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Clinical management of non-small cell lung cancer (NSCLC) requires accurate identification of tumor-specific genetic alterations to inform treatment options. Historically, providers have relied on single-gene testing (SGT) for actionable variants due to a perception of cost-effectiveness and/or efficient turnaround time compared to next-generation sequencing (NGS). However, not all actionable variants may be evaluated through SGT modalities, and an SGT approach can exhaust valuable tissue needed for more comprehensive analyses. In contrast, comprehensive genomic profiling (CGP) tests employ NGS to sequence megabases of DNA and RNA to evaluate all relevant molecular alterations, providing a broader genetic profile to identify actionable alterations that SGT may not accurately or efficiently assess. Here, we briefly describe four cases from a large reference laboratory in which actionable alterations were identified by CGP but not SGT. The discussion highlights the utility and advantages of using CGP to provide complete and timely treatment options and clinical trial opportunities for patients with NSCLC. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the leading cause of cancer-related mortality among women, and hormone receptor (HR)-positive subtype makes up the majority of all cases. The standard of care in HR+/HER2− metastatic BC (MBC) is endocrine therapy (ET) plus a CDK4/6 inhibitor (CDK4/6i). ESR1 mutations could impair the clinical efficacy of the ETs. Similarly, PIK3CA mutations may serve as a negative prognostic marker. Furthermore, MBC is challenging to treat despite new drug approvals. Our patient received multiple lines of ET ± CDK4/6i and chemotherapy but persistently progressed after each or stopped the treatment due to adverse events. Here we showed for the first time that an all-oral combination of elacestrant plus alpelisib was feasible, tolerable, and clinically active in an ESR1 and PIK3CA co-mutated and heavily pretreated patient. We achieved a remarkable response in the metastatic lesions with minor toxicity issues. This case highlights the importance of utilizing up-to-date therapeutic agents and reactive decision-making during personalized cancer treatment. [ABSTRACT FROM AUTHOR]

Objectives: The primary goal of this research is to develop treatment-related genomic predictive markers for non-small cell lung cancer by integrating various machine learning algorithms that recommends near-optimal individualized patient treatment for chemotherapy in an effort to maximize efficacy or minimize treatment-related toxicity. This research can contribute toward developing a more refined, accurate and effective therapy accounting for specific patient needs. Methods: To accomplish our research goal, we implement ensemble learning algorithms, bagging with regularized Cox regression models and nonparametric tree-based models via Random Survival Forests. A comprehensive meta-database was compiled from the NCBI Gene Expression Omnibus data repository for lung cancer patients to capture and utilize complex genomic patterns that can predict treatment outcomes more accurately. Results: The developed novel prediction algorithm demonstrates the ability to support complex clinical decision-making processes in the treatment of NSCLC. It effectively addresses patient heterogeneity, offering predictions that are both refined and personalized in improving the precision of chemotherapy regimens prescribed to the eligible patients. Conclusion: This research should contribute substantial advancement of cancer treatments by improving the accuracy and efficacy of chemotherapy treatments for a targeted group of patients who need the right treatment. The integration of complex machine learning techniques with genomic data holds substantial potential to transform current cancer treatment paradigms by providing robust support in clinical decision-making. [ABSTRACT FROM AUTHOR]

Simple Summary: The OncoIndx® platform is an NGS assay designed and developed to identify critical mutations that help in therapeutic decision-making. The OncoIndx® panel targets major exons and a few selected introns of 1080 cancer-associated and actionable genes. The test analyzes complex biomarkers such as single nucleotide variants (SNVs), copy number alterations (CNAs), specific gene fusions, and many more. This study validates the overall sensitivity and efficiency of the test using standard references, clinical samples, and U.S. Food and Drug Administration (FDA)-approved cross-laboratory samples. The ultimate goal of this research is to benchmark the assay against the current guidelines and increase the reliability of the test, thus increasing the confidence of medical professionals for better personalized therapeutic intervention. Comprehensive next-generation sequencing (NGS) assays enable the identification of clinically relevant mutations, enhancing the capability for targeted therapeutic interventions. In addition, genomic alterations driving the oncogenic roadmap and leading to resistance mechanisms are reshaping precision oncology. We report the workflow and clinical and technical validation of the OncoIndx® NGS platform—a comprehensive genomic profiling (CGP)-based assay for pan-cancer investigation. We evaluated the concordance between the OncoIndx® test findings and clinically established hotspot detection using SeraSeq reference standards. OncoIndx is a hybridization capture-based NGS assay for the targeted deep sequencing of all exons and selected introns of 1080 cancer-related genes. We show the outcome in the form of tier I and tier II single nucleotide variants (SNVs), copy number alterations (CNAs), and specific gene fusions. OncoIndx® also informs genome-wide tumor mutational burden (TMB), microsatellite instability (MSI), homologous recombination deficiency (HRD), and genomic loss of heterozygosity (gLOH). A total of 63 samples were utilized for validation with reference standards, clinical samples, and orthogonal assessment for genomic alterations. In addition, 49 cross-laboratory samples were validated for microsatellite instability (MSI), and for the tumor mutation burden (TMB), 18 samples as reference standards, 6 cross-laboratory samples, and 29 TCGA samples were utilized. We show a maximum clinical sensitivity of 98% and a positive predictive value (PPV) of 100% for the clinically actionable genomic variants detected by the assay. In addition, we demonstrate analytical validation with the performance of the assay, limit of detection (LoD), precision, and orthogonal concordance for various types of SVs, CNAs, genomic rearrangements, and complex biomarkers like TMB, MSI, and HRD. The assay offers reliable genomic predictions with the high-precision detection of actionable variants, validated by established reference standards. [ABSTRACT FROM AUTHOR]

Background: Comprehensive genomic profiling (CGP) can aid the discovery of clinically useful, candidate antitumor agents; however, the variant annotations sometimes differ among the various types of CGP tests as well as the public database. The aim of this study is to clarify the genomic landscape of evaluating detected variants in patients with a malignant solid tumor. Methods: The present, cross-sectional study used data from 57,084 patients with a malignant solid tumor who underwent CGP at the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) between June 1, 2019 and August 18, 2023. The pathogenicity of the variants was annotated using public databases. Results: As a result of re-annotation of the detected variants, 20.1% were pathogenic and 1.4% were benign. The mean number of pathogenic variants was 4.30 (95% confidence interval: 4.27–4.32) per patient. Of the entire cohort, 5.7% had no pathogenic variant. The co-occurrence of the genes depended on the tumor type. Germline findings were detected in 6.2%, 8.8%, and 15.8% of the patients using a tumor/normal panel, tumor-only panel, and liquid panel, respectively, with the most common gene being BRCA2 followed by TP53 and BRCA1. Conclusions: The detected variants should be re-annotated because several benign variants or variants of unknown significance were included in the CGP, and the genomic landscape derived from these results will help researchers and physicians interpret the results of CGP tests. The method of extracting presumptive, germline, pathogenic variants from patients using a tumor-only panel or circulating tumor DNA panel requires improvement. [ABSTRACT FROM AUTHOR]

