Set Of Driver Genes Research Articles

Patient-derived organoid elucidates the identical clonal origin of bilateral breast cancer with diverse molecular subtypes.

Bilateral breast cancer (BBC), an infrequent breast cancer subtype, has primarily been studied in terms of incidence, prognosis, and through comparative analysis of synchronous (SBBC) and metachronous (MBBC) manifestations. The advent and application of organoid technology hold profound implications for tumor research and clinical management. This study represents the pioneering use of organoid models in BBC research. We established organoid lines from two surgical tumor specimens of a BBC patient, with one line undergoing detailed pathological and genomic analysis. The BBC organoid from the right breast demonstrated a marker expression profile of ER (-), PR (-), HER-2 (0), and Ki67 index 10%, indicating that it may derived from the TNBC tissue. Whole Exome Sequencing (WES) displayed consistent set of Top10 cancer driver genes affected by missense mutations, frameshift mutation, or splice site mutations in three tumor tissues and the organoid samples. The organoids' single nucleotide polymorphisms (SNPs) were more closely aligned with the TNBC tissue than other tumor tissues. Evolutionary analysis suggested that different tumor regions might evolve from a common ancestral layer. In this case, the development of BBC organoids indicated that simultaneous lesions with diverse molecular profiles shared a high degree of consistency in key tumor-driving mutations. These findings suggest the feasibility of generating BBC organoids representing various molecular types, accurately replicating significant markers and driver mutations of the originating tumor. Consequently, organoids serve as a valuable in vitro model for exploring treatment strategies and elucidating the underlying mechanisms of BBC.

An Effective Method to Identify Cooperation Driver Gene Sets

Background: In cancer genomics research, identifying driver genes is a challenging task. Detecting cancer-driver genes can further our understanding of cancer risk factors and promote the development of personalized treatments. Gene mutations show mutual exclusivity and cooccur, and most of the existing methods focus on identifying driver pathways or driver gene sets through the study of mutual exclusivity, that is functionally redundant gene sets. Moreover, less research on cooperation genes with co-occurring mutations has been conducted. Objective: We propose an effective method that combines the two characteristics of genes, cooccurring mutations and the coordinated regulation of proliferation genes, to explore cooperation driver genes. Methods: This study is divided into three stages: (1) constructing a binary gene mutation matrix; (2) combining mutation co-occurrence characteristics to identify the candidate cooperation gene sets; and (3) constructing a gene regulation network to screen the cooperation gene sets that perform synergistically regulating proliferation. Results: The method performance is evaluated on three TCGA cancer datasets, and the experiments showed that it can detect effective cooperation driver gene sets. In further investigations, it was determined that the discovered set of co-driver genes could be used to generate prognostic classifications, which could be biologically significant and provide complementary information to the cancer genome. Conclusion: Our approach is effective in identifying sets of cancer cooperation driver genes, and the results can be used as clinical markers to stratify patients.

Abstract 1033: Evaluation of autoantibodies to proteins encoded by cancer driver genes as biomarkers for prostate cancer

