Landscape and Saturation Analysis of Mutations Associated With Race in Cancer Genomes by Clinical Sequencing.

Differences in cancer genomes between racial groups may impact tumor biology and health disparities. However, the discovery of race-associated mutations is constrained by the limited representation and sample size of different racial groups in prior genomic studies. We evaluated the influence of race on the frequency of gene mutations using the Genomics, Evidence, Neoplasia, Information, Exchange database, a large genomic dataset aggregated from clinical sequencing. Matched cohort analyses were used to identify histology-specific race-associated mutations including increased TERT promoter mutations in Black and Asian patients with gliomas and bladder cancers, and a decreased frequency of mutations in DNA repair pathway genes and subunits of the SWI/SNF chromatin complex in Asian and Black patients across multiple cancer types. The distribution of actionable mutations in oncogenes was also race-specific, demonstrating how targeted therapies may have a disparate impact on racial groups. Down-sampling analyses indicate that larger sample sizes are likely to discover more race-associated mutations. These results provide a resource to understand differences in cancer genomes between racial groups which may inform the design of clinical studies and patient recruitment strategies in biomarker trials.

Demographic Trends, Co-Alterations, and Imatinib Resistance across Genomic Variants in Gastrointestinal Stromal Tumors: An AACR Project GENIE Analysis.

INTRODUCTION: Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal neoplasm of the gastrointestinal tract, the treatment of which represents a significant breakthrough in targeted cancer therapy. Given its overall rare nature, genomic differences and clinical implications between demographic groups have not been previously investigated. METHODS: Anonymized demographic, clinical, and genomic data from 1,559 GIST patients in the American Association for Cancer Research Project GENIE database were analyzed using cBioPortal and custom Python scripts. Data on patient demographics, genomic alterations, and co-occurrence genetic alerations were collected and classified according to clinical implications using the OncoKB database. χ2 tests for differences in genomic alterations were used across various demographic factors and mutual exclusivity analysis was employed to identify co-mutation patterns. RESULTS: Male patients demonstrated higher incidence of PDGFRA mutation (14.56% vs. 8.05%; p < 0.001), while female patients had higher likelihood of NF1 mutations (7.46% vs. 3.23%; p = 0.001). Asian patients had higher alteration rates at KIT (85.59%; p = 0.002). Co-occurrence analysis revealed KIT alterations frequently co-occurred with CDKN2A (q < 0.001), MTAP (q = 0.045), and PTEN (q = 0.056), while there was mutual exclusivity with PDGFRA (q < 0.001), NF1 (q < 0.001), and BRAF (q = 0.015). CDKN2A alterations co-occurred with MTAP (q < 0.001) and PIK3CA (q = 0.015), while being mutually exclusive with TP53 (q = 0.002) and NF1 (q = 0.007). Trends were similar among patients who had received no prior medical treatment. Imatinib-resistant mutations were more common among male patients (25.6% vs. 18.9%; p = 0.0056) and individuals under 55 (27.3% vs. 20.9%; p = 0.0228). Among patients with imatinib-resistant mutations, 77.78% had sunitinib resistance, while 70.25% maintained sensitivity to ripretinib. CONCLUSION: Sex and race/ethnic differences in genomic alterations, as well as co-mutations, were prevalent among patients with GIST. Variations in mutational profiles highlight the importance of distinct genetic drivers that may be targeted to treat different patient populations.

Disease progression, survival, and molecular disparities in Black and White patients with endometrioid endometrial carcinoma in real-world registries and GOG/NRG oncology randomized phase III clinical trials.

