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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]

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]

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 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]

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]

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]

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 FROM AUTHOR]

Simple Summary: Cancer is a complex disease that develops over time through accumulated mutations in DNA that transform normal cells into a cancerous state. To fully capture the complexity of the cancer genome, computational methods have been developed to summarize the mutational patterns of cancer, distinguish causal oncogenic mutations, and determine clinically useful mutational patterns. In this review, we survey different computational approaches with an emphasis on important clinical roles and provide insights into better integration of computational methods for clinical use. Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning. [ABSTRACT FROM AUTHOR]

During oncogenesis, alterations in driver genes called driver alterations (DAs) modulate the transcriptome, methylome and proteome through oncogenic signaling pathways. These modulatory effects of any DA may be analyzed by examining differentially expressed mRNAs (DEMs), differentially methylated genes (DMGs) and differentially expressed proteins (DEPs) between tumor samples with and without that DA. We aimed to analyze these modulations with 12 common driver genes in Isocitrate Dehydrogenase 1 wildtype glioblastomas (IDH1-W-GBs). Using Cbioportal, groups of tumor samples with and without DAs in these 12 genes were generated from the IDH1-W-GBs available from "The Cancer Genomics Atlas Firehose Legacy Study Group" (TCGA-FL-SG) on Glioblastomas (GBs). For all 12 genes, samples with and without DAs were compared for DEMs, DMGs and DEPs. We found that DAs in PTEN were unassociated with any DEM or DMG in contrast to DAs in all other drivers, which were associated with several DEMs and DMGs. This contrasting PTEN-related property of being unassociated with differential gene expression or methylation in IDH1-W-GBs was unaffected by concurrent DAs in other common drivers or by the types of DAs affecting PTEN. From the lists of DEMs and DMGs associated with some common drivers other than PTEN, enriched gene ontology terms and insights into the co-regulatory effects of these drivers on the transcriptome were obtained. The findings from this study can improve our understanding of the molecular mechanisms underlying gliomagenesis with potential therapeutic benefits. [ABSTRACT FROM AUTHOR]

Bladder cancer (BC) is the most common neoplasm of the urinary tract, which originates in the epithelium that covers the inner surface of the bladder. The molecular BC profile has led to the development of different classifications of non-muscle invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). However, the genomic BC landscape profile of the Mexican population, including NMIBC and MIBC, is unknown. In this study, we aimed to identify somatic single nucleotide variants (SNVs) and copy number variations (CNVs) in Mexican patients with BC and their associations with clinical and pathological characteristics. We retrospectively evaluated 37 patients treated between 2012 and 2021 at the National Cancer Institute—Mexico (INCan). DNA samples were obtained from paraffin-embedded tumor tissues and exome sequenced. Strelka2 and Lancet packages were used to identify SNVs and insertions or deletions. FACETS was used to determine CNVs. We found a high frequency of mutations in TP53 and KMT2D, gains in 11q15.5 and 19p13.11-q12, and losses in 7q11.23. STAG2 mutations and 1q11.23 deletions were also associated with NMIBC and low histologic grade. [ABSTRACT FROM AUTHOR]

Deciphering cancer etiopathogenesis has proven to be an especially challenging task since the mechanisms that drive tumor development and progression are far from simple. An astonishing amount of research has revealed a wide spectrum of defects, including genomic abnormalities, epigenomic alterations, disturbance of gene transcription, as well as post-translational protein modifications, which cooperatively promote carcinogenesis. These findings suggest that the adoption of a multidimensional approach can provide a much more precise and comprehensive picture of the tumor landscape, hence serving as a powerful tool in cancer research and precision oncology. The introduction of next- and third-generation sequencing technologies paved the way for the decoding of genetic information and the elucidation of cancer-related cellular compounds and mechanisms. In the present review, we discuss the current and emerging applications of both generations of sequencing technologies, also referred to as massive parallel sequencing (MPS), in the fields of cancer genomics, transcriptomics and proteomics, as well as in the progressing realms of epi-omics. Finally, we provide a brief insight into the expanding scope of sequencing applications in personalized cancer medicine and pharmacogenomics. [ABSTRACT FROM AUTHOR]