This study investigates the impact of Hashimoto’s thyroiditis (HT), an autoimmune disorder, on the papillary thyroid cancer (PTC) microenvironment using a dataset of 140,456 cells from 11 patients. By comparing PTC cases with and without HT, we identify HT-specific cell populations (HASCs) and their role in creating a TSH-suppressive environment via mTE3, nTE0, and nTE2 thyroid cells. These cells facilitate intricate immune–stromal communication through the MIF–(CD74+CXCR4) axis, emphasizing immune regulation in the TSH context. In the realm of personalized medicine, our HASC-focused analysis within the TCGA-THCA dataset validates the utility of HASC profiling for guiding tailored therapies. Moreover, we introduce a novel, objective method to determine K- means clustering coefficients in copy number variation inference from bulk RNA- seq data, mitigating the arbitrariness in conventional coefficient selection. Collectively, our research presents a detailed single-cell atlas illustrating HT– PTC interactions, deepening our understanding of HT’s modulatory effects on PTC microenvironments. It contributes to our understanding of autoimmunity– carcinogenesis dynamics and charts a course for discovering new therapeutic targets in PTC, advancing cancer genomics and immunotherapy research. [ABSTRACT FROM AUTHOR]

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Abstract Background Health disparities have been highlighted among patient with prostate adenocarcinoma (PRAD) due to ethnicity. Mexican men present a more aggressive disease than other patients resulting in less favorable treatment outcome. We aimed to identify the mutational landscape which could help to reduce the health disparities among minority groups and generate the first genomics exploratory study of PRAD in Mexican patients. Methods Paraffin-embedded formalin-fixed tumoral tissue from 20 Mexican patients with early-stage PRAD treated at The Instituto Nacional de Cancerología, Mexico City from 2017 to 2019 were analyzed. Tumoral DNA was prepared for whole exome sequencing, the resulting files were mapped against h19 using BWA-MEM. Strelka2 and Lancet packages were used to identify single nucleotide variants (SNV) and insertions or deletions. FACETS was used to determine somatic copy number alterations (SCNA). Cancer Genome Interpreter web interface was used to determine the clinical relevance of variants. Results Patients were in an early clinical stage and had a mean age of 59.55 years (standard deviation [SD]: 7.1 years) with 90% of them having a Gleason Score of 7. Follow-up time was 48.50 months (SD: 32.77) with recurrences and progression in 30% and 15% of the patients, respectively. NUP98 (20%), CSMD3 (15%) and FAT1 (15%) were the genes most frequently affected by SNV; ARAF (75%) and ZNF419 (70%) were the most frequently affected by losses and gains SNCA’s. One quarter of the patients had mutations useful as biomarkers for the use of PARP inhibitors, they comprise mutations in BRCA, RAD54L and ATM. SBS05, DBS03 and ID08 were the most common mutational signatures present in this cohort. No associations with recurrence or progression were identified. Conclusions This pilot study reveals the mutational landscape of early-stage prostate adenocarcinoma in Mexican men, providing a first approach to understand the mutational patterns and actionable mutations in early prostate cancer can inform personalized treatment approaches and reduce the underrepresentation in genomic cancer studies.

Background: Health disparities have been highlighted among patient with prostate adenocarcinoma (PRAD) due to ethnicity. Mexican men present a more aggressive disease than other patients resulting in less favorable treatment outcome. We aimed to identify the mutational landscape which could help to reduce the health disparities among minority groups and generate the first genomics exploratory study of PRAD in Mexican patients. Methods: Paraffin-embedded formalin-fixed tumoral tissue from 20 Mexican patients with early-stage PRAD treated at The Instituto Nacional de Cancerología, Mexico City from 2017 to 2019 were analyzed. Tumoral DNA was prepared for whole exome sequencing, the resulting files were mapped against h19 using BWA-MEM. Strelka2 and Lancet packages were used to identify single nucleotide variants (SNV) and insertions or deletions. FACETS was used to determine somatic copy number alterations (SCNA). Cancer Genome Interpreter web interface was used to determine the clinical relevance of variants. Results: Patients were in an early clinical stage and had a mean age of 59.55 years (standard deviation [SD]: 7.1 years) with 90% of them having a Gleason Score of 7. Follow-up time was 48.50 months (SD: 32.77) with recurrences and progression in 30% and 15% of the patients, respectively. NUP98 (20%), CSMD3 (15%) and FAT1 (15%) were the genes most frequently affected by SNV; ARAF (75%) and ZNF419 (70%) were the most frequently affected by losses and gains SNCA's. One quarter of the patients had mutations useful as biomarkers for the use of PARP inhibitors, they comprise mutations in BRCA, RAD54L and ATM. SBS05, DBS03 and ID08 were the most common mutational signatures present in this cohort. No associations with recurrence or progression were identified. Conclusions: This pilot study reveals the mutational landscape of early-stage prostate adenocarcinoma in Mexican men, providing a first approach to understand the mutational patterns and actionable mutations in early prostate cancer can inform personalized treatment approaches and reduce the underrepresentation in genomic cancer studies. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, the researchers aimed to improve the prognostic information provided by the standard One-Step Nucleic Acid Amplification (OSNA) assay used to detect breast cancer (BC) metastasis in sentinel lymph nodes (SLNs). They analysed the expression of an immune gene panel in SLNs from Luminal A early-stage BC (cT1-T2 N0) patients, including those with and without subclinical metastasis. The study identified two distinct immune response profiles—one showing an adaptive anti-tumour immune response, and another with a more undifferentiated response. The researchers also identified seven key immunoregulatory genes that could serve as potential targets for immunotherapy. These findings suggest that analysing immune gene expression in SLNs can provide additional prognostic information beyond the OSNA assay results and may help guide personalised treatment approaches for early-stage BC patients. In the management of early-stage breast cancer (BC), lymph nodes (LNs) are typically characterised using the One-Step Nucleic Acid Amplification (OSNA) assay, a standard procedure for assessing subclinical metastasis in sentinel LNs (SLNs). The pivotal role of LNs in coordinating the immune response against BC is often overlooked. Our aim was to improve prognostic information provided by the OSNA assay and explore immune-related gene signatures in SLNs. The expression of an immune gene panel was analysed in SLNs from 32 patients with Luminal A early-stage BC (cT1-T2 N0). Using an unsupervised approach based on these expression values, this study identified two clusters, regardless of the SLN invasion: one evidencing an adaptive anti-tumoral immune response, characterised by an increase in naive B cells, follicular T helper cells, and activated NK cells; and another with a more undifferentiated response, with an increase in the activated-to-resting dendritic cells (DCs) ratio. Through a protein—protein interaction (PPI) network, we identified seven immunoregulatory hub genes: CD80, CD40, TNF, FCGR3A, CD163, FCGR3B, and CCR2. This study shows that, in Luminal A early-stage BC, SLNs gene expression studies enable the identification of distinct immune profiles that may influence prognosis stratification and highlight key genes that could serve as potential targets for immunotherapy. [ABSTRACT FROM AUTHOR]