OBJECTIVE: Investigate racial disparities in outcomes and molecular features in Black and White patients with endometrioid endometrial carcinoma (EEC). METHODS: Black and White patients diagnosed with EEC who underwent hysterectomy ± adjuvant treatment in SEER, National Cancer Database (NCDB), the Genomics Evidence Neoplasia Information Exchange (GENIE) project (v.13.0), and eight NCI-sponsored randomized phase III clinical trials (RCTs) were studied. Hazard ratio (HR) and 95% confidence interval (CI) were estimated for cancer-related death (CRD), non-cancer death (NCD), and all-cause death. RESULTS: Black (n = 4397) vs. White (n = 47,959) patients in SEER had a HR (95% CI) of 2.04 (1.87-2.23) for CRD and 1.22 (1.09-1.36) for NCD. In NCDB, the HR (95% CI) for death in Black (n = 13,468) vs. White (n = 155,706) patients was 1.52 (1.46-1.58) dropping to 1.29 (1.23-1.36) after propensity-score matching for age, comorbidity, income, insurance, grade, stage, LVSI, and treatment. In GENIE, Black (n = 109) vs. White (n = 1780) patients had fewer PTEN, PIK3R1, FBXW7, NF1, mTOR, CCND1, and PI3K-pathway-related gene mutations. In contrast, TP53 and DNA-repair-related gene mutation frequency as well as tumor mutational burden-high status were similar in Black and White patients. In RCTs, Black (n = 187) vs. White (n = 2877) patients were more likely to have advanced or recurrent disease, higher grade, worse performance status and progressive disease. Risk of death in Black vs. White patients in RCTs was 2.19 (1.77-2.71) persisting to 1.32 (1.09-1.61) after matching for grade, stage, and treatment arm while balancing age and performance status. CONCLUSIONS: Differences exist in clinical presentation, outcomes, and molecular features in Black vs. White patients with EEC in real-world registries and RCTs. Targeted-drug development, strategies to modify social determinants, and diverse inclusion in RCTs are approaches to reduce disparities.

Molecular characterization of gliosarcoma reveals prognostic biomarkers and clinical parallels with glioblastoma.

PURPOSE: Gliosarcoma is a rare histopathological variant of glioblastoma, but it is unclear whether distinct clinical or molecular features distinguish it from other glioblastomas. The purpose of this study was to characterize common genomic alterations of gliosarcoma, compare them to that of glioblastoma, and correlate them with prognosis. METHODS: This was a single-institution, retrospective cohort study of patients seen between 11/1/2017 to 1/28/2024. Clinical and genomic data were obtained from the medical record. Results were validated using data from AACR Project GENIE (v15.1-public). RESULTS: We identified 87 gliosarcoma patients in the institutional cohort. Compared to a contemporary cohort of 492 glioblastoma, there was no difference in overall survival, though progression free survival was inferior for patients with gliosarcoma (p = 0.01). Several of the most-commonly altered genes in gliosarcoma were more frequently altered than in glioblastoma (NF1, PTEN, TP53), while others were less frequently altered than in glioblastoma (EGFR). CDKN2A/CDKN2B/MTAP alterations were associated with inferior survival on univariate Cox (HR = 5.4, p = 0.023). When pooled with 93 patients from the GENIE cohort, CDKN2A/B (HR = 1.75, p = 0.039), RB1 (HR = 0.51, p = 0.016), LRP1B (p = 0.050, HR = 2.0), and TSC2 (HR = 0.31, p = 0.048) alterations or loss were significantly associated with survival. These effects remained when controlled for age, sex, and cohort of origin with multivariate Cox. CONCLUSION: Gliosarcoma has a similar overall survival but worse response to treatment and different mutational profile than glioblastoma. CDKN2A/B loss and LRP1B alterations were associated with inferior prognosis, while RB1 or TSC2 alterations were associated with improved outcomes. These findings may have implications for clinical management and therapeutic selection in this patient population.

The Comparison of Newly Diagnosed Invasive Breast Patient Cohorts in Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE-BPC) and Other Real-World Databases.