Simple Summary: Early stage primary cancer, where the tumour has not yet spread from its site of origin, is commonly curable through surgery alone. Conversely, metastatic cancer, where the tumour has disseminated to distant sites, is almost invariably lethal and is the main cause of cancer death. Given that primary cancer is often curable, it is likely that the ability to metastasise is acquired late in the development of cancer. To investigate if specific cancer gene mutations or pathways are subject to selection at different time points during the life-history of cancer, we compared genomic data from primary and metastatic cancer and timed the acquisition of genomic alterations. The results presented here support the view that certain events are selected for late in the evolution of cancer. However, we observed no difference in the type or timing of events between primary and metastatic cancer. Taken together, our results suggest that the ability to metastasise is not acquired through evolution solely at a genomic level. Cancer metastasis is the lethal developmental step in cancer, responsible for the majority of cancer deaths. To metastasise, cancer cells must acquire the ability to disseminate systemically and to escape an activated immune response. Here, we endeavoured to investigate if metastatic dissemination reflects acquisition of genomic traits that are selected for. We acquired mutation and copy number data from 8332 tumours representing 19 cancer types acquired from The Cancer Genome Atlas and the Hartwig Medical Foundation. A total of 827,344 non-synonymous mutations across 8332 tumour samples representing 19 cancer types were timed as early or late relative to copy number alterations, and potential driver events were annotated. We found that metastatic cancers had a significantly higher proportion of clonal mutations and a general enrichment of early mutations in p53 and RTK/KRAS pathways. However, while individual pathways demonstrated a clear time-separated preference for specific events, the relative timing did not vary between primary and metastatic cancers. These results indicate that the selective pressure that drives cancer development does not change dramatically between primary and metastatic cancer on a genomic level, and is mainly focused on alterations that increase proliferation. [ABSTRACT FROM AUTHOR]

There is a growing number of multi-domain genomic datasets for human tumors. Multi-domain data are usually interpreted after separately analyzing single-domain data and integrating the results post hoc. Data fusion techniques allow for the real integration of multi-domain data to ideally improve the tumor classification results for the prognosis and prediction of response to therapy. We have previously described the joint singular value decomposition (jSVD) technique as a means of data fusion. Here, we report on the development of these methods in open source code based on R and Python and on the application of these data fusion methods. The Cancer Genome Atlas (TCGA) Skin Cutaneous Melanoma (SKCM) dataset was used as a benchmark to evaluate the potential of the data fusion approaches to improve molecular classification of cancers in a clinically relevant manner. Our data show that the data fusion approach does not generate classification results superior to those obtained using single-domain data. Data from different domains are not entirely independent from each other, and molecular classes are characterized by features that penetrate different domains. Data fusion techniques might be better suited for response prediction, where they could contribute to the identification of predictive features in a domain-independent manner to be used as biomarkers. [ABSTRACT FROM AUTHOR]

