Identification Of Cancer Driver Genes Research Articles

Role of Homologous Recombination Repair (HRR) Genes in Uterine Leiomyosarcomas: A Retrospective Analysis.

Simple SummaryA more in-depth molecular characterization of uterine leiomyosarcomas (uLMS), a rare disease characterized with dismal prognosis, could provide data suitable for the identification of potential target-based drugs. We aimed to define frequencies of gene alterations in uLMS, especially regarding the somatic mutations of BRCA and HRR gene alterations, and identify the impact of these molecular alterations on clinical outcomes. This retrospective analysis of the mutational profile of uLMS showed that the most frequent alterations involved the TP53 gene, and that patients with TP53 alterations experienced a worse prognosis compared to patients with wild-type TP53 genes. Conversely, patient clinical outcomes were similar within patients with BRCA- and HRR-related genes versus non-HRR-related genes. However, although the frequency of patients with BRCA- and HRR-related alterations and mutations was relatively small, this setting could deserve an investigation into drug actionability, and potentially benefit from PARP inhibitors.Uterine leiomyosarcomas (uLMS) is a very rare disease, and patients experience a dismal prognosis even when treated with chemotherapy. Therefore, a more in-depth molecular characterization of this disease could provide suitable data for the identification of potential target-based drugs. This retrospective, single institutional study aimed to define the frequencies of gene alterations in uLMS, especially regarding the somatic mutations of BRCA and Homologous Recombination Repair (HRR) genes, and the impact of molecular alterations on clinical outcomes. The 16-genes Next-Generation Sequencing (NGS) panel, Homologous Recombination Solution TM (HRS, Sophia Genetics, Saint Sulpice, Switzerland), was used for the molecular evaluation of samples. The majority of patients (66/105, 63%) carried at least one sequence alteration, with a prevalence of TP53 involvement followed by RAD51B, BRCA1/2, and FANCL. Patients with TP53 gene alterations experienced a significantly worse prognosis for progression free survival (PFS) and overall survival (OS) versus wild-type patients. Given the number of patients with the BRCA1/2 mutation (N = 12), we included them in the HRR patient group; there was no difference in clinical outcomes with HRR versus non-HRR. The Cox’s multivariate analysis showed that stage and TP53 gene alterations resulted in a significantly worse OS. The integration of gene networking data, such as tumor mutation burdens and cancer driver gene identification, could show a clearer discrimination of gene distribution patterns, and lead to the implementation of therapeutic targets.

DGRanker: Cancer Driver Gene Detection in Human Transcriptional Regulatory Network.

Cancer is a group of diseases that have received much attention in biological research because of its high mortality rate and the lack of accurate identification of its root causes. In such studies, researchers usually try to identify cancer driver genes (CDGs) that start cancer in a cell. The majority of the methods that have ever been proposed for the identification of CDGs are based on gene expression data and the concept of mutation in genomic data. Recently, using networking techniques and the concept of influence maximization, some models have been proposed to identify these genes. We aimed to construct the cancer transcriptional regulatory network and identify cancer driver genes using a network science approach without the use of mutation and genomic data. In this study, we will employ the social influence network theory to identify CDGs in the human gene regulatory network (GRN) that is based on the concept of influence and power of webpages. First, we will create GRN Networks using gene expression data and Existing nodes and edges. Next, we will implement the modified algorithm on GRN networks being studied by weighting the regulatory interaction edges using the influence spread concept. Nodes with the highest ratings will be selected as the CDGs. The results show our proposed method outperforms most of the other computational and network-based methods and show its superiority in identifying CDGs compared to many other methods. In addition, the proposed method canidentifymany CDGs that are overlooked by all previously published methods. Our study demonstrated that the Google's PageRank algorithm can be utilized and modified as a network-based method for identifying cancer driver gene in transcriptional regulatory network. Furthermore, the proposed method can be considered as a complementary method to the computational-based cancer driver gene identification tools.

A Network-Centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers.

One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric.

Improving cancer driver gene identification using multi-task learning on graph convolutional network.

Cancer is thought to be caused by the accumulation of driver genetic mutations. Therefore, identifying cancer driver genes plays a crucial role in understanding the molecular mechanism of cancer and developing precision therapies and biomarkers. In this work, we propose a Multi-Task learning method, called MTGCN, based on the Graph Convolutional Network to identify cancer driver genes. First, we augment gene features by introducing their features on the protein-protein interaction (PPI) network. After that, the multi-task learning framework propagates and aggregates nodes and graph features from input to next layer to learn node embedding features, simultaneously optimizing the node prediction task and the link prediction task. Finally, we use a Bayesian task weight learner to balance the two tasks automatically. The outputs of MTGCN assign each gene a probability of being a cancer driver gene. Our method and the other four existing methods are applied to predict cancer drivers for pan-cancer and some single cancer types. The experimental results show that our model shows outstanding performance compared with the state-of-the-art methods in terms of the area under the Receiver Operating Characteristic (ROC) curves and the area under the precision-recall curves. The MTGCN is freely available via https://github.com/weiba/MTGCN.