BACKGROUND: Oncology databases that integrate genomic and clinical data have become valuable resources for precision medicine. However, the generalizability of these databases has not been comprehensively assessed. OBJECTIVES: To describe the demographics, clinical characteristics, treatments, and overall survival of breast cancer cohorts in GENIE-BPC and three other databases. METHODS: This study utilized GENIE-BPC, SEER, SEER-Medicare, and Merative MarketScan Research Databases. Women with invasive breast cancer were identified through EHR, cancer registries or ICD-9/10-CM codes. The ages were 18+ years or per database requirement. Treatments were based on EHR or HCPCS/NDC codes in claims. Overall survival was estimated as time from diagnosis to death. RESULTS: Of female breast cancer patients in GENIE-BPC (n = 775), SEER (n = 548 336), SEER-Medicare (n = 68 914), and Marketscan (n = 109 499) databases, the median ages at initial diagnosis were 44, 62, 74, and 57 years, respectively. A greater proportion of patients in GENIE-BPC, compared to SEER/SEER-Medicare, had higher nuclear grades (%III-%IV: 57% vs. 26%/24%), advanced disease stage (%IV: 25.3% vs. 5%/3.6%), percent of triple negative breast cancer (19.7% vs. 10.2%/8.5%), and receipt of chemotherapy (85.0% vs. NA/22.3%). The 1-, 3-, and 5-year overall survival rates were lower in GENIE-BPC (78.5%, 60.5%, 55.5%) than in SEER (95.8%, 89.5%, 85.5%) and SEER-Medicare (91.6%, 81.4%, 75.0%). CONCLUSION: Breast cancer patients in GENIE-BPC were younger, had more advanced disease, had a higher proportion of triple negative breast cancer and recipients of chemotherapy, and had poorer overall survival. Researchers must use statistical adjustment when extrapolating results (e.g., biomarker prevalence) from GENIE-BPC to the larger breast cancer population.

Insights into treatment-specific prognostic somatic mutations in NSCLC from the AACR NSCLC GENIE BPC cohort analysis.

BACKGROUND: Treatment of non-small lung cancer (NSCLC) has evolved in recent years, benefiting from advances in immunotherapy and targeted therapy. However, limited biomarkers exist to assist clinicians and patients in selecting the most effective, personalized treatment strategies. Targeted next-generation sequencing-based genomic profiling has become routine in cancer treatment and generated crucial clinicogenomic data over the last decade. This has made the development of mutational biomarkers for drug response possible. METHODS: To investigate the association between a patient's responses to a specific somatic mutation treatment, we analyzed the NSCLC GENIE BPC cohort, which includes 2,004 tumor samples from 1,846 patients. RESULTS: We identified somatic mutation signatures associated with response to immunotherapy and chemotherapy, including carboplatin-, cisplatin-, pemetrexed- or docetaxel-based chemotherapy. The prediction power of the chemotherapy-associated signature was significantly affected by epidermal growth factor receptor (EGFR) mutation status. Therefore, we developed an EGFR wild-type-specific mutation signature for chemotherapy selection. CONCLUSION: Our treatment-specific gene signatures will assist clinicians and patients in selecting from multiple treatment options.

Prostate cancer genetic alterations in Hispanic men.

BACKGROUND: Differences in DNA alterations in prostate cancer among White, Black, and Asian men have been widely described. This is the first description of the frequency of DNA alterations in primary and metastatic prostate cancer samples of self-reported Hispanic men. METHODS: We utilized targeted next-generation sequencing tumor genomic profiles from prostate cancer tissues that underwent clinical sequencing at academic centers (GENIE 11th). We decided to restrict our analysis to the samples from Memorial Sloan Kettering Cancer Center as it was by far the main contributor of Hispanic samples. The numbers of men by self-reported ethnicity and racial categories were analyzed via Fisher's exact test between Hispanic-White versus non-Hispanic White. RESULTS AND LIMITATIONS: Our cohort consisted of 1412 primary and 818 metastatic adenocarcinomas. In primary adenocarcinomas, TMPRSS2 and ERG gene alterations were less common in non-Hispanic White men than Hispanic White (31.86% vs. 51.28%, p = 0.0007, odds ratio [OR] = 0.44 [0.27-0.72] and 25.34% vs. 42.31%, p = 0.002, OR = 0.46 [0.28-0.76]). In metastatic tumors, KRAS and CCNE1 alterations were less prevalent in non-Hispanic White men (1.03% vs. 7.50%, p = 0.014, OR = 0.13 [0.03, 0.78] and 1.29% vs. 10.00%, p = 0.003, OR = 0.12 [0.03, 0.54]). No significant differences were found in actionable alterations and androgen receptor mutations between the groups. Due to the lack of clinical characteristics and genetic ancestry in this dataset, correlation with these could not be explored. CONCLUSION: DNA alteration frequencies in primary and metastatic prostate cancer tumors differ among Hispanic-White and non-Hispanic White men. Notably, we found no significant differences in the prevalence of actionable genetic alterations between the groups, suggesting that a significant number of Hispanic men could benefit from the development of targeted therapies.