Abstract Prostate cancer (PCa) is a complex disease driven by genomic alterations, and understanding the immune response to these alterations can provide valuable insights into its pathogenesis. This study aimed at exploring the immunogenicity of proteins encoded by PCa driver genes and identifying corresponding autoantibodies as potential PCa biomarkers. Bioinformatics analysis, enzyme-linked immunosorbent assay (ELISA), and immunohistochemistry (IHC) were conducted in this study. A comprehensive screening process involving bioinformatics analysis and published studies identified a set of PCa driver genes for further investigation. Three strategies were employed to identify candidate driver genes: (1) utilizing the IntOGen database (Martínez-Jiménez et.al, Nature Reviews Cancer 2020), nine cohorts comprising 1503 primary PCa patient samples were analyzed, resulting in the identification of 63 PCa driver genes, with the top 20 genes exhibiting high mutation rates and frequencies in these cohorts; (2) employing the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, 16 driver genes that were enriched in prostate cancer pathways were identified from the initial 63 genes; (3) referring to a published article (Dietlein et.al, Nature Genetics 2020), the top 17 driver genes that highly related to prostate cancer across 28 tumor types were selected. A final set of 15 PCa driver genes, which appeared in at least two of the screening strategies, were included in subsequent analyses. ELISA was then performed to assess the presence of autoantibodies targeting proteins encoded by these 15 PCa driver genes. Autoantibodies were evaluated using serum samples from 203 PCa patients and 93 normal controls. The results revealed that seven out of the 15 autoantibodies showed significantly elevated levels in the sera of PCa patients compared to normal controls. Notably, the autoantibody targeting SPOP (speckled-type POZ protein) exhibited the highest frequency of 42.4%, followed by the autoantibody to PTEN (phosphatase and tensin homolog) at 37.4% in the PCa group. Finally, the SPOP protein was analyzed by IHC on the prostate tissue microarray containing PCa tissues, prostate simple hyperplasia tissues, cancer adjacent prostate tissues, and normal prostate tissues further to explore the consistency of SPOP expression in antigen-antibody reactions. High expression of SPOP protein was found in the PCa tissues. These findings suggest that proteins encoded by PCa driver genes may possess immunogenic properties due to neoepitopes possibly generated by mutations, leading to the production of corresponding autoantibodies. Identifying specific autoantibodies associated with PCa driver genes could offer novel opportunities for discovering autoantibody-based biomarkers in PCa. Further validation of these autoantibodies is warranted to evaluate their diagnostic potential in PCa. Citation Format: Cuipeng Qiu, Xiao Wang, Carlos A. Casiano, Jian-Ying Zhang. Evaluation of autoantibodies to proteins encoded by cancer driver genes as biomarkers for prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1033.

Abstract A027: Exploring autoantibody response to proteins encoded by prostate cancer driver genes

Abstract Prostate cancer (PCa) is a complex disease driven by genomic alterations, and understanding the immune response to these alterations can provide valuable insights into the pathogenesis. This study aimed to explore the immunogenicity of proteins encoded by PCa driver genes and to further identify corresponding autoantibodies as potential biomarkers. Through a comprehensive screening process involving bioinformatics analysis based on published studies, a set of PCa driver genes was identified for further investigation. Three strategies were employed to identify candidate driver genes: (1) utilizing the IntOGen database (Martínez-Jiménez et.al, Nature Reviews Cancer 2020), nine cohorts comprising 1503 primary PCa patient samples were analyzed, resulting in the identification of 63 PCa driver genes, with the top 20 genes exhibiting high mutation rates and frequencies in these cohorts; (2) employing the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, 16 driver genes that were enriched in prostate cancer pathways were identified from the initial 63 genes; (3) referring to a published article (Dietlein et.al, Nature Genetics 2020), the top 17 driver genes specific to prostate cancer across 28 tumor types were selected. A final set of 15 PCa driver genes, which appeared in at least two of the screening strategies, were included in subsequent analyses. To assess the presence of autoantibodies targeting proteins encoded by these 15 PCa driver genes, an enzyme-linked immunosorbent assay (ELISA) was performed. Autoantibodies to proteins encoded by the selected PCa driver genes were evaluated using serum samples from PCa patients and normal controls. The results revealed that seven out of the 15 autoantibodies showed significantly elevated levels in the sera of PCa patients compared to normal controls. Notably, the autoantibody targeting SPOP (speckle-type POZ protein) exhibited the highest frequency of 42.4%, followed by the autoantibody to PTEN (phosphatase and tensin homolog) at 37.4% in the PCa group. These findings suggest that proteins encoded by PCa driver genes may possess immunogenic properties, leading to the production of corresponding autoantibodies. The identification of specific autoantibodies associated with PCa driver genes could offer novel opportunities for the discovery of autoantibody-based biomarkers in prostate cancer. Further validation and characterization of these autoantibodies are warranted to evaluate their diagnostic or prognostic potential and elucidate their functional roles in PCa pathogenesis. Citation Format: Jian-Ying Zhang, Cuipeng Qiu, Xiao Wang, Bofei Wang. Exploring autoantibody response to proteins encoded by prostate cancer driver genes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A027.

Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals.

