher A. Bristow, Ya’an Kang, Hongwu Zheng, Huamin Wang, Jason B. Fleming, Michael Kim, Timothy P. Heffernan, Giulio F. Draetta, Duojia Pan, Anirban Maitra, Wantong Yao, Sonal Gupta, Haoqiang Ying
Transcriptomic profiling classifies pancreatic ductal adenocarcinoma (PDAC) into several molecular subtypes with distinctive histological and clinical characteristics. However, little is known about the molecular mechanisms that define each subtype and their correlation with clinical outcome. Mutant KRAS is the most prominent driver in PDAC, present in over 90% of tumors, but the dependence of tumors on oncogenic KRAS signaling varies between subtypes. In particular, squamous subtype are relatively independent of oncogenic KRAS signaling and typically display much more aggressive clinical behavior versus progenitor subtype. Here, we identified that YAP1 activation is enriched in the squamous subtype and associated with poor prognosis. Activation of YAP1 in progenitor subtype cancer cells profoundly enhanced malignant phenotypes and transformed progenitor subtype cells into squamous subtype. Conversely, depletion of YAP1 specifically suppressed tumorigenicity of squamous subtype PDAC cells. Mechanistically, we uncovered a significant positive correlation between WNT5A expression and the YAP1 activity in human PDAC, and demonstrated that WNT5A overexpression led to YAP1 activation and recapitulated YAP1-dependent but Kras-independent phenotype of tumor progression and maintenance. Thus, our study identifies YAP1 oncogene as a major driver of squamous subtype PDAC and uncovers the role of WNT5A in driving PDAC malignancy through activation of the YAP pathway.
1,272 downloads cancer biology
Stephen J. Pettitt, Dragomir B. Krastev, Inger Brandsma, Amy Drean, Feifei Song, Radoslav Aleksandrov, Maria I. Harrell, Malini Menon, Rachel Brough, James Campbell, Jessica Frankum, Michael Ranes, Helen N. Pemberton, Rumana Rafiq, Kerry Fenwick, Amanda Swain, Sebastian Guettler, Jung-Min Lee, Elizabeth M. Swisher, Stoyno Stoynov, Kosuke Yusa, Alan Ashworth, Christopher J. Lord
PARP inhibitors (PARPi) target homologous recombination defective tumour cells via synthetic lethality. Genome-wide and high-density CRISPR-Cas9 "tag, mutate and enrich" mutagenesis screens identified single amino acid mutations in PARP1 that cause profound PARPi-resistance. These included PARP1 mutations outside of the DNA interacting regions of the protein, such as mutations in solvent exposed regions of the catalytic domain and clusters of mutations around points of contact between ZnF, WGR and HD domains. These mutations altered PARP1 trapping, as did a mutation found in a clinical case of PARPi resistance. These genetic studies reinforce the importance of trapped PARP1 as a key cytotoxic DNA lesion and suggest that interactions between non-DNA binding domains of PARP1 influence cytotoxicity. Finally, different mechanisms of PARPi resistance (BRCA1 reversion, PARP1, 53BP1, REV7 mutation) had differing effects on chemotherapy sensitivity, suggesting that the underlying mechanism of PARPi resistance likely influences the success of subsequent therapies.
1,269 downloads cancer biology
Systematic prediction of cellular response to perturbations is a central challenge in biology, both for mechanistic explanations and for the design of effective therapeutic interventions. We addressed this challenge using a computational/experimental method, termed perturbation biology, which combines high-throughput (phospho)proteomic and phenotypic response profiles to targeted perturbations, prior information from signaling databases and network inference algorithms from statistical physics. The resulting network models are computationally executed to predict the effects of tens of thousands of untested perturbations. We report cell type specific network models of signaling in RAF-inhibitor resistant melanoma cells based on data from 89 combinatorial perturbation conditions and 143 readouts per condition. Quantitative simulations predicted c-Myc as an effective co-target with BRAF or MEK. Experiments showed that targeting c-Myc, using the BET bromodomain inhibitor JQ1, and the ERK pathway is both effective and synergistic in this context. We propose these combinations as pre-clinical candidates to prevent or overcome RAF inhibitor resistance in melanoma.
1,264 downloads cancer biology
Recent cancer genome studies have identified numerous genomic alterations in cancer genomes. It is hypothesized that only a fraction of these genomic alterations drive the progression of cancer -- often called driver mutations. Current sample sizes for cancer studies, often in the hundreds, are sufficient to detect pivotal drivers solely based on their high frequency of alterations. In cases where the alterations for a single function are distributed among multiple genes of a common pathway, however, single gene alteration frequencies might not be statistically significant. In such cases, we expect to observe that most samples are altered in only one of those alternative genes because additional alterations would not convey an additional selective advantage to the tumor. This leads to a mutual exclusion pattern of alterations, that can be exploited to identify these groups. We developed a novel method for the identification of sets of mutually exclusive gene alterations in a signaling network. We scan the groups of genes with a common downstream effect, using a mutual exclusivity criterion that makes sure that each gene in the group significantly contributes to the mutual exclusivity pattern. We have tested the method on all available TCGA cancer genomics datasets, and detected multiple previously unreported alterations that show significant mutual exclusivity and are likely to be driver events.