Computational methods and translational applications for targeted next-generation sequencing platforms.

During the past decade, next-generation sequencing (NGS) technologies have become widely adopted in cancer research and clinical care. Common applications within the clinical setting include patient stratification into relevant molecular subtypes, identification of biomarkers of response and resistance to targeted and systemic therapies, assessment of heritable cancer risk based on known pathogenic variants, and longitudinal monitoring of treatment response. The need for efficient downstream processing and reliable interpretation of sequencing data has led to the development of novel algorithms and computational pipelines, as well as structured knowledge bases that link genomic alterations to currently available drugs and ongoing clinical trials. Cancer centers around the world use different types of targeted solid-tissue and blood based NGS assays to analyze the genomic and transcriptomic profile of patients as part of their routine clinical care. Recently, cross-institutional collaborations have led to the creation of large pooled datasets that can offer valuable insights into the genomics of rare cancers.

Rapid ocean acidification and protracted Earth system recovery followed the end-Cretaceous Chicxulub impact.

Mass extinction at the Cretaceous-Paleogene (K-Pg) boundary coincides with the Chicxulub bolide impact and also falls within the broader time frame of Deccan trap emplacement. Critically, though, empirical evidence as to how either of these factors could have driven observed extinction patterns and carbon cycle perturbations is still lacking. Here, using boron isotopes in foraminifera, we document a geologically rapid surface-ocean pH drop following the Chicxulub impact, supporting impact-induced ocean acidification as a mechanism for ecological collapse in the marine realm. Subsequently, surface water pH rebounded sharply with the extinction of marine calcifiers and the associated imbalance in the global carbon cycle. Our reconstructed water-column pH gradients, combined with Earth system modeling, indicate that a partial ∼50% reduction in global marine primary productivity is sufficient to explain observed marine carbon isotope patterns at the K-Pg, due to the underlying action of the solubility pump. While primary productivity recovered within a few tens of thousands of years, inefficiency in carbon export to the deep sea lasted much longer. This phased recovery scenario reconciles competing hypotheses previously put forward to explain the K-Pg carbon isotope records, and explains both spatially variable patterns of change in marine productivity across the event and a lack of extinction at the deep sea floor. In sum, we provide insights into the drivers of the last mass extinction, the recovery of marine carbon cycling in a postextinction world, and the way in which marine life imprints its isotopic signal onto the geological record.

A Deep Learning-Based Chinese Semantic Parser for the Almond Virtual Assistant.

Almond is an extendible open-source virtual assistant designed to help people access Internet services and IoT (Internet of Things) devices. Both are referred to as skills here. Service providers can easily enable their devices for Almond by defining proper APIs (Application Programming Interfaces) for ThingTalk in Thingpedia. ThingTalk is a virtual assistant programming language, and Thingpedia is an application encyclopedia. Almond uses a large neural network to translate user commands in natural language into ThingTalk programs. To obtain enough data for the training of the neural network, Genie was developed to synthesize pairs of user commands and corresponding ThingTalk programs based on a natural language template approach. In this work, we extended Genie to support Chinese. For 107 devices and 261 functions registered in Thingpedia, 649 Chinese primitive templates and 292 Chinese construct templates were analyzed and developed. Two models, seq2seq (sequence-to-sequence) and MQAN (multiple question answer network), were trained to translate user commands in Chinese into ThingTalk programs. Both models were evaluated, and the experiment results showed that MQAN outperformed seq2seq. The exact match, BLEU, and F1 token accuracy of MQAN were 0.7, 0.82, and 0.88, respectively. As a result, users could use Chinese in Almond to access Internet services and IoT devices registered in Thingpedia.