High heterogeneity in genome and phenotype of cancer populations made it difficult to apply population-based common driver genes to the diagnosis and treatment of cancer individuals. Characterizing and identifying the personalized driver mechanism for glioblastoma multiforme (GBM) individuals were pivotal for the realization of precision medicine. We proposed an integrative method to identify the personalized driver gene sets by integrating the profiles of gene expression and genetic alterations in cancer individuals. This method coupled genetic algorithm and random walk to identify the optimal gene sets that could explain abnormality of transcriptome phenotype to the maximum extent. The personalized driver gene sets were identified for 99 GBM individuals using our method. We found that genomic alterations in between one and seven driver genes could maximally and cumulatively explain the dysfunction of cancer hallmarks across GBM individuals. The driver gene sets were distinct even in GBM individuals with significantly similar transcriptomic phenotypes. Our method identified MCM4 with rare genetic alterations as previously unknown oncogenic genes, the high expression of which were significantly associated with poor GBM prognosis. The functional experiments confirmed that knockdown of could significantly inhibit proliferation, invasion, migration and clone formation of the GBM cell lines U251 and U118MG, and overexpression of MCM4 significantly promoted the proliferation, invasion, migration and clone formation of the GBM cell line U87MG. Our method could dissect the personalized driver genetic alteration sets that are pivotal for developing targeted therapy strategies and precision medicine. Our method could be extended to identify key drivers from other levels, and could be applied to more cancer types.

A workflow to study mechanistic indicators for driver gene prediction with Moonlight.

Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.

Early expression onset of tissue-specific effector genes during the specification process in sea urchin embryos

BackgroundIn the course of animal developmental processes, various tissues are differentiated through complex interactions within the gene regulatory network. As a general concept, differentiation has been considered to be the endpoint of specification processes. Previous works followed this view and provided a genetic control scheme of differentiation in sea urchin embryos: early specification genes generate distinct regulatory territories in an embryo to express a small set of differentiation driver genes; these genes eventually stimulate the expression of tissue-specific effector genes, which provide biological identity to differentiated cells, in each region. However, some tissue-specific effector genes begin to be expressed in parallel with the expression onset of early specification genes, raising questions about the simplistic regulatory scheme of tissue-specific effector gene expression and the current concept of differentiation itself.ResultsHere, we examined the dynamics of effector gene expression patterns during sea urchin embryogenesis. Our transcriptome-based analysis indicated that many tissue-specific effector genes begin to be expressed and accumulated along with the advancing specification GRN in the distinct cell lineages of embryos. Moreover, we found that the expression of some of the tissue-specific effector genes commences before cell lineage segregation occurs.ConclusionsBased on this finding, we propose that the expression onset of tissue-specific effector genes is controlled more dynamically than suggested in the previously proposed simplistic regulation scheme. Thus, we suggest that differentiation should be conceptualized as a seamless process of accumulation of effector expression along with the advancing specification GRN. This pattern of effector gene expression may have interesting implications for the evolution of novel cell types.

Aberrant activation of TCL1A promotes stem cell expansion in clonal haematopoiesis.

Mutations in a diverse set of driver genes increase the fitness of haematopoietic stem cells (HSCs), leading to clonal haematopoiesis1. These lesions are precursors for blood cancers2-6, but the basis of their fitness advantage remains largely unknown, partly owing to a paucity of large cohorts in which the clonal expansion rate has been assessed by longitudinal sampling. Here, to circumvent this limitation, we developed a method to infer the expansion rate from data from a single time point. We applied this method to 5,071 people with clonal haematopoiesis. A genome-wide association study revealed that a common inherited polymorphism in the TCL1A promoter was associated with a slower expansion rate in clonal haematopoiesis overall, but the effect varied by driver gene. Those carrying this protective allele exhibited markedly reduced growth rates or prevalence of clones with driver mutations in TET2, ASXL1, SF3B1 and SRSF2, butthis effect was not seen inclones withdriver mutations in DNMT3A. TCL1A was not expressed in normal or DNMT3A-mutated HSCs, but the introduction of mutations in TET2 or ASXL1 led to the expression of TCL1A protein and the expansion of HSCs in vitro. The protective allele restricted TCL1A expression and expansion of mutant HSCs, as did experimentalknockdown of TCL1A expression. Forced expression of TCL1A promoted the expansion of human HSCs in vitro and mouse HSCs in vivo. Our results indicate that the fitness advantage of several commonly mutated driver genes in clonal haematopoiesis may be mediated by TCL1A activation.