The rapid advancement of sequencing technologies has led to the identification of numerous mutations in cancer genomes, many of which are variants of unknown significance (VUS). Computational models are increasingly being used to predict the functional impact of these mutations, in both coding and noncoding regions. Integration of these models with emerging genomic datasets will refine our understanding of mutation effects and guide clinical decision making. Future advancements in modeling protein interactions and transcriptional regulation will further enhance our ability to interpret VUS. Periodic incorporation of these developments into VUS reclassification practice has the potential to significantly improve personalized cancer care. [ABSTRACT FROM AUTHOR]

As the projected incidence and mortality of cancer in Sub-Saharan Africa (SSA) rises to epidemic proportions, it is imperative that more is done to identify the genomic differences and commonalities between patients of African and European ancestry to fulfil the promise of precision oncology. Here, we summarize the utility of precision oncology approaches, with a focus on comprehensive genomic profiling (CGP) and consolidate examples of national and international consortia that are driving the field forward. We describe the importance of genomic diversity and its relevance in cancer, and propose recommendations, success factors and desired outcomes for precision oncology consortia to adopt in SSA. Through this, we hope to catalyze the initiation of such projects and to contribute to improving cancer patient outcomes in the region. [ABSTRACT FROM AUTHOR]

Motivation Metastasis formation is a hallmark of cancer lethality. Yet, metastases are generally unobservable during their early stages of dissemination and spread to distant organs. Genomic datasets of matched primary tumors and metastases may offer insights into the underpinnings and the dynamics of metastasis formation. Results We present metMHN, a cancer progression model designed to deduce the joint progression of primary tumors and metastases using cross-sectional cancer genomics data. The model elucidates the statistical dependencies among genomic events, the formation of metastasis, and the clinical emergence of both primary tumors and their metastatic counterparts. metMHN enables the chronological reconstruction of mutational sequences and facilitates estimation of the timing of metastatic seeding. In a study of nearly 5000 lung adenocarcinomas, metMHN pinpointed TP53 and EGFR as mediators of metastasis formation. Furthermore, the study revealed that post-seeding adaptation is predominantly influenced by frequent copy number alterations. Availability and implementation All datasets and code are available on GitHub at https://github.com/cbg-ethz/metMHN. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To phenotype the psychoneurologic (PN) symptom cluster in individuals with metastatic breast cancer and associate those phenotypes with individual characteristics and cancer genomic variables from circulating tumor DNA. SAMPLE & SETTING: This study included 201 individuals with metastatic breast cancer recruited in western Pennsylvania. METHODS & VARIABLES: A descriptive, crosssectional design was used. Symptom data were collected via the MD Anderson Symptom Inventory, and cancer genomic data were collected via ultralow-pass whole-genome sequencing of circulating tumor DNA from participant blood. RESULTS: Three distinct PN symptom phenotypes were described in a population with metastatic breast cancer: mild symptoms, moderate symptoms, and severe mood-related symptoms. Breast cancer TP53 deletion was significantly associated with membership in a moderate to severe symptoms phenotype (p = 0.013). IMPLICATIONS FOR NURSING: Specific cancer genomic changes associated with increased genomic instability may be predictive of PN symptoms. This finding may enable proactive treatment or reveal new therapeutic targets for symptom management. [ABSTRACT FROM AUTHOR]

The number of targeted therapies licenced for non-small cell lung cancer (NSCLC) has increased substantially in recent years, resulting in a major increase in demand for genomic testing of tissue specimens. The range of different alteration types that are now actionable in NSCLC is reflected in the complexity of current testing approaches. Cellular pathologists hold a key role in triaging tissue specimens for genomic biomarker testing and should be familiar with the genomic alterations seen in NSCLC, the testing strategies utilised and how to interpret molecular pathology reports in order to effectively support the management of NSCLC patients. [ABSTRACT FROM AUTHOR]

Background: Next-generation sequencing (NGS) technologies offer fast and inexpensive identification of DNA sequences. Somatic sequencing is among the primary applications of NGS, where acquired (non-inherited) variants are based on comparing diseased and healthy tissues from the same individual. Somatic mutations in genetic diseases such as cancer are tightly associated with genomic instability. Genomic instability increases heterogenity, complicating sequencing efforts further, a task already challenged by the presence of short reads and repetitions in human DNA. This leads to low concordance among studies and limits reproducibility. This limitation is a significant problem since identified mutations in somatic sequencing are major biomarkers for diagnosis and the primary input of targeted therapies. Benchmarking studies were conducted to assess the error rates and increase reproducibility. Unfortunately, the number of somatic benchmarking sets is very limited due to difficulties in validating true somatic variants. Moreover, most NGS benchmarking studies are based on relatively simpler germline (inherited) sequencing. Recently, a comprehensive somatic sequencing benchmarking set was published by Sequencing Quality Control Phase 2 (SEQC2). We chose this dataset for our experiments because it is a well-validated, cancer-focused dataset that includes many tumor/normal biological replicates. Our study has two primary goals. First goal is to determine how replicate-based consensus approaches can improve the accuracy of somatic variant detection systems. Second goal is to develop highly predictive machine learning (ML) models by employing replicate-based consensus variants as labels during the training phase. Results: Ensemble approaches that combine alternative algorithms are relatively common; here, as an alternative, we study the performance enhancement potential of biological replicates. We first developed replicate-based consensus approaches that utilize the biological replicates available in this study to improve variant calling performance. Subsequently, we trained ML models using these biological replicates and achieved performance comparable to optimal ML models, those trained using high-confidence variants identified in advance. Conclusions: Our replicate-based consensus approach can be used to improve variant calling performance and develop efficient ML models. Given the relative ease of obtaining biological replicates, this strategy allows for the development of efficient ML models tailored to specific datasets or scenarios. [ABSTRACT FROM AUTHOR]

BackgroundPulmonary hypertension (PH) can lead to right ventricular hypertrophy, significantly increasing mortality rates. This study aims to clarify PH-specific metabolites and their impact on genomic and post-translational modifications (PTMs) in cancer, evaluating DHA and EPA’s therapeutic potential to mitigate oxidative stress and inflammation.MethodsData from 289,365 individuals were analyzed using Mendelian randomization to examine 1,400 metabolites’ causal roles in PH. Anti-inflammatory and antioxidative effects of DHA and EPA were tested in RAW 264.7 macrophages and cancer cell lines (A549, HCT116, HepG2, LNCaP). Genomic features like CNVs, DNA methylation, tumor mutation burden (TMB), and PTMs were analyzed. DHA and EPA’s effects on ROS production and cancer cell proliferation were assessed.ResultsWe identified 57 metabolites associated with PH risk and examined key tumor-related pathways through promoter methylation analysis. DHA and EPA significantly reduced ROS levels and inflammatory markers in macrophages, inhibited the proliferation of various cancer cell lines, and decreased nuclear translocation of SUMOylated proteins during oxidative stress and inflammatory responses. These findings suggest a potential anticancer role through the modulation of stress-related nuclear signaling, as well as a regulatory function on cellular PTMs.ConclusionThis study elucidates metabolic and PTM changes in PH and cancer, indicating DHA and EPA’s role in reducing oxidative stress and inflammation. These findings support targeting these pathways for early biomarkers and therapies, potentially improving disease management and patient outcomes.