Simple Summary: Chromosomal alterations and other structural variants have been recurrently identified in Wilms tumors (WT) and are promising biomarkers for risk stratification. Chromosome 1q gain occurs in one in three WTs and is associated with poor prognosis, but its impact on tumor biology remains unknown. Here, we investigated the mutational mechanisms and functional effects of chromosomal alterations in WTs, and in particular 1q gain. We identified subgroups of tumors with typical activated biological processes: muscle differentiation, immune system, kidney development and proliferation. Combining these subgroups with genomic data showed that tumors with 1q gain occur in all subgroups and can be associated with different functional effects. Also, 1q gain tumors differ in mutational mechanisms and co-occurring tumor-specific mutations. In conclusion, we identified subgroups of tumors with 1q gain and therefore propose that incorporating expression data in risk stratification could improve the clinical utility of 1q gain. Chromosomal alterations have recurrently been identified in Wilms tumors (WTs) and some are associated with poor prognosis. Gain of 1q (1q+) is of special interest given its high prevalence and is currently actively studied for its prognostic value. However, the underlying mutational mechanisms and functional effects remain unknown. In a national unbiased cohort of 30 primary WTs, we integrated somatic SNVs, CNs and SVs with expression data and distinguished four clusters characterized by affected biological processes: muscle differentiation, immune system, kidney development and proliferation. Combined genome-wide CN and SV profiles showed that tumors profoundly differ in both their types of 1q+ and genomic stability and can be grouped into WTs with co-occurring 1p−/1q+, multiple chromosomal gains or CN neutral tumors. We identified 1q+ in eight tumors that differ in mutational mechanisms, subsequent rearrangements and genomic contexts. Moreover, 1q+ tumors were present in all four expression clusters reflecting activation of various biological processes, and individual tumors overexpress different genes on 1q. In conclusion, by integrating CNs, SVs and gene expression, we identified subgroups of 1q+ tumors reflecting differences in the functional effect of 1q gain, indicating that expression data is likely needed for further risk stratification of 1q+ WTs. [ABSTRACT FROM AUTHOR]

The Hispanic population, compared with other ethnic groups, presents a more aggressive gastric cancer phenotype with higher frequency of diffuse-type gastric adenocarcinoma (GA); this could be related to the mutational landscape of GA in these patients. Using whole-exome sequencing, we sought to present the mutational landscape of GA from 50 Mexican patients who were treated at The Instituto Nacional de Cancerología from 2019 to 2020. We performed a comprehensive statistical analysis to explore the relationship of the genomic variants and clinical data such as tumor histology and presence of signet-ring cell, H. pylori, and EBV. We describe a potentially different mutational landscape between diffuse and intestinal GA in Mexican patients. Patients with intestinal-type GA tended to present a higher frequency of NOTCH1 mutations, copy number gains in cytobands 13.14, 10q23.33, and 12q25.1, and copy number losses in cytobands 7p12, 14q24.2, and 11q13.1; whereas patients with diffuse-type GA tended to present a high frequency of CDH1 mutations and CNV gains in cytobands 20q13.33 and 22q11.21. This is the first description of a mutational landscape of GA in Mexican patients to better understand tumorigenesis in Hispanic patients and lay the groundwork for discovering potential biomarkers and therapeutic targets. [ABSTRACT FROM AUTHOR]

To provide precision medicine for better cancer care, researchers must work on clinical patient data, such as electronic medical records, physiological measurements, biochemistry, computerized tomography scans, digital pathology, and the genetic landscape of cancer tissue. To interpret big biodata in cancer genomics, an operational flow based on artificial intelligence (AI) models and medical management platforms with high-performance computing must be set up for precision cancer genomics in clinical practice. To work in the fast-evolving fields of patient care, clinical diagnostics, and therapeutic services, clinicians must understand the fundamentals of the AI tool approach. Therefore, the present article covers the following four themes: (i) computational prediction of pathogenic variants of cancer susceptibility genes; (ii) AI model for mutational analysis; (iii) single-cell genomics and computational biology; (iv) text mining for identifying gene targets in cancer; and (v) the NVIDIA graphics processing units, DRAGEN field programmable gate arrays systems and AI medical cloud platforms in clinical next-generation sequencing laboratories. Based on AI medical platforms and visualization, large amounts of clinical biodata can be rapidly copied and understood using an AI pipeline. The use of innovative AI technologies can deliver more accurate and rapid cancer therapy targets. [ABSTRACT FROM AUTHOR]