ToMExO: A probabilistic tree-structured model for cancer progression.

Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculating the likelihood of our noisy datasets and use it to build our Markov Chain Monte Carlo (MCMC) inference algorithm, ToMExO. Together with a set of engineered MCMC moves, our fast likelihood calculations enable us to work with datasets with hundreds of genes and thousands of tumors, which cannot be dealt with using available cancer progression analysis methods. We demonstrate our method's performance on several synthetic datasets covering various scenarios for cancer progression dynamics. Then, a comparison against two state-of-the-art methods on a moderate-size biological dataset shows the merits of our algorithm in identifying significant and valid patterns. Finally, we present our analyses of several large biological datasets, including colorectal cancer, glioblastoma, and pancreatic cancer. In all the analyses, we validate the results using a set of method-independent metrics testing the causality and significance of the relations identified by ToMExO or competing methods.

Rapid Generation of Murine CLL/Richter Syndrome Models By Multiplexed CRISPR/Cas9 Editing of Common CLL Driver Genes

Background: Recent large-scale next generation sequencing studies have revealed a highly heterogeneous genetic landscape in CLL. However, the majority of the genetic lesions are relatively infrequent and their functional role and clinical relevance are still unknown. Moreover, in vivo models to study how these lesions cooperate between themselves and affect the response to novel therapeutic agents are still lacking. To further understand the relative contribution of individual genetic lesions to CLL development, progression and response to treatment, we performed multiplex CRISPR/Cas9-editing of normal and leukemic murine B cells by targeting a large set of CLL driver genes. Materials and Methods: Multiplexed CRISPR/Cas9 editing was done on normal B1 and B2 B cells isolated from the peritoneal cavity of young (<4 months) del(13q14)MDR-/- mice or on leukemic B1 B cells isolated from the spleen of old (>12 months) Eμ-TCL1 mice using a recently established procedure (Chakraborty et al, Blood 2021). The effects of the introduced genetic lesions on leukemia cell growth and treatment resistance were investigated by analyzing the mutant allele frequency (MAF) of the injected cells and cells recovered from the spleens of untreated or venetoclax-treated mice. Results: To investigate whether multiplex CRISPR/Cas9 editing can be used to transform normal murine B cells derived from del(13q14)MDR-/- mice, we simultaneously targeted 12 putative CLL driver genes representative of cellular pathways that are commonly affected in CLL, including DNA damage response (TP53, ATM and POT1), chromatin modifiers (CHD2, SETD2), cell cycle (RB1), inflammation (SAMHD1) and the NF-kB (BIRC3, NFKBIE, TRAIL-R), NOTCH (FBXW7) and MYC pathways (MGA) (n=4 experiments). The transfected B1 and B2 B cells were transplanted in the peritoneal cavity of B cell-deficient NSG mice and analyzed at different timepoints within a range of 1 to 5 months. Both cellular subsets were detected in the peritoneal cavity of NSG mice at the initial timepoint, but B1 B cells preferentially expanded at the later timepoints, giving rise to CD19+/CD5+ leukemias 3-5 months after transplantation. An increase in MAF was observed for all targeted genes, suggesting that all of the introduced genetic lesions provide a growth advantage to the leukemic cells. To investigate the impact of selected genetic lesions on CLL progression and Richter transformation (RT), we simultaneously introduced activating mutations in NOTCH1 (targeting of the PEST domain) or inactivating mutations in MGA, ATM, TP53, CDKN2A, CDKN2B, BIRC3 and NFKBIE in CLL cells derived from Eμ-TCL1 transgenic mice (n=3 independent experiments). A rise in MAF was observed for all 8 genes following in vivo propagation of the edited cells, suggesting that all of these mutations are positively selected during leukemia progression. However, selection of NOTCH1, TP53, ATM, BIRC3 and NFKBIE mutations occurred more rapidly, reaching the maximum MAF already after the 1st passage in vivo, whereas the MAF of genetic lesions specifically associated with Richter transformation, i.e. CDKN2A, CDKN2B and MGA, rouse more slowly and increased during serial passages (Figure 1). Interestingly, a substantial increase in MAF was observed for CDKN2A and CDKN2B when cells from the 1st passage were placed in culture (from 44% to 93%, p=0.017, and from 64% to 94%, p=0.043, after 6 weeks in culture for CDKN2A and CDKN2B, respectively), consistent with our recent finding that biallelic disruption of these genes allows for spontaneous proliferation of Eμ-TCL1 leukemia cells and may represent the transforming factor in a subset of cases with Richter Transformation. In contrast, the MAF of the other five driver genes showed no or only minor changes during culture. The MAF of CDKN2A and CDKN2B also increased to a greater extent in mice treated with venetoclax compared to untreated mice, indicating that cells with such genetic lesions are more resistant to venetoclax treatment. Conclusions: This study shows that multiplex CRISPR/Cas9 editing can be used to rapidly generate murine leukemias with the genetic make-up of human CLL or Richter Syndrome. These models can represent an important preclinical tool to study how individual genetic lesions cooperate during CLL development and progression and how they affect the response to treatment with novel therapeutic agents. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Dynamic cancer drivers: a causal approach for cancer driver discovery based on bio-pathological trajectories.