ParsVNN: parsimony visible neural networks for uncovering cancer-specific and drug-sensitive genes and pathways.

Prediction of cancer-specific drug responses as well as identification of the corresponding drug-sensitive genes and pathways remains a major biological and clinical challenge. Deep learning models hold immense promise for better drug response predictions, but most of them cannot provide biological and clinical interpretability. Visible neural network (VNN) models have emerged to solve the problem by giving neurons biological meanings and directly casting biological networks into the models. However, the biological networks used in VNNs are often redundant and contain components that are irrelevant to the downstream predictions. Therefore, the VNNs using these redundant biological networks are overparameterized, which significantly limits VNNs’ predictive and explanatory power. To overcome the problem, we treat the edges and nodes in biological networks used in VNNs as features and develop a sparse learning framework ParsVNN to learn parsimony VNNs with only edges and nodes that contribute the most to the prediction task. We applied ParsVNN to build cancer-specific VNN models to predict drug response for five different cancer types. We demonstrated that the parsimony VNNs built by ParsVNN are superior to other state-of-the-art methods in terms of prediction performance and identification of cancer driver genes. Furthermore, we found that the pathways selected by ParsVNN have great potential to predict clinical outcomes as well as recommend synergistic drug combinations.

Mutations in Noncoding Cis-Regulatory Elements Reveal Cancer Driver Cistromes in Luminal Breast Cancer.

Whole-genome sequencing of primary breast tumors enabled the identification of cancer driver genes and noncoding cancer driver plexuses from somatic mutations. However, differentiating driver from passenger events among noncoding genetic variants remains a challenge. Herein, we reveal cancer-driver cis-regulatory elements linked to transcription factors previously shown to be involved in development of luminal breast cancers by defining a tumor-enriched catalogue of approximately 100,000 unique cis-regulatory elements from 26 primary luminal estrogen receptor (ER)+ progesterone receptor (PR)+ breast tumors. Integrating this catalog with somatic mutations from 350 publicly available breast tumor whole genomes, we uncovered cancer driver cistromes, defined as the sum of binding sites for a transcription factor, for ten transcription factors in luminal breast cancer such as FOXA1 and ER, nine of which are essential for growth in breast cancer with four exclusive to the luminal subtype. Collectively, we present a strategy to find cancer driver cistromes relying on quantifying the enrichment of noncoding mutations over cis-regulatory elements concatenated into a functional unit. IMPLICATIONS: Mapping the accessible chromatin of luminal breast cancer led to discovery of an accumulation of mutations within cistromes of transcription factors essential to luminal breast cancer. This demonstrates coopting of regulatory networks to drive cancer and provides a framework to derive insight into the noncoding space of cancer.

Evaluating machine learning methodologies for identification of cancer driver genes

Cancer is driven by distinctive sorts of changes and basic variations in genes. Recognizing cancer driver genes is basic for accurate oncological analysis. Numerous methodologies to distinguish and identify drivers presently exist, but efficient tools to combine and optimize them on huge datasets are few. Most strategies for prioritizing transformations depend basically on frequency-based criteria. Strategies are required to dependably prioritize organically dynamic driver changes over inert passengers in high-throughput sequencing cancer information sets. This study proposes a model namely PCDG-Pred which works as a utility capable of distinguishing cancer driver and passenger attributes of genes based on sequencing data. Keeping in view the significance of the cancer driver genes an efficient method is proposed to identify the cancer driver genes. Further, various validation techniques are applied at different levels to establish the effectiveness of the model and to obtain metrics like accuracy, Mathew’s correlation coefficient, sensitivity, and specificity. The results of the study strongly indicate that the proposed strategy provides a fundamental functional advantage over other existing strategies for cancer driver genes identification. Subsequently, careful experiments exhibit that the accuracy metrics obtained for self-consistency, independent set, and cross-validation tests are 91.08%., 87.26%, and 92.48% respectively.

Corrigendum: FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network

[This corrects the article DOI: 10.3389/fgene.2020.564839.].