Clinical management of non-small cell lung cancer (NSCLC) requires accurate identification of tumor-specific genetic alterations to inform treatment options. Historically, providers have relied on single-gene testing (SGT) for actionable variants due to a perception of cost-effectiveness and/or efficient turnaround time compared to next-generation sequencing (NGS). However, not all actionable variants may be evaluated through SGT modalities, and an SGT approach can exhaust valuable tissue needed for more comprehensive analyses. In contrast, comprehensive genomic profiling (CGP) tests employ NGS to sequence megabases of DNA and RNA to evaluate all relevant molecular alterations, providing a broader genetic profile to identify actionable alterations that SGT may not accurately or efficiently assess. Here, we briefly describe four cases from a large reference laboratory in which actionable alterations were identified by CGP but not SGT. The discussion highlights the utility and advantages of using CGP to provide complete and timely treatment options and clinical trial opportunities for patients with NSCLC.

Background: Pancreatic ductal adenocarcinoma (PDACs) can have actionable genomic alterations at varying frequencies. Harnessing tumour profiling for its treatment is an intricate procedure because the matching of aberrations with therapeutic opportunities is increasingly complex. We evaluated a clinical decision support system (CDSS) that can be used to rationalise this process. Methods: Patients with advanced PDAC were enrolled in a prospective observational study to assess a CDSS, MH Guide. Sequencing data were analysed with the CDSS, and a study-specific molecular tumour board (MTB) assessed reported actionabilities, ineffective drugs, and excess toxicity warnings. We carried out a post hoc analysis of each patient’s treatment within the purview of their medical team and compared it with CDSS and MTB statements. Results: We included 39 patients in the study, 31 had complete CDSS reports and 28 were discussed at the MTB. The CDSS made 80 treatment suggestions based on 61 actionable variants. It highlighted 14 inefficacy marker–drug pairs based on 7 individual markers and flagged a total of 15 individual markers of increased risks of drug-associated toxicity for 28 different cancer treatments. The study-specific MTB endorsed molecularly informed therapeutic options in 21 cases, but there was no exclusion of any drugs based on inefficacy or toxicity markers. The overall evidence underpinning assertions of actionability was weak. After the end of the study, eight patients received targeted treatment without signs of response or clinical benefit. Conclusion: The oncology CDSS MH Guide can be deployed along the care path of patients with advanced PDAC but a critical review of assertions from it is warranted.

Background: The interaction between environmental endocrine-disrupting chemicals, such as Bisphenol A (BPA), and their influence on cancer progression, particularly regarding the GOLPH3 gene in colorectal cancer, remains unclear. Methods: We performed an integrated analysis of transcriptional profiling, clinical data, and bioinformatics analyses utilizing data from the Comparative Toxicogenomics Database and The Cancer Genome Atlas. The study employed ClueGO, Gene Set Enrichment Analysis, and Gene Set Variation Analysis for functional enrichment analysis, alongside experimental assays to examine the effects of BPA exposure on colorectal cancer cell lines, focusing on GOLPH3 expression and its implications for cancer progression. Results: Our findings demonstrated that BPA exposure significantly promoted the progression of colorectal cancer by upregulating GOLPH3, which in turn enhanced the malignant phenotype of colorectal cancer cells. Comparative analysis revealed elevated GOLPH3 protein levels in cancerous tissues versus normal tissues, with single-cell analysis indicating widespread GOLPH3 presence across various cell types in the cancer microenvironment. GOLPH3 was also associated with multiple carcinogenic pathways, including the G2M checkpoint. Furthermore, our investigation into the colorectal cancer microenvironment and genomic mutation signature underscored the oncogenic potential of GOLPH3, exacerbated by BPA exposure. Conclusion: This study provides novel insights into the complex interactions between BPA exposure and GOLPH3 in the context of colorectal cancer, emphasizing the need for heightened awareness and measures to mitigate BPA exposure risks. Our findings advocate for further research to validate these observations in clinical and epidemiological settings and explore potential therapeutic targets within these pathways. [ABSTRACT FROM AUTHOR]

Introduction: The association between mutations in susceptibility genes and the occurrence of ovarian cancer has been extensively studied. Previous research has primarily concentrated on genes involved in the homologous recombination repair pathway, particularly BRCA1 and BRCA2. However, a wider range of genes related to the DNA damage response pathways has not been fully explored. Methods: To investigate the mutation characteristics of cancer susceptibility genes in the Chinese ovarian cancer population and the associations between gene mutations and clinical data, this study initially gathered a total of 1171 Chinese ovarian cancer samples and compiled a dataset of germline mutations in 171 genes. Results: In this study, it was determined that MC1R and PRKDC were high-frequency ovarian cancer susceptibility genes in the Chinese population, exhibiting notable distinctions from those in European and American populations; moreover high-frequency mutation genes, such as MC1R: c.359T>C and PRKDC: c.10681T>A, typically had high-frequency mutation sites. Furthermore, we identified c.8187G>T as a characteristic mutation of BRCA2 in the Chinese population, and the CHEK2 mutation was significantly associated with the early onset of ovarian cancer, while the CDH1 and FAM175A mutations were more prevalent in Northeast China. Additionally, Fanconi anemia pathway-related genes were significantly associated with ovarian carcinogenesis. Conclusion: In summary, this research provided fundamental data support for the optimization of ovarian cancer gene screening policies and the determination of treatment, and contributed to the precise intervention and management of patients. [ABSTRACT FROM AUTHOR]

Chromosomal instability is a hallmark of colorectal carcinogenesis and produces an accumulation of different forms of aneuploidies or broad copy number aberrations. Colorectal cancer is characterized by gain-type broad copy number aberrations, specifically in Chr20, Chr8q, Chr13 and Chr7, but their roles and mechanisms in cancer progression are not fully understood. It has been suggested that broad copy number gains might contribute to tumor development through the so-called caricature transcriptomic effect. We intend to investigate the impact of broad copy number gains on long non-coding RNAs' expression in colorectal cancer, given their well-known role in oncogenesis. The influence of such chromosomal aberrations on lncRNAs' transcriptome profile was investigated by SNP and transcriptome arrays in our series of colorectal cancer samples and cell lines. The correlation between aneuploidies and transcriptomic profiles led us to obtain a class of Over-UpT lncRNAs, which are transcripts upregulated in CRC and further overexpressed in colon tumors bearing specific chromosomal aberrations. The identified lncRNAs can contribute to a wide interaction network to establish the cancer driving effect of gain-type aneuploidies. [ABSTRACT FROM AUTHOR]

The last decade has seen rapid progress in the use of genomic tests, including gene panels, whole‐exome sequencing, and whole‐genome sequencing, in research and clinical cancer care. These advances have created expansive opportunities to characterize the molecular attributes of cancer, revealing a subset of cancer‐associated aberrations called driver mutations. The identification of these driver mutations can unearth vulnerabilities of cancer cells to targeted therapeutics, which has led to the development and approval of novel diagnostics and personalized interventions in various malignancies. The applications of this modern approach, often referred to as precision oncology or precision cancer medicine, are already becoming a staple in cancer care and will expand exponentially over the coming years. Although genomic tests can lead to better outcomes by informing cancer risk, prognosis, and therapeutic selection, they remain underutilized in routine cancer care. A contributing factor is a lack of understanding of their clinical utility and the difficulty of results interpretation by the broad oncology community. Practical guidelines on how to interpret and integrate genomic information in the clinical setting, addressed to clinicians without expertise in cancer genomics, are currently limited. Building upon the genomic foundations of cancer and the concept of precision oncology, the authors have developed practical guidance to aid the interpretation of genomic test results that help inform clinical decision making for patients with cancer. They also discuss the challenges that prevent the wider implementation of precision oncology. [ABSTRACT FROM AUTHOR]