Advancing undergraduate synthetic biology education: insights from a Canadian iGEM student perspective.

The last two decades have seen vigorous activity in synthetic biology research and the ever-increasing applications of these technologies. However, pedagogical research pertaining to teaching synthetic biology is scarce, especially when compared to other science and engineering disciplines. Within Canada, there are only three universities that offer synthetic biology programs, two of which are at the undergraduate level. Rather than taking place in formal academic settings, many Canadian undergraduate students are introduced to synthetic biology through participation in the annual International Genetically Engineered Machine (iGEM) competition. Although the iGEM competition has had a transformative impact on synthetic biology training in other nations, its impact in Canada has been relatively modest. Consequently, the iGEM competition remains a major setting for synthetic biology education in Canada. To promote further development of synthetic biology education, we surveyed undergraduate students from the Canadian iGEM design teams of 2019. We extracted insights from these data using qualitative analysis to provide recommendations for best teaching practices in synthetic biology undergraduate education, which we describe through our proposed Framework for Transdisciplinary Synthetic Biology Education (FTSBE).

Analysis on GENIE reveals novel recurrent variants that affect molecular diagnosis of sizable number of cancer patients.

BACKGROUND: Significant numbers of variants detected in cancer patients are often left labeled only as variants of unknown significance (VUS). In order to expand precision medicine to a wider population, we need to extend our knowledge of pathogenicity and drug response in the context of VUS's. METHODS: In this study, we analyzed variants from AACR Project GENIE Consortium APG (Cancer Discov 7:818-831, 2017) and compared them to the COSMIC database Forbes et al. (Nucleic Acids Res 43:D805-811, 2015) to identify recurrent variants that would merit further study. We filtered out known hotspot variants, inactivating variants in tumor suppressors, and likely benign variants by comparing with COSMIC and ExAC Lee et al. (Science 337:967-971, 2012). RESULTS: We have identified 45,933 novel variants with unknown significance unique to GENIE. In our analysis, we found on average six variants per patient where two could be considered as pathogenic or likely pathogenic and the majority are VUS's. More importantly, we have discovered 730 recurrent variants that appear more than 3 times in GENIE but less than 3 in COSMIC. If we combine the recurrences of GENIE and COSMIC for all variants, 2586 are newly identified as occurring more than 3 times than when using COSMIC alone. CONCLUSIONS: Although it would be inappropriate to blindly accept these recurrent variants as pathogenic, they may warrant higher priority than other observed VUS's. These newly identified recurrent variants might affect the molecular profiles of approximately 1 in 6 patients. Further analysis and characterization of these variants in both research and clinical contexts will improve patient treatments and the development of new therapeutics.

Using the gini coefficient to measure the chemical diversity of small-molecule libraries.

Modern databases of small organic molecules contain tens of millions of structures. The size of theoretically available chemistry is even larger. However, despite the large amount of chemical information, the "big data" moment for chemistry has not yet provided the corresponding payoff of cheaper computer-predicted medicine or robust machine-learning models for the determination of efficacy and toxicity. Here, we present a study of the diversity of chemical datasets using a measure that is commonly used in socioeconomic studies. We demonstrate the use of this diversity measure on several datasets that were constructed to contain various congeneric subsets of molecules as well as randomly selected molecules. We also apply our method to a number of well-known databases that are frequently used for structure-activity relationship modeling. Our results show the poor diversity of the common sources of potential lead compounds compared to actual known drugs. © 2016 Wiley Periodicals, Inc.

Transgenic animals resistant to infectious diseases.