The traditional way for discovering genes which drive cancer (namely cancer drivers) neglects the dynamic information of cancer development, even though it is well known that cancer progresses dynamically. To enhance cancer driver discovery, we expand cancer driver concept to dynamic cancer driver as a gene driving one or more bio-pathological transitions during cancer progression. Our method refers to the fact that cancer should not be considered as a single process but a compendium of altered biological processes causing the disease to develop over time. Reciprocally, different drivers of cancer can potentially be discovered by analysing different bio-pathological pathways. We propose a novel approach for causal inference of genes driving one or more core processes during cancer development (i.e. dynamic cancer driver). We use the concept of pseudotime for inferring the latent progression of samples along a biological transition during cancer and identifying a critical event when such a process is significantly deviated from normal to carcinogenic. We infer driver genes by assessing the causal effect they have on the process after such a critical event. We have applied our method to single-cell and bulk sequencing datasets of breast cancer. The evaluation results show that our method outperforms well-recognized cancer driver inference methods. These results suggest that including information of the underlying dynamics of cancer improves the inference process (in comparison with using static data), and allows us to discover different sets of driver genes from different processes in cancer. R scripts and datasets can be found at https://github.com/AndresMCB/DynamicCancerDriver.

Cancer driver drug interaction explorer.

Cancer is a heterogeneous disease characterized by unregulated cell growth and promoted by mutations in cancer driver genes some of which encode suitable drug targets. Since the distinct set of cancer driver genes can vary between and within cancer types, evidence-based selection of drugs is crucial for targeted therapy following the precision medicine paradigm. However, many putative cancer driver genes can not be targeted directly, suggesting an indirect approach that considers alternative functionally related targets in the gene interaction network. Once potential drug targets have been identified, it is essential to consider all available drugs. Since tools that offer support for systematic discovery of drug repurposing candidates in oncology are lacking, we developed CADDIE, a web application integrating six human gene-gene and four drug-gene interaction databases, information regarding cancer driver genes, cancer-type specific mutation frequencies, gene expression information, genetically related diseases, and anticancer drugs. CADDIE offers access to various network algorithms for identifying drug targets and drug repurposing candidates. It guides users from the selection of seed genes to the identification of therapeutic targets or drug candidates, making network medicine algorithms accessible for clinical research. CADDIE is available at https://exbio.wzw.tum.de/caddie/ and programmatically via a python package at https://pypi.org/project/caddiepy/.

Exploiting protein family and protein network data to identify novel drug targets for bladder cancer.