Identification of genes and molecular pathways with congruent profiles in the proteomic and transcriptomic datasets may result in the discovery of promising transcriptomic biomarkers that would be more relevant to phenotypic changes. In this study, we conducted comparative analysis of 943 paired RNA and proteomic profiles obtained for the same samples of seven human cancer types from The Cancer Genome Atlas (TCGA) and NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) [two major open human cancer proteomic and transcriptomic databases] that included 15,112 protein-coding genes and 1611 molecular pathways. Overall, our findings demonstrated statistically significant improvement of the congruence between RNA and proteomic profiles when performing analysis at the level of molecular pathways rather than at the level of individual gene products. Transition to the molecular pathway level of data analysis increased the correlation to 0.19-0.57 (Pearson) and 0.14-057 (Spearman), or 2-3-fold for some cancer types. Evaluating the gain of the correlation upon transition to the data analysis the pathway level can be used to refine the omics data by identifying outliers that can be excluded from the comparison of RNA and proteomic profiles. We suggest using sample- and gene-wise correlations for individual genes and molecular pathways as a measure of quality of RNA/protein paired molecular data. We also provide a database of human genes, molecular pathways, and samples related to the correlation between RNA and protein products to facilitate an exploration of new cancer transcriptomic biomarkers and molecular mechanisms at different levels of human gene expression. [ABSTRACT FROM AUTHOR]

SKP2 (S-phase kinase-associated protein 2) is a member of the F-box family of substrate-recognition subunits in the SCF ubiquitin-protein ligase complexes. It is associated with ubiquitin-mediated degradation in the mammalian cell cycle components and other target proteins involved in cell cycle progression, signal transduction, and transcription. Being an oncogene in solid tumors and hematological malignancies, it is frequently associated with drug resistance and poor disease outcomes. In the current review, we discussed the novel role of SKP2 in different hematological malignancies. Further, we performed a limited in-silico analysis to establish the involvement of SKP2 in a few publicly available cancer datasets. Interestingly, our study identified Skp2 expression to be altered in a cancer-specific manner. While it was found to be overexpressed in several cancer types, few cancer showed a down-regulation in SKP2. Our review provides evidence for developing novel SKP2 inhibitors in hematological malignancies. We also investigated the effect of SKP2 status on survival and disease progression. In addition, the role of miRNA and its associated families in regulating Skp2 expression was explored. Subsequently, we predicted common miRNAs against Skp2 genes by using miRNA-predication tools. Finally, we discussed current approaches and future prospective approaches to target the Skp2 gene by using different drugs and miRNA-based therapeutics applications in translational research. [ABSTRACT FROM AUTHOR]

The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype–phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine. [ABSTRACT FROM AUTHOR]

Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (ie, cohorts), we propose multiple augmented reduced rank regression (maRRR), a flexible matrix regression and factorization method to concurrently learn both covariate-driven and auxiliary structured variations. We consider a structured nuclear norm objective that is motivated by random matrix theory, in which the regression or factorization terms may be shared or specific to any number of cohorts. Our framework subsumes several existing methods, such as reduced rank regression and unsupervised multimatrix factorization approaches, and includes a promising novel approach to regression and factorization of a single dataset (aRRR) as a special case. Simulations demonstrate substantial gains in power from combining multiple datasets, and from parsimoniously accounting for all structured variations. We apply maRRR to gene expression data from multiple cancer types (ie, pan-cancer) from The Cancer Genome Atlas, with somatic mutations as covariates. The method performs well with respect to prediction and imputation of held-out data, and provides new insights into mutation-driven and auxiliary variations that are shared or specific to certain cancer types. [ABSTRACT FROM AUTHOR]

Cancer is a heterogeneous disease that results from genetic alteration of cell cycle and proliferation controls. Identifying mutations that drive cancer, understanding cancer type specificities, and delineating how driver mutations interact with each other to establish disease is vital for identifying therapeutic vulnerabilities. Such cancer specific patterns and gene co-occurrences can be identified by studying tumor genome sequences, and networks have proven effective in uncovering relationships between sequences. We present two network-based approaches to identify driver gene patterns among tumor samples. The first approach relies on analysis using the Directed Weighted All Nearest Neighbors (DiWANN) model, which is a variant of sequence similarity network, and the second approach uses bipartite network analysis. A data reduction framework was implemented to extract the minimal relevant information for the sequence similarity network analysis, where a transformed reference sequence is generated for constructing the driver gene network. This data reduction process combined with the efficiency of the DiWANN network model, greatly lowered the computational cost (in terms of execution time and memory usage) of generating the networks enabling us to work at a much larger scale than previously possible. The DiWANN network helped us identify cancer types in which samples were more closely connected to each other suggesting they are less heterogeneous and potentially susceptible to a common drug. The bipartite network analysis provided insight into gene associations and co-occurrences. We identified genes that were broadly mutated in multiple cancer types and mutations exclusive to only a few. Additionally, weighted one-mode gene projections of the bipartite networks revealed a pattern of occurrence of driver genes in different cancers. Our study demonstrates that network-based approaches can be an effective tool in cancer genomics. The analysis identifies co-occurring and exclusive driver genes and mutations for specific cancer types, providing a better understanding of the driver genes that lead to tumor initiation and evolution.

As the projected incidence and mortality of cancer in Sub-Saharan Africa (SSA) rises to epidemic proportions, it is imperative that more is done to identify the genomic differences and commonalities between patients of African and European ancestry to fulfil the promise of precision oncology. Here, we summarize the utility of precision oncology approaches, with a focus on comprehensive genomic profiling (CGP) and consolidate examples of national and international consortia that are driving the field forward. We describe the importance of genomic diversity and its relevance in cancer, and propose recommendations, success factors and desired outcomes for precision oncology consortia to adopt in SSA. Through this, we hope to catalyze the initiation of such projects and to contribute to improving cancer patient outcomes in the region.

Abstract Background Genomic rearrangements in cancer cells can create fusion genes that encode chimeric proteins or alter the expression of coding and non-coding RNAs. In some cancer types, fusions involving specific kinases are used as targets for therapy. Fusion genes can be detected by whole genome sequencing (WGS) and targeted fusion panels, but RNA sequencing (RNA-Seq) has the advantageous capability of broadly detecting expressed fusion transcripts. Results We developed a pipeline for validation of fusion transcripts identified in RNA-Seq data using matched WGS data from The Cancer Genome Atlas (TCGA) and applied it to 910 tumors from 11 different cancer types. This resulted in 4237 validated gene fusions, 3049 of them with at least one identified genomic breakpoint. Utilizing validated fusions as true positive events, we trained a machine learning classifier to predict true and false positive fusion transcripts from RNA-Seq data. The final precision and recall metrics of the classifier were 0.74 and 0.71, respectively, in an independent dataset of 249 breast tumors. Application of this classifier to all samples with RNA-Seq data from these cancer types vastly extended the number of likely true positive fusion transcripts and identified many potentially targetable kinase fusions. Further analysis of the validated gene fusions suggested that many are created by intrachromosomal amplification events with microhomology-mediated non-homologous end-joining. Conclusions A classifier trained on validated fusion events increased the accuracy of fusion transcript identification in samples without WGS data. This allowed the analysis to be extended to all samples with RNA-Seq data, facilitating studies of tumor biology and increasing the number of detected kinase fusions. Machine learning could thus be used in identification of clinically relevant fusion events for targeted therapy. The large dataset of validated gene fusions generated here presents a useful resource for development and evaluation of fusion transcript detection algorithms.