Molecular differential analysis of uterine leiomyomas and leiomyosarcomas through weighted gene network and pathway tracing approaches

ABSTRACT Uterine smooth muscular neoplastic growths like benign leiomyomas (UL) and metastatic leiomyosarcomas (ULMS) share similar clinical symptoms, radiological and histological appearances making their clinical distinction a difficult task. Therefore, the objective of this study is to identify key genes and pathways involved in transformation of UL to ULMS through molecular differential analysis. Global gene expression profiles of 25 ULMS, 25 UL, and 29 myometrium (Myo) tissues generated on Affymetrix U133A 2.0 human genome microarrays were analyzed by deploying robust statistical, molecular interaction network, and pathway enrichment methods. The comparison of expression signals across Myo vs UL, Myo vs ULMS, and UL vs ULMS groups identified 249, 1037, and 716 significantly expressed genes, respectively (p ≤ 0.05). The analysis of 249 DEGs from Myo vs UL confirms multistage dysregulation of various key pathways in extracellular matrix, collagen, cell contact inhibition, and cytokine receptors transform normal myometrial cells to benign leiomyomas (p value ≤ 0.01). The 716 DEGs between UL vs ULMS were found to affect cell cycle, cell division related Rho GTPases and PI3K signaling pathways triggering uncontrolled growth and metastasis of tumor cells (p value ≤ 0.01). Integration of gene networking data, with additional parameters like estimation of mutation burden of tumors and cancer driver gene identification, has led to the finding of 4 hubs (JUN, VCAN, TOP2A, and COL1A1) and 8 bottleneck genes (PIK3R1, MYH11, KDR, ESR1, WT1, CCND1, EZH2, and CDKN2A), which showed a clear distinction in their distribution pattern among leiomyomas and leiomyosarcomas. This study provides vital clues for molecular distinction of UL and ULMS which could further assist in identification of specific diagnostic markers and therapeutic targets. Abbreviations UL: Uterine Leiomyomas; ULMS: Uterine Leiomyosarcoma; Myo: Myometrium; DEGs: Differential Expressed Genes; RMA: Robust Multiarray Average; DC: Degree of Centrality; BC: Betweenness of Centrality; CGC: Cancer Gene Census; FDR: False Discovery Rate; TCGA: Cancer Genome Atlas; BP: Biological Process; CC: Cellular Components; MF: Molecular Function; PPI: Protein–Protein Interaction

NetMix: A Network-Structured Mixture Model for Reduced-Bias Estimation of Altered Subnetworks

A classic problem in computational biology is the identification of altered subnetworks: subnetworks of an interaction network that contain genes/proteins that are differentially expressed, highly mutated, or otherwise aberrant compared with other genes/proteins. Numerous methods have been developed to solve this problem under various assumptions, but the statistical properties of these methods are often unknown. For example, some widely used methods are reported to output very large subnetworks that are difficult to interpret biologically. In this work, we formulate the identification of altered subnetworks as the problem of estimating the parameters of a class of probability distributions that we call the Altered Subset Distribution (ASD). We derive a connection between a popular method, jActiveModules, and the maximum likelihood estimator (MLE) of the ASD. We show that the MLE is statistically biased, explaining the large subnetworks output by jActiveModules. Based on these insights, we introduce NetMix, an algorithm that uses Gaussian mixture models to obtain less biased estimates of the parameters of the ASD. We demonstrate that NetMix outperforms existing methods in identifying altered subnetworks on both simulated and real data, including the identification of differentially expressed genes from both microarray and RNA-seq experiments and the identification of cancer driver genes in somatic mutation data.

FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.

Identification of driver genes, whose mutations cause the development of tumors, is crucial for the improvement of cancer research and precision medicine. To overcome the problem that the traditional frequency-based methods cannot detect lowly recurrently mutated driver genes, researchers have focused on the functional impact of gene mutations and proposed the function-based methods. However, most of the function-based methods estimate the distribution of the null model through the non-parametric method, which is sensitive to sample size. Besides, such methods could probably lead to underselection or overselection results. In this study, we proposed a method to identify driver genes by using functional impact prediction neural network (FI-net). An artificial neural network as a parametric model was constructed to estimate the functional impact scores for genes, in which multi-omics features were used as the multivariate inputs. Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. According to the comprehensive evaluation criterion, FI-net was powerful among various datasets and outperformed the other methods in terms of the overlap fraction with Cancer Gene Census and Network of Cancer Genes database, and the consensus in predictions among methods. Furthermore, the results illustrated that FI-net can identify known and potential novel driver genes.

Identification of cancer driver genes based on nucleotide context.