Bladder cancer remains one of the most common forms of cancer and yet there are limited small molecule targeted therapies. Here, we present a computational platform to identify new potential targets for bladder cancer therapy. Our method initially exploited a set of known driver genes for bladder cancer combined with predicted bladder cancer genes from mutationally enriched protein domain families. We enriched this initial set of genes using protein network data to identify a comprehensive set of 323 putative bladder cancer targets. Pathway and cancer hallmarks analyses highlighted putative mechanisms in agreement with those previously reported for this cancer and revealed protein network modules highly enriched in potential drivers likely to be good targets for targeted therapies. 21 of our potential drug targets are targeted by FDA approved drugs for other diseases — some of them are known drivers or are already being targeted for bladder cancer (FGFR3, ERBB3, HDAC3, EGFR). A further 4 potential drug targets were identified by inheriting drug mappings across our in-house CATH domain functional families (FunFams). Our FunFam data also allowed us to identify drug targets in families that are less prone to side effects i.e., where structurally similar protein domain relatives are less dispersed across the human protein network. We provide information on our novel potential cancer driver genes, together with information on pathways, network modules and hallmarks associated with the predicted and known bladder cancer drivers and we highlight those drivers we predict to be likely drug targets.

Clonal Hematopoiesis Is Driven By Aberrant Activation of TCL1A

Introduction: Clonal hematopoiesis of indeterminate potential (CHIP) may occur when a hematopoietic stem cell (HSC) acquires a fitness-increasing mutation resulting in its clonal expansion. A diverse set of driver genes, such as regulators of DNA methylation, splicing, and chromatin remodeling, have been associated with CHIP, but it remains largely unknown why HSCs bearing these mutations are positively selected. It has been challenging to identify the genetic and environmental factors mediating clonal expansion in humans, partially due to a lack of large cohorts with longitudinal blood sampling of participants. To circumvent this limitation, we developed a method to infer clonal expansion rate from single timepoint data called PACER (passenger-approximated clonal expansion rate).Methods: PACER is based on the principle that genomic passenger mutations can be used to infer the birth date of pre-malignant clones because these mutations accumulate fairly linearly over time. Thus, an individual with CHIP with a greater number of passenger mutations in the mutant clone is expected to have acquired the clone at a later age than someone with fewer passenger mutations. For two individuals of the same age and with clones of the same size, we expect the clone with more passengers to be more fit, as it expanded to the same size in less time. Typically, one would need to isolate single-cell colonies derived from HSCs in order to calculate the total passenger mutation burden. However, we hypothesized that this measure could also be approximated from whole genome sequencing of blood cell DNA, such as that used in large biobank projects. The expansion rate (PACER) is then estimated by adjusting the total passenger count for age and variant allele fraction in each individual. The ability of passengers to predict future clonal expansion was validated using longitudinal blood samples from 51 CHIP carriers in the Women's Health Initiative taken ~10 years apart (Figure 1). It also accurately predicted the known fitness effects due to different driver mutations in 5,551 CHIP carriers from the Trans-Omics for Precision Medicine (TOPMed) program (Figure 2).Results: Having validated the approach, we next hypothesized that we could identify germline variants influencing PACER, thus revealing genes and pathways mediating clonal expansion. The lead hit in a genome-wide association study (GWAS) of PACER was a common single nucleotide polymorphism (SNP) in the TCL1A promoter that was associated with slower clonal expansion in CHIP overall (Figure 3). TCL1A is an oncogene that is activated via translocation in T-cell prolymphocytic leukemia, but has no known role in CHIP or myeloid malignancies. A gene-level analysis indicated that the TCL1A SNP was associated with slower growth of clones bearing TET2 mutations, but had no effect on DNMT3A-mutant clone growth. We further found that those carrying two copies of the protective SNP had 40-80% reduced odds of having clones with driver mutations in TET2, ASXL1, SF3B1, SRSF2, and JAK2, but not DNMT3A. A concomitant decrease in incident myeloid malignancies was also seen in carriers of this protective SNP.Next, we interrogated how the protective SNP influenced TCL1A activity in HSCs. Normal human HSCs lacked open chromatin at the TCL1A promoter and TCL1A expression, but inducing frameshift mutations in TET2 via CRISPR editing led to accessibility of the promoter and gene/protein expression in HSCs (Figure 4). This effect was abrogated in HSCs from donors of the protective TCL1A SNP in a dose-dependent manner. Finally, we found that HSCs from donors homozygous for the protective SNP had markedly less expansion of phenotypic stem and progenitor cells in vitro after the introduction of TET2 mutations than TET2-edited HSCs from donors with two copies of the reference allele.Conclusions: In summary, we developed a novel method to infer the expansion rate of pre-malignant clones and performed the first ever GWAS for this trait. Our results indicate that the fitness advantage of several common driver genes in CHIP and hematological cancers is mediated through TCL1A activation, which may be a therapeutic target to treat these conditions. PACER is an approach that can be widely adopted to uncover genetic and environmental determinants of pre-malignant clonal expansion in blood and other tissues. [Display omitted] DisclosuresDesai: Bristol Myers Squibb: Consultancy; Kura Oncology: Consultancy; Agios: Consultancy; Astex: Research Funding; Takeda: Consultancy; Janssen R&D: Research Funding. Natarajan: Blackstone Life Sciences: Consultancy; Boston Scientific: Research Funding; Novartis: Consultancy, Research Funding; AstraZeneca: Consultancy, Research Funding; Apple: Consultancy, Research Funding; Amgen: Research Funding; Genentech: Consultancy; Foresite Labs: Consultancy. Jaiswal: Novartis: Consultancy, Honoraria; AVRO Bio: Consultancy, Honoraria; Genentech: Consultancy, Honoraria; Foresite Labs: Consultancy; Caylo: Current holder of stock options in a privately-held company.

Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients.

Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.

Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness.

Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited. Thus, new approaches are necessary to explore the genetic architecture of insulin resistance. To that end, we generated an iPSC library across the spectrum of insulin sensitivity in humans. RNA-seq based analysis of 310 induced pluripotent stem cell (iPSC) clones derived from 100 individuals allowed us to identify differentially expressed genes between insulin resistant and sensitive iPSC lines. Analysis of the co-expression architecture uncovered several insulin sensitivity-relevant gene sub-networks, and predictive network modeling identified a set of key driver genes that regulate these co-expression modules. Functional validation in human adipocytes and skeletal muscle cells (SKMCs) confirmed the relevance of the key driver candidate genes for insulin responsiveness.

Identification of the distinct genomic features in gastroesophageal junction adenocarcinoma and its Siewert subtypes.

Rates of gastroesophageal junction adenocarcinomas (GEJAs) have shown an alarming increase; however, the genetic background of GEJA and its Siewert classification have yet to be uncovered. Here, 60 paired tumor and normal DNA samples from GEJA patients were analyzed by whole-exome sequencing. Among them, 13 were Siewert type I, 14 were type II, and 33 were type III. A predominance of C/G>T/A substitutions was discovered in GEJA, followed by C/G>A/T substitutions. Notably, Siewert type I and type II/III display distinct sets of driver genes, mutational spectrum, and recurrently disrupted pathways. Siewert type I showed similarity to esophageal adenocarcinomas (EACs) and the chromosomal instability subtype of stomach adenocarcinomas, while Siewert type II/III showed similarity to the genomic stable subtype of stomach adenocarcinoma. We also found that mutation of FBXW7, a driver gene of GEJA, was enriched in Siewert type I. Our data identify differences between GEJA and stomach/EACs at the genomic level and provide evidence for differential treatment based on Siewert classification of GEJA. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Abstract LB-036: A genome wide functional genomics screen for co-driver mutations of mutant TP53 promoting cellular heterogeneity during breast cancer progression