Abstract Background Despite the acceleration of somatic driver gene discovery facilitated by recent large-scale tumor sequencing data, the contribution of inherited variants remains largely unexplored, primarily focusing on previously known cancer predisposition genes (CPGs) due to the low statistical power associated with detecting rare pathogenic variant-phenotype associations. Methods Here, we introduce a generalized log-regression model to measure the excess of pathogenic variants within genes in cancer patients compared to control samples. It aims to measure gene-level cancer risk enrichment by collapsing rare pathogenic variants after controlling the population differences across samples. Results In this study, we investigate whether pathogenic variants in Mendelian disease-associated genes (OMIM genes) are enriched in cancer patients compared to controls. Utilizing data from PCAWG and the 1,000 Genomes Project, we identify 103 OMIM genes demonstrating significant enrichment of pathogenic variants in cancer samples (FDR 20%). Through an integrative approach considering three distinct properties, we classify these CPG-like OMIM genes into four clusters, indicating potential diverse mechanisms underlying tumor progression. Further, we explore the function of PAH (a key metabolic enzyme associated with Phenylketonuria), the gene exhibiting the highest prevalence of pathogenic variants in a pan-cancer (1.8%) compared to controls (0.6%). Conclusions Our findings suggest a possible cancer progression mechanism through metabolic profile alterations. Overall, our data indicates that pathogenic OMIM gene variants contribute to cancer progression and introduces new CPG classifications potentially underpinning diverse tumorigenesis mechanisms.

The exon junction complex (EJC) plays key roles throughout the lifespan of RNA and is particularly relevant in the nervous system. We investigated the roles of two EJC members, the paralogs MAGOH and MAGOHB, with respect to brain tumour development. High MAGOH/MAGOHB expression was observed in 14 tumour types; glioblastoma (GBM) showed the greatest difference compared to normal tissue. Increased MAGOH/MAGOHB expression was associated with poor prognosis in glioma patients, while knockdown of MAGOH/MAGOHB affected different cancer phenotypes. Reduced MAGOH/MAGOHB expression in GBM cells caused alterations in the splicing profile, including re-splicing and skipping of multiple exons. The binding profiles of EJC proteins indicated that exons affected by MAGOH/MAGOHB knockdown accumulated fewer complexes on average, providing a possible explanation for their sensitivity to MAGOH/MAGOHB knockdown. Transcripts (genes) showing alterations in the splicing profile are mainly implicated in cell division, cell cycle, splicing, and translation. We propose that high MAGOH/MAGOHB levels are required to safeguard the splicing of genes in high demand in scenarios requiring increased cell proliferation (brain development and GBM growth), ensuring efficient cell division, cell cycle regulation, and gene expression (splicing and translation). Since differentiated neuronal cells do not require increased MAGOH/MAGOHB expression, targeting these paralogs is a potential option for treating GBM.

Despite recent advances, biliary tract cancer (BTC) remains one of the most lethal tumor worldwide due to late diagnosis, limited therapeutic strategies and resistance to conventional therapies. In recent years, high-throughput technologies have enabled extensive genome, and transcriptome sequencing unveiling, among others, the regulatory potential of microRNAs (miRNAs). Compelling evidence shown that miRNA are attractive therapeutic targets and promising candidates as biomarkers for various therapy-resistant tumors. The analysis of miRNA profile successfully identified miR-181c and -181d as significantly downregulated in BTC patients. Low miR-181c and -181d expression levels were correlated with worse prognosis and poor treatment efficacy. In fact, progression-free survival analysis indicated poor survival rates in miR-181c and -181d low expressing patients. The expression profile of miR-181c and -181d in BTC cell lines revealed that both miRNAs were dysregulated. Functional in vitro experiments in BTC cell lines showed that overexpression of miR-181c and -181d affected cell viability and increased sensitivity to chemotherapy compared to controls. In addition, by using bioinformatic tools we showed that the miR-181c/d functional role is determined by binding to their target SIRT1 (Sirtuin 1). Moreover, BTC patients expressing high levels of miR-181 and low SIRT1 shown an improved survival and treatment response. An integrative network analysis demonstrated that, miR-181/SIRT1 circuit had a regulatory effect on several important metabolic tumor-related processes. Our study demonstrated that miR-181c and -181d act as tumor suppressor miRNA in BTC, suggesting the potential use as therapeutic strategy in resistant cancers and as predictive biomarker in the precision medicine of BTC. [ABSTRACT FROM AUTHOR]

The management of anal squamous cell carcinoma (ASCC) has yet to experience the transformative impact of precision medicine. Conducting genomic analyses may uncover novel prognostic biomarkers and offer potential directions for the development of targeted therapies. To that end, we assessed the prognostic and theragnostic implications of pathogenic variants identified in 571 cancer‐related genes from surgical samples collected from a homogeneous, multicentric French cohort of 158 ASCC patients who underwent abdominoperineal resection treatment. Alterations in PI3K/AKT/mTOR, chromatin remodeling, and Notch pathways were frequent in HPV‐positive tumors, while HPV‐negative tumors often harbored variants in cell cycle regulation and genome integrity maintenance genes (e.g., frequent TP53 and TERT promoter mutations). In patients with HPV‐positive tumors, KMT2C and PIK3CA exon 9/20 pathogenic variants were associated with worse overall survival in multivariate analysis (Hazard ratio (HR)KMT2C = 2.54, 95%CI = [1.25,5.17], P value =.010; HRPIK3CA = 2.43, 95%CI = [1.3,4.56], P value =.006). Alterations with theragnostic value in another cancer type was detected in 43% of patients. These results suggest that PIK3CA and KMT2C pathogenic variants are independent prognostic factors in patients with ASCC with HPV‐positive tumors treated by abdominoperineal resection. And, importantly, the high prevalence of alterations bearing potential theragnostic value strongly supports the use of genomic profiling to allow patient enrollment in precision medicine clinical trials. [ABSTRACT FROM AUTHOR]