1,262 downloads cancer biology
Mutation signatures in cancer genomes reflect endogenous and exogenous mutational processes, offering insights into tumour etiology, features for prognostic and biologic stratification and vulnerabilities to be exploited therapeutically. We present a novel machine learning formalism for improved signature inference, based on multi-modal correlated topic models (MMCTM) which can at once infer signatures from both single nucleotide and structural variation counts derived from cancer genome sequencing data. We exemplify the utility of our approach on two hormone driven, DNA repair deficient cancers: breast and ovary (n=755 cases total). Our results illuminate a new age-associated structural variation signature in breast cancer, and an independently identified substructure within homologous recombination deficient (HRD) tumours in breast and ovarian cancer. Together, our study emphasizes the importance of integrating multiple mutation modes for signature discovery and patient stratification, with biological and clinical implications for DNA repair deficient cancers.
1,254 downloads cancer biology
Pei-Hsuan Chen, Ling Cai, Kenneth Huffman, Chendong Yang, Jiyeon Kim, Brandon Faubert, Lindsey Boroughs, Bookyung Ko, Jessica Sudderth, Elizabeth A McMillan, Luc Girard, Michael Peyton, Misty D Shields, David Shames, Hyun Seok Kim, Brenda Timmons, Ikuo Sekine, Rebecca Britt, Stephanie Weber, Lauren A Byers, John V Heymach, Michael A White, John D. Minna, Guanghua Xiao, Ralph J DeBerardinis
Intermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes including signal transduction and gene expression patterns arising from specific oncogenotypes and cell lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here we quantified over 100 metabolic features, mostly from 13C enrichment of molecules from central carbon metabolism, in over 80 non-small cell lung cancer (NSCLC) cell lines cultured under identical conditions. Because these cell lines were extensively annotated for oncogenotype, gene expression, protein expression and therapeutic sensitivity, the resulting database enables the user to uncover new relationships between metabolism and these orthogonal processes.
1,254 downloads cancer biology
The cell of origin of high grade serous ovarian carcinoma (HGSOC) remains controversial, with fallopian tube epithelium FTE and ovarian surface epithelium (OSE) each suggested as candidates. Here, by using genetically engineered mouse models and novel organoid systems, we assessed the tumor forming capacity and properties of FTE and OSE harboring the same oncogenic abnormalities. Combined RB family inactivation (via T121 expression) and Tp53 mutation in Pax8+ FTE caused transformation to Serous Tubal Intraepithelial Carcinoma (STIC), which rapidly metastasized to the ovarian surface. This mouse model was recapitulated by FTE organoids, which, upon orthotopic injection, generated widely metastatic HGSOC. The same genetic lesions in Lgr5+ OSE cells or organoids also caused metastatic HGSOC, although with longer latency and lower penetrance. Comparative transcriptome analysis was consistent with different human HGSOCs arising from FTE and OSE. Furthermore, FTE- and OSE-derived organoids showed differential sensitivity to HGSOC chemotherapeutics. Our results comport with a dualistic origin for HGSOC and suggest the cell-of-origin could influence therapeutic response.
1,254 downloads cancer biology
Altered chromatin structure is a hallmark of cancer, and inappropriate regulation of chromatin structure may represent the origin of transformation. Important studies have mapped human nucleosome distributions genome wide, but the role of chromatin structure in cancer progression has not been addressed. We developed a MNase-Transcription Start Site Sequence Capture method (mTSS-seq) to map the nucleosome distribution at human transcription start sites genome-wide in primary human lung and colon adenocarcinoma tissue. Here, we confirm that nucleosome redistribution is an early, widespread event in lung (LAC) and colon (CRC) adenocarcinoma. These altered nucleosome architectures are consistent between LAC and CRC patient samples indicating that they may serve as important early adenocarcinoma markers. We demonstrate that the nucleosome alterations are driven by the underlying DNA sequence and potentiate transcription factor binding. We conclude that DNA-directed nucleosome redistributions are widespread early in cancer progression. We have proposed an entirely new hierarchical model for chromatin-mediated genome regulation.
1,252 downloads cancer biology
I use the Nernst equation, parameterised with experimental data, to predict that cancer cells will accumulate more of a lipophilic anion than normal cells. This effect is correlated to charge number. Model cancer cells accumulate *100 more of an anion, *103 more di-anion, *106 more tri-anion, *108 more tetra-anion and *1010 more penta-anion (>>1 billion times more). The trend endures, conveying even greater specificity, for higher charge numbers. This effect could be leveraged for cancer therapy. Wherein the lipophilic anion is a toxin that targets some vital cellular process, which normal and cancer cells may even share. It delivers a high, lethal dose to cancer cells but a low, safe dose to normal cells. This mathematical finding conveys the prospect of a broad, powerful new front against cancer.