Abstract Breast tumors present a high degree of heterogeneity. The major tumor suppressor TP53 is mutated in 30% of all breast tumors and 80% in basal subtype co-existing with numerous other somatic mutations. We hypothesized that combination neo-morphic functions of specific TP53 driver mutations and combination of unique ‘co-drivers' result in inter-tumor heterogeneity. We observed a broad spectrum of phenotypic changes in hallmarks of cancer like cell survival, resistance to apoptosis and anoikis, cell migration and invasion among a panel of 10 mutant p53 expressing mammary epithelial cell lines compared to those with WT p53. Integrated analysis from ChIP-seq and RNA-seq revealed distinct promoter binding profiles of the different mutant p53 proteins, implying non-canonical transcriptional activity contributing to the possible phenotypic heterogeneity in TP53-mutated tumors. The phenotypic heterogeneity of seen in TP53 mutated breast tumors can be attributed to both altered DNA binding properties resulting in expression changes of canonical or mutant-specific target genes and functional crosstalk with distinct co-existing somatic mutations or co-drivers. For the proof-of-concept of co-drivers of mutant p53, when PTEN was deleted using CRISPR in non-invasive p53 mutant Y234C expressing cells a remarkable increase in cell invasion was observed. A genome wide CRISPR based pooled library screen maintained at low MOI on two distinct p53 mutant Y234C and R273C cells followed by bulk sequencing identified completely different candidate co-driver mutations that promoted cell invasion, the initiating step for tumor cell metastasis. The top co-driver candidates included unique genes associated with cytoskeletal and apoptosis resistance pathways and some known mutated genes reported in breast cancer patients harboring TP53 mutations. These invasive pooled library transduced cells when injected in a xenograft mouse model failed to form any mammary tumors. Interestingly, primary tumors appeared in mice injected with pooled library cells transduced at high MOI along with overexpression of MYC. Such results reinstated the importance of functional crosstalk between genes and specific gene combinations driving tumorigenesis. We are currently optimizing a single-cell sequencing platform to capture the combinatorial co-drivers of mutant TP53 promoting the mice primary tumors. This combined approach of deep molecular profiling and functional genomic screen powered the identification of distinct mutant TP53 driver and co-driver gene sets that may contribute to heterogeneous cellular phenotypes and promote aggressive behavior which can guide the development of novel targeted therapies. Citation Format: Anasuya Pal, Chenxi Xu, Jin Park, Joshua LaBaer. A genome wide functional genomics screen for co-driver mutations of mutant TP53 promoting cellular heterogeneity during breast cancer progression [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-036.

Human Lung Adenocarcinoma-Derived Organoid Models for Drug Screening

SummaryLung cancer is an extremely heterogeneous disease, and its treatment remains one of the most challenging tasks in medicine. Few existing laboratory lung cancer models can faithfully recapitulate the diversity of the disease and predict therapy response. Here, we establish 12 patient-derived organoids from the most common lung cancer subtype, lung adenocarcinoma (LADC). Extensive gene and histopathology profiling show that the tumor organoids retain the histological architectures, genomic landscapes, and gene expression profiles of their parental tumors. Patient-derived lung cancer organoids are amenable for biomarker identification and high-throughput drug screening in vitro. This study should enable the generation of patient-derived lung cancer organoid lines, which can be used to further the understanding of lung cancer pathophysiology and to assess drug response in personalized medicine.

QuaDMutNetEx: a method for detecting cancer driver genes with low mutation frequency

BackgroundCancer is caused by genetic mutations, but not all somatic mutations in human DNA drive the emergence or growth of cancers. While many frequently-mutated cancer driver genes have already been identified and are being utilized for diagnostic, prognostic, or therapeutic purposes, identifying driver genes that harbor mutations occurring with low frequency in human cancers is an ongoing endeavor. Typically, mutations that do not confer growth advantage to tumors – passenger mutations – dominate the mutation landscape of tumor cell genome, making identification of low-frequency driver mutations a challenge. The leading approach for discovering new putative driver genes involves analyzing patterns of mutations in large cohorts of patients and using statistical methods to discriminate driver from passenger mutations.ResultsWe propose a novel cancer driver gene detection method, QuaDMutNetEx. QuaDMutNetEx discovers cancer drivers with low mutation frequency by giving preference to genes encoding proteins that are connected in human protein-protein interaction networks, and that at the same time show low deviation from the mutual exclusivity pattern that characterizes driver mutations occurring in the same pathway or functional gene group across a cohort of cancer samples.ConclusionsEvaluation of QuaDMutNetEx on four different tumor sample datasets show that the proposed method finds biologically-connected sets of low-frequency driver genes, including many genes that are not found if the network connectivity information is not considered. Improved quality and interpretability of the discovered putative driver gene sets compared to existing methods shows that QuaDMutNetEx is a valuable new tool for detecting driver genes. QuaDMutNetEx is available for download from https://github.com/bokhariy/QuaDMutNetExunder the GNU GPLv3 license.