Background: Despite the acceleration of somatic driver gene discovery facilitated by recent large-scale tumor sequencing data, the contribution of inherited variants remains largely unexplored, primarily focusing on previously known cancer predisposition genes (CPGs) due to the low statistical power associated with detecting rare pathogenic variant-phenotype associations. Methods: Here, we introduce a generalized log-regression model to measure the excess of pathogenic variants within genes in cancer patients compared to control samples. It aims to measure gene-level cancer risk enrichment by collapsing rare pathogenic variants after controlling the population differences across samples. Results: In this study, we investigate whether pathogenic variants in Mendelian disease-associated genes (OMIM genes) are enriched in cancer patients compared to controls. Utilizing data from PCAWG and the 1,000 Genomes Project, we identify 103 OMIM genes demonstrating significant enrichment of pathogenic variants in cancer samples (FDR 20%). Through an integrative approach considering three distinct properties, we classify these CPG-like OMIM genes into four clusters, indicating potential diverse mechanisms underlying tumor progression. Further, we explore the function of PAH (a key metabolic enzyme associated with Phenylketonuria), the gene exhibiting the highest prevalence of pathogenic variants in a pan-cancer (1.8%) compared to controls (0.6%). Conclusions: Our findings suggest a possible cancer progression mechanism through metabolic profile alterations. Overall, our data indicates that pathogenic OMIM gene variants contribute to cancer progression and introduces new CPG classifications potentially underpinning diverse tumorigenesis mechanisms. [ABSTRACT FROM AUTHOR]

Simple Summary: We find that mutations in the TP53, FLT4, EYA1, NCOR2, CSMD3, and PCDH15 genes are associated with prostate cancer metastasis and mutations in EYA1 and CSMD3 are associated with poor outcome. Our finding that oxidative phosphorylation is associated with metastasis and identification of mutations enriched in prostate cancer metastases have implications for our understanding of the molecular events required for prostate metastasis. They also have relevance for our understanding of prostate cancer health disparities and provide a rationale for identification of compounds that target oxidative phosphorylation in metastatic prostate cancer. Prostate cancer is a highly heterogeneous disease and mortality is mainly due to metastases but the initial steps of metastasis have not been well characterized. We have performed integrative whole exome sequencing and transcriptome analysis of primary prostate tumor foci and corresponding lymph node metastases (LNM) from 43 patients enrolled in clinical trial. We present evidence that, while there are some cases of clonally independent primary tumor foci, 87% of primary tumor foci and metastases are descended from a common ancestor. We demonstrate that genes related to oxidative phosphorylation are upregulated in LNM and in African-American patients relative to White patients. We further show that mutations in TP53, FLT4, EYA1, NCOR2, CSMD3, and PCDH15 are enriched in prostate cancer metastases. These findings were validated in a meta-analysis of 3929 primary tumors and 2721 metastases and reveal a pattern of molecular alterations underlying the pathology of metastatic prostate cancer. We show that LNM contain multiple subclones that are already present in primary tumor foci. We observed enrichment of mutations in several genes including understudied genes such as EYA1, CSMD3, FLT4, NCOR2, and PCDH15 and found that mutations in EYA1 and CSMD3 are associated with a poor outcome in prostate cancer. [ABSTRACT FROM AUTHOR]

Tasmanian devils are endangered by a transmissible cancer known as Tasmanian devil facial tumour 1 (DFT1). A 2020 study by Patton et al. (Science 370, eabb9772 (doi:10.1126/science.abb9772)) used genome data from DFT1 tumours to produce a dated phylogenetic tree for this transmissible cancer lineage, and thence, using phylodynamics models, to estimate its epidemiological parameters and predict its future trajectory. It concluded that the effective reproduction number for DFT1 had declined to a value of one, and that the disease had shifted from emergence to endemism. We show that the study is based on erroneous mutation calls and flawed methodology, and that its conclusions cannot be substantiated.

Abstract Background Genomic insights in settings where tumour sample sizes are limited to just hundreds or even tens of cells hold great clinical potential, but also present significant technical challenges. We previously developed the DigiPico sequencing platform to accurately identify somatic mutations from such samples. Results Here, we complete this genomic characterisation with copy number. We present a novel protocol, PicoCNV, to call allele-specific somatic copy number alterations from picogram quantities of tumour DNA. We find that PicoCNV provides exactly accurate copy number in 84% of the genome for even the smallest samples, and demonstrate its clinical potential in maintenance therapy. Conclusions PicoCNV complements our existing platform, allowing for accurate and comprehensive genomic characterisations of cancers in settings where only microscopic samples are available.

Background: Gene fusions occur when two independent genes form a hybrid gene through genomic rearrangements, which often leads to abnormal expression and function of an encoded protein. In hematological and solid cancers, oncogenic fusions may be prognostic, diagnostic, or therapeutic biomarkers. Improved detection and understanding of the functional implications of such fusions may be beneficial for patient care. Methods: We performed a retrospective analysis of our internal genomic database to identify known and novel gene fusions in different solid tumors seen in our community cancer center. We then investigated the clinical implications of the fusions we identified. Results: We identified 420 known oncogenic fusions and 25 unclassified gene fusions across twenty-six different cancer types. Of 420 fusion-positive tumors with known fusions, there were 366 unique gene fusions. Conclusions: About 10% of tumors investigated had oncogenic fusions, which supports the notion that comprehensive molecular profiling, including RNA sequencing, should be provided for patients with advanced cancers.

Abstract Background Hundreds of functional genomic screens have been performed across a diverse set of cancer contexts, as part of efforts such as the Cancer Dependency Map, to identify gene dependencies—genes whose loss of function reduces cell viability or fitness. Recently, large-scale screening efforts have shifted from RNAi to CRISPR-Cas9, due to superior efficacy and specificity. However, many effective oncology drugs only partially inhibit their protein targets, leading us to question whether partial suppression of genes using RNAi could reveal cancer vulnerabilities that are missed by complete knockout using CRISPR-Cas9. Here, we compare CRISPR-Cas9 and RNAi dependency profiles of genes across approximately 400 matched cancer cell lines. Results We find that CRISPR screens accurately identify more gene dependencies per cell line, but the majority of each cell line’s dependencies are part of a set of 1867 genes that are shared dependencies across the entire collection (pan-lethals). While RNAi knockdown of about 30% of these genes is also pan-lethal, approximately 50% have selective dependency patterns across cell lines, suggesting they could still be cancer vulnerabilities. The accuracy of the unique RNAi selectivity is supported by associations to multi-omics profiles, drug sensitivity, and other expected co-dependencies. Conclusions Incorporating RNAi data for genes that are pan-lethal knockouts facilitates the discovery of a wider range of gene targets than could be detected using the CRISPR dataset alone. This can aid in the interpretation of contrasting results obtained from CRISPR and RNAi screens and reinforce the importance of partial gene suppression methods in building a cancer dependency map.