The list of transgenic animals developed to test ways of producing livestock resistant to infectious disease continues to grow. Although the basic techniques for generating transgenic animals have not changed very much in the ten years since they were last reviewed for the World Organisation for Animal Health, one recent fundamental technological advance stands to revolutionise genome engineering. The advent of technically simple and efficient site-specific gene targeting has profound implications for genetically modifying livestock species.

La liste d'animaux transgéniques créés pour tester des moyens de produire des espèces d'élevage résistantes aux maladies infectieuses ne cesse de croître. Bien que les techniques de base pour créer ces animaux transgéniques n'aient pas beaucoup changé depuis la dernière synthèse publiée il y a dix ans sur le sujet par l'Organisation mondiale de la santé animale, une avancée technologique majeure mise au point récemment pourrait révolutionner le génie génétique. La capacité de modifier de manière spécifique des sites du génome ciblés au moyen d'une technique simple et efficace aura de profondes conséquences pour la modification génétique des espèces animales d'élevage.

La lista de animales transgénicos creados con la finalidad de ensayar formas de producción de ganado resistente a enfermedades infecciosas no deja de ir en aumento. Aunque las técnicas básicas para generar animales transgénicos no han cambiado mucho en los diez años transcurridos desde que la Organización Mundial de Sanidad Animal las examinó por última vez, últimamente ha habido un avance tecnológico que está llamado a revolucionar la ingeniería genómica. El advenimiento de técnicas sencillas y eficaces para modificar sitios génicos específicos (gene targeting) influirá profundamente en las labores de modificación genética de especies ganaderas.

ROAD value-based detection for GAE enhanced interference mitigation in LDACS.

The increasing volume of air traffic has placed significant stress on the current Air Traffic Management (ATM) systems, especially concerning the use of Very High Frequency (VHF) communication bands. As air traffic continues to grow, the limitations of the existing spectrum and infrastructure necessitate significant upgrades to ensure safety, efficiency, and capacity. The modernization of air traffic management systems has led to the development and introduction of the L-band Digital Aeronautical Communication System (LDACS), a new communication protocol. LDACS is designed to operate alongside existing L-band systems, ensuring compatibility with legacy users. The coexistence of LDACS with legacy systems poses significant interference challenges, as any disruption in data retrieval can critically impact flight safety. This paper proposes four potential interference mitigation techniques that LDACS can employ to detect and reduce the primary source of interference: Distance Measuring Equipment (DME). The authors introduce a prototype LDACS receiver that uses Rank-Ordered Absolute Differences (ROAD) statistics for effective interference sensing and employs GAE-enhanced pulse peak processors to mitigate Distance Measuring Equipment (DME) interference. Unlike the current GAE-enhanced pulse peak processors, the proposed methods use ROAD value-based detection for identifying DME interference. The performance of the proposed methods - ROAD GAE enhanced Pulse Peak Attenuator (RGPPA), ROAD GAE enhanced Pulse Peak Limiter (RGPPL), ROAD joint GAE enhanced Pulse Peak Attenuator (RJGPPA), and ROAD joint GAE enhanced Pulse Peak Limiter (RJGPPL) is analyzed across different threshold ROAD values to determine their efficacy in various signal conditions. Moreover, the performance of the proposed methods is compared to existing methods such as conventional pulse blanking and GAE-enhanced nonlinear devices, which use the amplitude of the received signal for the detection of interference. Furthermore, the proposed method's performance is compared to another method, ROAD PB, which uses ROAD statistics to detect DME interference and pulse blanking for DME mitigation. The comparative results show that the proposed methods outperformed conventional pulse blanking and ROADPB. Besides, these methods outperformed existing GAE-enhanced methods for their optimum threshold ROAD value.

Molecular profiling of colorectal cancer in a genetically admixed Hispanic population.