Simple Summary: Immune checkpoint inhibitors (ICIs) markedly improve the survival benefits of advanced melanoma and non-small cell lung cancer (NSCLC). Nevertheless, only a subset of patients could benefit from such a therapy. Novel and effective clinical biomarkers are needed to assess ICI treatment efficacy. Heparan sulfate proteoglycan 2 (HSPG2) is frequently mutated in melanoma and NSCLC. In this study, we comprehensively integrated the pretreatment somatic mutational profiles and clinical information of both tumors and observed that HSPG2 mutations were associated with favorable tumor immunogenicity and immunotherapeutic efficacy. Our study provides a potential clinical molecular biomarker for evaluating ICI therapy responses. Immune checkpoint inhibitors (ICIs) markedly promote the survival outcome of advanced melanoma and non-small cell lung cancer (NSCLC). Clinically, favorable ICI treatment efficacy is noticed only in a smaller proportion of patients. Heparan sulfate proteoglycan 2 (HSPG2) frequently mutates in both tumors. Herein, we aim to investigate the immunotherapeutic and immunological roles of HSPG2 mutations in melanoma and NSCLC. A total of 631 melanoma samples and 109 NSCLC samples with both somatic mutational profiles and clinical immunotherapy data were curated. In addition, by using The Cancer Genome Atlas data, genomic and immunological traits behind HSPG2 mutations were elucidated. Melanoma patients with HSPG2 mutations had a markedly extended ICI outcome than other patients. An association between HSPG2 mutations and the improved outcome was further confirmed in NSCLC. In addition, an elevated ICI response rate was presented in HSPG2-mutated NSCLC patients (81.8% vs. 29.7%, p = 0.002). Subsequent analyses revealed that HSPG2-mutated patients had a favorable abundance of response immunocytes, an inferior abundance of suppression immunocytes, enhanced mutational burden, and interferon response-relevant signaling pathways. We uncovered that HSPG2 mutations were predictive of a better ICI response and associated with preferable immunogenicity, which may be considered as a genomic determinant to customize biotherapy strategies. [ABSTRACT FROM AUTHOR]

Autosomal dominant lateral temporal epilepsy (ADLTE) is a genetic focal epilepsy associated with mutations in the LGI1, RELN, and MICAL1 genes. A previous study linking ADLTE with two MICAL1 mutations that resulted in the substitution of a highly conserved glycine residue for serine (G150S) or a frameshift mutation that swapped the last three C-terminal amino acids for 59 extra residues (A1065fs) concluded that the mutations increased enzymatic activity and promoted cell contraction. The roles of the Molecule Interacting with CasL 1 (MICAL1) protein in tightly regulated semaphorin signaling pathways suggest that activating MICAL1 mutations could result in defects in axonal guidance during neuronal development. Further studies would help to illuminate the causal relationships of these point mutations with ADLTE. In this review, we discuss the proposed pathogenesis caused by mutations in these three genes, with a particular emphasis on the G150S point mutation discovered in MICAL1. We also consider whether these types of activating MICAL1 mutations could be linked to cancer. [ABSTRACT FROM AUTHOR]

The disruptive advances in genomics contributed to achieve higher levels of precision in the diagnosis and treatment of cancer. This scientific advance entails the need for greater literacy for all healthcare professionals. Our study summarizes the training initiatives conducted worldwide in cancer genomics field for healthcare professionals. We conducted a web search of the training initiatives aimed at improving healthcare professionals' literacy in cancer genomics undertaken worldwide by using two search engines (Google and Bing) in English language and conducted from 2003 to 2021. A total of 85,649 initiatives were identified. After the screening process, 36 items were included. The majority of training programs were organized in the United States (47%) and in the United Kingdom (28%). Most of the initiatives were conducted in the last five years (83%) by universities (30%) and as web-based modalities (80%). In front of the technological advances in genomics, education in cancer genomics remains fundamental. Our results may contribute to provide an update on the development of educational programs to build a skilled and appropriately trained genomics health workforce in the future. [ABSTRACT FROM AUTHOR]