Abstract Background The implementation of the National Genomic Medicine Service in the UK has increased patient access to germline genomic testing. Increased testing leads to more genetic diagnoses but does result in the identification of genomic variants of uncertain significance (VUS). The rigorous process of interpreting these variants requires multi-disciplinary, highly trained healthcare professionals (HCPs). To meet this training need, we designed two Massive Open Online Courses (MOOCs) for HCPs involved in germline genomic testing pathways: Fundamental Principles (FP) and Inherited Cancer Susceptibility (ICS). Methods An evaluation cohort of HCPs involved in genomic testing were recruited, with additional data also available from anonymous self-registered learners to both MOOCs. Pre- and post-course surveys and in-course quizzes were used to assess learner satisfaction, confidence and knowledge gained in variant interpretation. In addition, granular feedback was collected on the complexity of the MOOCs to iteratively improve the resources. Results A cohort of 92 genomics HCPs, including clinical scientists, and non-genomics clinicians (clinicians working in specialties outside of genomics) participated in the evaluation cohort. Between baseline and follow-up, total confidence scores improved by 38% (15.2/40.0) (95% confidence interval [CI] 12.4–18.0) for the FP MOOC and 54% (18.9/34.9) (95%CI 15.5–22.5) for the ICS MOOC (p

Abstract Background Cancer genome sequencing enables accurate classification of tumours and tumour subtypes. However, prediction performance is still limited using exome-only sequencing and for tumour types with low somatic mutation burden such as many paediatric tumours. Moreover, the ability to leverage deep representation learning in discovery of tumour entities remains unknown. Methods We introduce here Mutation-Attention (MuAt), a deep neural network to learn representations of simple and complex somatic alterations for prediction of tumour types and subtypes. In contrast to many previous methods, MuAt utilizes the attention mechanism on individual mutations instead of aggregated mutation counts. Results We trained MuAt models on 2587 whole cancer genomes (24 tumour types) from the Pan-Cancer Analysis of Whole Genomes (PCAWG) and 7352 cancer exomes (20 types) from the Cancer Genome Atlas (TCGA). MuAt achieved prediction accuracy of 89% for whole genomes and 64% for whole exomes, and a top-5 accuracy of 97% and 90%, respectively. MuAt models were found to be well-calibrated and perform well in three independent whole cancer genome cohorts with 10,361 tumours in total. We show MuAt to be able to learn clinically and biologically relevant tumour entities including acral melanoma, SHH-activated medulloblastoma, SPOP-associated prostate cancer, microsatellite instability, POLE proofreading deficiency, and MUTYH-associated pancreatic endocrine tumours without these tumour subtypes and subgroups being provided as training labels. Finally, scrunity of MuAt attention matrices revealed both ubiquitous and tumour-type specific patterns of simple and complex somatic mutations. Conclusions Integrated representations of somatic alterations learnt by MuAt were able to accurately identify histological tumour types and identify tumour entities, with potential to impact precision cancer medicine.

In this mini-review, we introduce and summarize research from the Quantum Technologies Group (QTG) at Carnegie Mellon University related to computational experience with quantum annealing, performed in collaboration with several other institutions including IIT-Madras and NASA (QuAIL). We present a novel hybrid quantum-classical heuristic algorithm (GAMA, Graver Augmented Multi-seed Algorithm) for non-linear, integer optimization, and illustrate it on an application (in cancer genomics). We then present an algebraic geometry-based algorithm for embedding a problem onto a hardware that is not fully connected, along with a companion Integer Programming (IP) approach. Next, we discuss the performance of two photonic devices - the Temporal Multiplexed Ising Machine (TMIM) and the Spatial Photonic Ising Machine (SPIM) - on Max-Cut and Number Partitioning instances. We close with an outline of the current work.

Background: Genomic insights in settings where tumour sample sizes are limited to just hundreds or even tens of cells hold great clinical potential, but also present significant technical challenges. We previously developed the DigiPico sequencing platform to accurately identify somatic mutations from such samples. Results: Here, we complete this genomic characterisation with copy number. We present a novel protocol, PicoCNV, to call allele-specific somatic copy number alterations from picogram quantities of tumour DNA. We find that PicoCNV provides exactly accurate copy number in 84% of the genome for even the smallest samples, and demonstrate its clinical potential in maintenance therapy. Conclusions: PicoCNV complements our existing platform, allowing for accurate and comprehensive genomic characterisations of cancers in settings where only microscopic samples are available. [ABSTRACT FROM AUTHOR]

As the progression of low-grade diffuse astrocytomas into grade 4 tumors significantly impacts patient prognosis, a better understanding of this process is of paramount importance for improved patient care. In this project, we analyzed matched IDH-mutant astrocytomas before and after progression to grade 4 from six patients (discovery cohort) with genome-wide sequencing, 21 additional patients with targeted sequencing, and 33 patients from Glioma Longitudinal AnalySiS cohort for validation. The Cancer Genome Atlas data from 595 diffuse gliomas provided supportive information. All patients in our discovery cohort received radiation, all but one underwent chemotherapy, and no patient received temozolomide (TMZ) before progression to grade 4 disease. One case in the discovery cohort exhibited a hypermutation signature associated with the inactivation of the MSH2 and DNMT3A genes. In other patients, the number of chromosomal rearrangements and deletions increased in grade 4 tumors. The cell cycle checkpoint gene CDKN2A, or less frequently RB1, was most commonly inactivated after receiving both chemo- and radiotherapy when compared to other treatment groups. Concomitant activating PDGFRA/MET alterations were detected in tumors that acquired a homozygous CDKN2A deletion. NRG3 gene was significantly downregulated and recurrently altered in progressed tumors. Its decreased expression was associated with poorer overall survival in both univariate and multivariate analysis. We also detected progression-related alterations in RAD51B and other DNA repair pathway genes associated with the promotion of error-prone DNA repair, potentially facilitating tumor progression. In our retrospective analysis of patient treatment and survival timelines (n = 75), the combination of postoperative radiation and chemotherapy (mainly TMZ) outperformed radiation, especially in the grade 3 tumor cohort, in which it was typically given after primary surgery. Our results provide further insight into the contribution of treatment and genetic alterations in cell cycle, growth factor signaling, and DNA repair-related genes to tumor evolution and progression. [ABSTRACT FROM AUTHOR]

Simple Summary: We introduce GUST-mouse (Genes Under Selection in Tumors for mouse), a novel computational method designed to identify cancer driver genes within mouse tumor exomes. As the first method of its kind, GUST-mouse transcends conventional frequency-based rules. It incorporates molecular evolutionary theories and leverages transfer learning techniques to effectively differentiate between oncogenes, tumor suppressor genes, and passenger genes. When applied to mouse models of breast cancer, leukemia, and lung cancer, GUST-mouse unveiled that the emergence of somatic driver mutations is profoundly influenced by the genetically engineered background of the mouse models. A comparative analysis with human cancer drivers illuminated both shared and distinct patterns, casting new light on the intricate process of tumorigenesis. The pioneering framework of the GUST-mouse method opens a new avenue for identifying driver genes in non-human cancers. The mouse is a widely used model organism in cancer research. However, no computational methods exist to identify cancer driver genes in mice due to a lack of labeled training data. To address this knowledge gap, we adapted the GUST (Genes Under Selection in Tumors) model, originally trained on human exomes, to mouse exomes via transfer learning. The resulting tool, called GUST-mouse, can estimate long-term and short-term evolutionary selection in mouse tumors, and distinguish between oncogenes, tumor suppressor genes, and passenger genes using high-throughput sequencing data. We applied GUST-mouse to analyze 65 exomes of mouse primary breast cancer models and 17 exomes of mouse leukemia models. Comparing the predictions between cancer types and between human and mouse tumors revealed common and unique driver genes. The GUST-mouse method is available as an open-source R package on github. [ABSTRACT FROM AUTHOR]