1,248 downloads cancer biology
Xia Gao, Sydney M Sanderson, Ziwei Dai, Michael A. Reid, Daniel E. Cooper, Min Lu, John P. Richie, Amy Ciccarella, Ana Calcagnotto, Peter G. Mikhael, Samantha J Mentch, Juan Liu, Gene Ables, David G. Kirsch, David S. Hsu, Sailendra Nichenametla, Jason W Locasale
Nutrition exerts profound effects on health and dietary interventions are commonly used to treat diseases of metabolic etiology. Although cancer has a substantial metabolic component, the principles that define whether nutrition may be used to influence tumour outcome are unclear. Nevertheless, it is established that targeting metabolic pathways with pharmacological agents or radiation can sometimes lead to controlled therapeutic outcomes. In contrast, whether specific dietary interventions could influence the metabolic pathways that are targeted in standard cancer therapies is not known. We now show that dietary restriction of methionine (MR), an essential amino acid, and the reduction of which has aging and obesogenic properties, influences cancer outcome through controlled and reproducible changes to one carbon metabolism. This pathway metabolizes methionine and further is the target of a host of cancer interventions involving chemotherapy and radiation. MR produced therapeutic responses in chemoresistant RAS-driven colorectal cancer patient derived xenografts and autochthonous KRAS G12D +/−; TP53 −/− -driven soft tissue sarcomas resistant to radiation. Metabolomics revealed the therapeutic mechanisms to occur through tumor cell autonomous effects on the flux through one carbon metabolism that impacted redox and nucleotide metabolism, thus interacting with the antimetabolite or radiation intervention. Finally, in a controlled and tolerated feeding study in humans, MR resulted in similar effects on systemic metabolism as obtained in responsive mice. These findings provide evidence that a targeted dietary manipulation can affect specific tumor cell metabolism to mediate broad aspects of cancer outcome.
1,244 downloads cancer biology
Claudia Calabrese, Kjong-Van Lehmann, Lara Urban, F Liu, S Erkek, Nuno A Fonseca, Andre Kahles, Helena Kilpinen, Julia Markowski, PCAWG Group 3, Sebastian M Waszak, Jan O. Korbel, Zemin Zhang, Alvis Brazma, Gunnar Raetsch, Roland F Schwarz, Oliver Stegle
Cancer is characterised by somatic genetic variation, but the effect of the majority of non-coding somatic variants and the interface with the germline genome are still unknown. We analysed the whole genome and RNA-seq data from 1,188 human cancer patients as provided by the Pan-cancer Analysis of Whole Genomes (PCAWG) project to map cis expression quantitative trait loci of somatic and germline variation and to uncover the causes of allele-specific expression patterns in human cancers. The availability of the first large-scale dataset with both whole genome and gene expression data enabled us to uncover the effects of the non-coding variation on cancer. In addition to confirming known regulatory effects, we identified novel associations between somatic variation and expression dysregulation, in particular in distal regulatory elements. Finally, we uncovered links between somatic mutational signatures and gene expression changes, including TERT and LMO2, and we explained the inherited risk factors in APOBEC-related mutational processes. This work represents the first large-scale assessment of the effects of both germline and somatic genetic variation on gene expression in cancer and creates a valuable resource cataloguing these effects.
1,235 downloads cancer biology
A major goal of cancer biology is determination of the relative importance of the genetic alterations that confer selective advantage to cancer cells. Tumor sequence surveys have frequently ranked the importance of substitutions to cancer growth by P value or a false-discovery conversion thereof. However, P values are thresholds for belief, not metrics of effect. Their frequent misuse as metrics of effect has often been vociferously decried. Here, we estimate the effect sizes of all recurrent single nucleotide variants in 23 cancer types, quantifying relative importance within and between driver genes. Some of the variants with the highest effect size, such as EGFR L858R in lung adenocarcinoma and BRAF V600E in primary skin cutaneous melanoma, have yielded remarkable therapeutic responses. Quantification of cancer effect sizes has immediate importance to the prioritization of clinical decision-making by tumor boards, selection and design of clinical trials, pharmacological targeting, and basic research prioritization.
1,234 downloads cancer biology
Malachi Griffith, Nicholas C Spies, Kilannin Krysiak, Adam C Coffman, Joshua F McMichael, Benjamin J Ainscough, Damian T Rieke, Arpad M Danos, Lynzey Kujan, Cody A Ramirez, Alex H Wagner, Zachary L Skidmore, Connor J Liu, Martin R Jones, Rachel L Bilski, Robert Lesurf, Erica K Barnell, Nakul M Shah, Melika Bonakdar, Lee Trani, Matthew Matlock, Avinash Ramu, Katie M Campbell, Gregory C Spies, Aaron P Graubert, Karthik Gangavarapu, James M Eldred, David E Larson, Jason R Walker, Benjamin M Good, Chunlei Wu, Andrew I Su, Rodrigo Dienstmann, Steven JM Jones, Ron Bose, David H Spencer, Lukas D Wartman, Richard K Wilson, Elaine R. Mardis, Obi L Griffith
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.
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