Cancer genomes contain large numbers of somatic mutations, but few of these mutations drive tumor development. Current approaches identify driver genes based on mutational recurrence, or they approximate the functional consequences of nonsynonymous mutations using bioinformatic scores. While passenger mutations are enriched in characteristic nucleotide contexts, driver mutations occur in functional positions, which are not necessarily surrounded by a particular nucleotide context. We observed that mutations in contexts that deviate from the characteristic contexts around passenger mutations provide a signal in favor of driver genes. We therefore developed a method that combines this feature with the signals traditionally used for driver gene identification. We applied our method to whole-exome sequencing data from 11,873 tumor-normal pairs and identified 460 driver genes that clustered into 21 cancer-related pathways. Our study provides a resource of driver genes across 28 tumor types with additional driver genes identified based on mutations in unusual nucleotide contexts.

Interpreting pathways to discover cancer driver genes with Moonlight

Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions. Identification of cancer driver genes, especially those that can act as tumour suppressors or oncogenes depending on context, remains a challenge. Here, the authors introduce Moonlight, a tool that integrates multi-omic data to address this challenge and identify numerous dual-role cancer genes.

Detection of Oncogenic and Clinically Actionable Mutations in Cancer Genomes Critically Depends on Variant Calling Tools

The analysis of cancer genomes provides fundamental information about its aetiology, the processes driving cell transformation or potential treatments. While researchers and clinicians are often only interested in the identification of oncogenic mutations, actionable variants or mutational signatures, the first crucial step in the analysis of any tumor genome is the identification of somatic variants in cancer cells (i.e. those that have been acquired during their evolution). For that purpose, a wide range of computational tools have been developed in recent years to detect somatic mutations in sequencing data from tumors. While there have been some efforts to benchmark somatic variant calling tools and strategies, the extent to which variant calling decisions impact the results of downstream analyses of tumor genomes remains unknown. Here we present a study to elucidate whether different variant callers (MuSE, MuTect2, SomaticSniper, VarScan2) and strategies to combine them (Consensus and Union) lead to different results in three important analyses of cancer genomics data: identification of cancer driver genes, quantification of mutational signatures and detection of clinically actionable variants. To this end, we tested how the results of these three analyses varied depending on the somatic mutation caller or strategy in five different projects from The Cancer Genome Atlas (TCGA). Our results show that variant calling decisions have a significant impact on these analyses, creating important differences that could even impact treatment decisions for some patients. Moreover, the Consensus calling strategy to combine the output of multiple variant calling tools, a very widely used strategy by the research community, can lead to the loss of some cancer driver genes and actionable mutations. On the other hand, the Union seems to be a legit strategy for some downstream analyses with a robust performance. Overall, our results point to important differences in critical analyses of tumor sequencing data depending on variant calling and highlight the limitations of widespread practices within the cancer genomics community.

A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping

Oncogenesis and cancer can arise as a consequence of a wide range of genomic aberrations including mutations, copy number alterations, expression changes and epigenetic modifications encompassing multiple omics layers. Integrating genomic, transcriptomic, proteomic and epigenomic datasets via multi-omics analysis provides the opportunity to derive a deeper and holistic understanding of the development and progression of cancer. There are two primary approaches to integrating multi-omics data: multi-staged (focused on identifying genes driving cancer) and meta-dimensional (focused on establishing clinically relevant tumour or sample classifications). A number of ready-to-use bioinformatics tools are available to perform both multi-staged and meta-dimensional integration of multi-omics data. In this study, we compared nine different integration tools using real and simulated cancer datasets. The performance of the multi-staged integration tools were assessed at the gene, function and pathway levels, while meta-dimensional integration tools were assessed based on the sample classification performance. Additionally, we discuss the influence of factors such as data representation, sample size, signal and noise on multi-omics data integration. Our results provide current and much needed guidance regarding selection and use of the most appropriate and best performing multi-omics integration tools.