BACKGORUND: Colorectal cancer (CRC) is among the leading causes of cancer-related deaths among Hispanics living in the United States (USH). Understanding the most common carcinogenic molecular pathways that affect Hispanics with CRC is crucial to guide research efforts in developing new therapeutic modalities incorporating genomically diverse populations. Tumor profiling techniques help identify actionable alternatives to recommend treatment and improve survival in cancer patients. METHODS: We conducted a secondary data analysis to evaluate the mutational profile of 218 CRC tumors in Hispanics living in Puerto Rico (PRH) who underwent next-generation sequencing (NGS) testing from 2015 to 2020. We compared the prevalence of CRC tumor somatic mutations in PRHs with the mutational profiles reported for CRC from The Cancer Genome Atlas (TCGA) Pan-Cancer Clinical Data, the AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE)-Non-Hispanic, and GENIE-Hispanic datasets. RESULTS: Among the top mutated genes in CRC tumors in PRHs were APC, TP53, and KRAS, which had significantly higher mutational frequencies in PRH compared to the examined datasets, including GENIE-Hispanics. The most frequent gene amplifications for PRH were CDX2, CDKN1B, and HNRNPA2B1. Targetable biomarkers for CRC, such as microsatellite instability-high (MSI), wild-type KRAS, wild-type NRAS, V600E BRAF, and ERBB2 gene amplifications were found in 2.0%, 43.8%, 97.8%, 3.9%, and 2.3%, respectively, of PRH patients. CONCLUSION: This is the first study to report the mutational profile of CRC tumors in PRHs and make comparisons to other non-Hispanic and USH populations.

A Pan-Cancer Assessment of RB1/TP53 Co-Mutations.

Nearly all tumors have multiple mutations in cancer-causing genes. Which of these mutations act in tandem with other mutations to drive malignancy and also provide therapeutic vulnerability? To address this fundamental question, we conducted a pan-cancer screen of co-mutation enrichment (looking for two genes mutated together in the same tumor at a statistically significant rate) using the AACR-GENIE 11.0 data (AACR, Philadelphia, PA, USA). We developed a web tool for users to review results and perform ad hoc analyses. From our screen, we identified a number of such co-mutations and their associated lineages. Here, we focus on the RB1/TP53 co-mutation, which we discovered was the most frequently observed co-mutation across diverse cancer types, with particular enrichment in small cell carcinomas, neuroendocrine carcinomas, and sarcomas. Furthermore, in many cancers with a substantial fraction of co-mutant tumors, the presence of concurrent RB1/TP53 mutations is associated with poor clinical outcomes. From pan-cancer cell line multi-omics and functional screening datasets, we identified many targetable co-mutant-specific molecular alterations. Overall, our analyses revealed the prevalence, cancer type-specificity, clinical significance, and therapeutic vulnerabilities of the RB1/TP53 co-mutation in the pan-cancer landscape and provide a roadmap forward for future clinical translational research.

Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice.

Nitrogen (N) and Water (W) - two resources critical for crop productivity - are becoming increasingly limited in soils globally. To address this issue, we aim to uncover the gene regulatory networks (GRNs) that regulate nitrogen use efficiency (NUE) - as a function of water availability - in Oryza sativa, a staple for 3.5 billion people. In this study, we infer and validate GRNs that correlate with rice NUE phenotypes affected by N-by-W availability in the field. We did this by exploiting RNA-seq and crop phenotype data from 19 rice varieties grown in a 2x2 N-by-W matrix in the field. First, to identify gene-to-NUE field phenotypes, we analyzed these datasets using weighted gene co-expression network analysis (WGCNA). This identified two network modules ("skyblue" & "grey60") highly correlated with NUE grain yield (NUEg). Next, we focused on 90 TFs contained in these two NUEg modules and predicted their genome-wide targets using the N-and/or-W response datasets using a random forest network inference approach (GENIE3). Next, to validate the GENIE3 TF→target gene predictions, we performed Precision/Recall Analysis (AUPR) using nine datasets for three TFs validated in planta. This analysis sets a precision threshold of 0.31, used to "prune" the GENIE3 network for high-confidence TF→target gene edges, comprising 88 TFs and 5,716 N-and/or-W response genes. Next, we ranked these 88 TFs based on their significant influence on NUEg target genes responsive to N and/or W signaling. This resulted in a list of 18 prioritized TFs that regulate 551 NUEg target genes responsive to N and/or W signals. We validated the direct regulated targets of two of these candidate NUEg TFs in a plant cell-based TF assay called TARGET, for which we also had in planta data for comparison. Gene ontology analysis revealed that 6/18 NUEg TFs - OsbZIP23 (LOC_Os02g52780), Oshox22 (LOC_Os04g45810), LOB39 (LOC_Os03g41330), Oshox13 (LOC_Os03g08960), LOC_Os11g38870, and LOC_Os06g14670 - regulate genes annotated for N and/or W signaling. Our results show that OsbZIP23 and Oshox22, known regulators of drought tolerance, also coordinate W-responses with NUEg. This validated network can aid in developing/breeding rice with improved yield on marginal, low N-input, drought-prone soils.