8PD - Automatic interpretation of cancer genomes creates the largest repository of tumour genetic driver events

BackgroundTumor genome sequencing is becoming widely available in the clinical setting. However, the interpretation of tumor somatic variants remains an important challenge to implement precision cancer medicine. MethodsHere, we present IntOGen, a platform aimed for tumor genome interpretation. The first component of the platform is an automatic pipeline for cancer driver gene identification across cohorts. This pipeline implements several bioinformatics methods to detect driver genes, on the basis of signals of positive selection in their mutational pattern across tumor cohorts. It also annotates several oncogenomic features that shed light onto the role of driver genes in the development of each malignancy. The second component of the platform leverages these features to identify the somatic mutations that drive the tumor of each patient providing additional information about the possible effect of driver mutations on treatment response. ResultsWe have applied the framework to more than 50,000 tumors across more than 60 tumors types creating the largest repository of tumor genetic driver events. This comprehensive oncogenomics repository is the third component of IntOGen. ConclusionsAll the results and framework are available online to the cancer genomics research community at www.intogen.org, which we envision can support a broad range of oncology use cases. Legal entity responsible for the studyInstitute for Research in Biomedicine (IRB, Barcelona). FundingMinisterio de Educación y Ciencia de España (SAF-2015-R-66084 and RTI2018-094095-B-I00) Instituto Nacional de Bioinformática (INB) - PT17/0009/0013. DisclosureAll authors have declared no conflicts of interest.

Abstract 3375: leveraging protein dynamics to identify mutational hotspot communities in cancer driver genes

Abstract Large-scale exome sequencing of tumor genomes has enabled the identification of cancer driver genes using recurrence analysis and clustering-based approaches. Some of these methods also employ 3D protein structural data to identify missense mutational hotspots in many cancer-associated genes. However, in determining such mutational clusters in protein structures, a majority of these approaches overlook the role of protein dynamics. In this work, we present a novel framework to identify cancer driver genes using a more sensitive search of mutational hotspot communities on protein structures by incorporating models of protein dynamics. We applied our method to the TCGA pan-cancer atlas missense mutation catalog to identify cancer genes with hotspot mutational clusters within their associated PDB structures. Overall, our analysis predicts one or more mutational hotspot within the resolved structures of known as well as novel cancer driver genes. Ontological enrichment analysis implicates the roles of genes with predicted hotspots in vital biological processes, including cell differentiation, cell proliferation, signal transduction, kinase activity, and nucleotide binding. Furthermore, a comparison between our approach with previous 3D structure-based cancer hotspot detection methods suggests that the including protein dynamics significantly improves the identification of additional putative driver genes. In summary, our proposed framework predicts novel cancer driver genes and provides mechanistic insight into cancer progression through the lens mutational hotspots. Citation Format: Sushant Kumar, Declan Clarke, Mark Gerstein. leveraging protein dynamics to identify mutational hotspot communities in cancer driver genes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3375.

Genome-Wide CRISPR/Cas9 Screening for Identification of Cancer Genes in Cell Lines.

In this protocol, pooled sgRNA libraries targeting thousands of genes are computationally designed, generated using microarray-based synthesis techniques, and packaged into lentiviral particles. Target cells of interest are transduced with the lentiviral sgRNA pools to generate a collection of knockout mutants-via Cas9-mediated genomic cleavage-and screened for a phenotype of interest. The relative abundance of each mutant in the population can be monitored over time through high-throughput sequencing of the integrated sgRNA expression cassettes. Using this technique, we outline strategies for the identification of cancer driver genes and genes mediating drug response.

Identification of Cancer Driver Genes from a Custom Set of Next Generation Sequencing Data.

Next generation sequencing (NGS) has become the norm of cancer genomic researches. Large-scale cancer sequencing projects seek to comprehensively uncover mutated genes that confer a selective advantage for cancer cells. Numerous computational algorithms have been developed to find genes that drive cancer based on their patterns of mutation in a patient cohort. It has been noted that the distinct features of driver gene alterations in different subgroups are based on clinical characteristics. Previously, we have developed a database, DriverDB, to integrate all public cancer sequencing data and to identify cancer driver genes according to bioinformatics tools. In this chapter, we describe the use of the function "Meta-Analysis" in DriverDB that offers a list of clinical characteristics to define samples and provides a high degree of freedom for researchers to utilize the huge amounts of sequencing data. Moreover, researchers can use the "Gene" section to explore a single driver gene in all cancers by different kinds of aspects after identifying the specific driver genes by "Meta-Analysis." DriverDB is available at http://ngs.ym.edu.tw/driverdb/ .

Identification of Mutated Cancer Driver Genes in Unpaired RNA-Seq Samples.

The identification of cancer driver genes through the analysis of mutations detected with high-throughput sequencing is a useful tool and a key challenge in cancer genomics. The workflow presented here relies on unpaired RNA-seq tumoral samples, thus leveraging already available RNA-seq data and providing the intrinsical benefits of directly targeting the transcriptome. Based on well-established methods for variant detection, this workflow also involves thorough data cleaning and extensive annotation, which enable the selection for somatic mutations with functional impact and the prioritization of genes relevant to the carcinogenic processes in the input samples.