Evaluating the performance of random forest and iterative random forest based methods when applied to gene expression data.

Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses. There exists multiple network inference tools to produce these gene-to-gene networks from matrices of gene expression data. Random Forest-Leave One Out Prediction (RF-LOOP) is a method that has been shown to be efficient at producing these gene-to-gene networks, frequently known as GEne Network Inference with Ensemble of trees (GENIE3). Random Forest can be replaced in this process by iterative Random Forest (iRF), which performs variable selection and boosting. Here we validate that iterative Random Forest-Leave One Out Prediction (iRF-LOOP) produces higher quality networks than GENIE3 (RF-LOOP). We use both synthetic and empirical networks from the Dialogue for Reverse Engineering Assessment and Methods (DREAM) Challenges by Sage Bionetworks, as well as two additional empirical networks created from Arabidopsis thaliana and Populus trichocarpa expression data.

Utility of Extrapolating Human S1500+ Genes to the Whole Transcriptome: Tunicamycin Case Study.

The TempO-Seq S1500+ platform(s), now available for human, mouse, rat, and zebrafish, measures a discrete number of genes that are representative of biological and pathway co-regulation across the entire genome in a given species. While measurement of these genes alone provides a direct assessment of gene expression activity, extrapolating expression values to the whole transcriptome (~26 000 genes in humans) can estimate measurements of non-measured genes of interest and increases the power of pathway analysis algorithms by using a larger background gene expression space. Here, we use data from primary hepatocytes of 54 donors that were treated with the endoplasmic reticulum (ER) stress inducer tunicamycin and then measured on the human S1500+ platform containing ~3000 representative genes. Measurements for the S1500+ genes were then used to extrapolate expression values for the remaining human transcriptome. As a case study of the improved downstream analysis achieved by extrapolation, the "measured only" and "whole transcriptome" (measured + extrapolated) gene sets were compared. Extrapolation increased the number of significant genes by 49%, bringing to the forefront many that are known to be associated with tunicamycin exposure. The extrapolation procedure also correctly identified established tunicamycin-related functional pathways reflected by coordinated changes in interrelated genes while maintaining the sample variability observed from the "measured only" genes. Extrapolation improved the gene- and pathway-level biological interpretations for a variety of downstream applications, including differential expression analysis, gene set enrichment pathway analysis, DAVID keyword analysis, Ingenuity Pathway Analysis, and NextBio correlated compound analysis. The extrapolated data highlight the role of metabolism/metabolic pathways, the ER, immune response, and the unfolded protein response, each of which are key activities associated with tunicamycin exposure that were unrepresented or underrepresented in one or more of the analyses of the original "measured only" dataset. Furthermore, the inclusion of the extrapolated genes raised "tunicamycin" from third to first upstream regulator in Ingenuity Pathway Analysis and from sixth to second most correlated compound in NextBio analysis. Therefore, our case study suggests an approach to extend and enhance data from the S1500+ platform for improved insight into biological mechanisms and functional outcomes of diseases, drugs, and other perturbations.