Prostate cancer (CaP) is the most commonly diagnosed non-cutaneous cancer and the second leading cause of male cancer deaths in the United States. Among African American (AA) men, CaP is the most prevalent malignancy, with disproportionately higher incidence and mortality rates. Even after discounting the influence of socioeconomic factors, the effect of molecular and genetic factors on racial disparity of CaP is evident. Earlier studies on the molecular basis for CaP disparity have focused on the influence of heritable mutations and single-nucleotide polymorphisms (SNPs). Most CaP susceptibility alleles identified based on genome-wide association studies (GWAS) were common, low-penetrance variants. Germline CaP-associated mutations that are highly penetrant, such as those found in HOXB13 and BRCA2, are usually rare. More recently, genomic studies enabled by Next-Gen Sequencing (NGS) technologies have focused on the identification of somatic mutations that contribute to CaP tumorigenesis. These studies confirmed the high prevalence of ERG gene fusions and PTEN deletions among Caucasian Americans and identified novel somatic alterations in SPOP and FOXA1 genes in early stages of CaP. Individuals with African ancestry and other minorities are often underrepresented in these large-scale genomic studies, which are performed primarily using tumors from men of European ancestry. The insufficient number of specimens from AA men and other minority populations, together with the heterogeneity in the molecular etiology of CaP across populations, challenge the generalizability of findings from these projects. Efforts to close this gap by sequencing larger numbers of tumor specimens from more diverse populations, although still at an early stage, have discovered distinct genomic alterations. These research findings can have a direct impact on the diagnosis of CaP, the stratification of patients for treatment, and can help to address the disparity in incidence and mortality of CaP. This review examines the progress of understanding in CaP genetics and genomics and highlight the need to increase the representation from minority populations. [ABSTRACT FROM AUTHOR]

The human LINE-1 (or L1) element is a non-LTR retrotransposon that is mobilized through an RNA intermediate by an L1-encoded reverse transcriptase and other L1-encoded proteins. L1 elements remain actively mobile today and continue to mutagenize human genomes. Importantly, when new insertions disrupt gene function, they can cause diseases. Historically, L1s were thought to be active in the germline but silenced in adult somatic tissues. However, recent studies now show that L1 is active in at least some somatic tissues, including epithelial cancers. In this review, we provide an overview of these recent developments, and examine evidence that somatic L1 retrotransposition can initiate and drive tumorigenesis in humans. Recent studies have: (i) cataloged somatic L1 activity in many epithelial tumor types; (ii) identified specific full-length L1 source elements that give rise to somatic L1 insertions; and (iii) determined that L1 promoter hypomethylation likely plays an early role in the derepression of L1s in somatic tissues. A central challenge moving forward is to determine the extent to which L1 driver mutations can promote tumor initiation, evolution, and metastasis in humans. [ABSTRACT FROM AUTHOR]

The application of next-generation sequencing (NGS) to characterize cancer genomes has resulted in the discovery of numerous genetic markers. Consequently, the number of markers that warrant routine screening in molecular diagnostic laboratories, often from limited tumor material, has increased. This increased demand has been difficult to manage by traditional low- and/or medium-throughput sequencing platforms. Massively parallel sequencing capabilities of NGS provide a much-needed alternative for mutation screening in multiple genes with a single low investment of DNA. However, implementation of NGS technologies, most of which are for research use only (RUO), in a diagnostic laboratory, needs extensive validation in order to establish Clinical Laboratory Improvement Amendments (CLIA) and College of American Pathologists (CAP)-compliant performance characteristics. Here, we have reviewed approaches for validation of NGS technology for routine screening of tumors. We discuss the criteria for selecting gene markers to include in the NGS panel and the deciding factors for selecting target capture approaches and sequencing platforms. We also discuss challenges in result reporting, storage and retrieval of the voluminous sequencing data and the future potential of clinical NGS. [ABSTRACT FROM AUTHOR]

Traditionally, tumors are classified by histopathological criteria, i.e., based on their specific morphological appearances. Consequently, current therapeutic decisions in oncology are strongly influenced by histology rather than underlying molecular or genomic aberrations. The increase of information on molecular changes however, enabled by the Human Genome Project and the International Cancer Genome Consortium as well as the manifold advances in molecular biology and high-throughput sequencing techniques, inaugurated the integration of genomic information into disease classification. Furthermore, in some cases it became evident that former classifications needed major revision and adaption. Such adaptations are often required by understanding the pathogenesis of a disease from a specific molecular alteration, using this molecular driver for targeted and highly effective therapies. Altogether, reclassifications should lead to higher information content of the underlying diagnoses, reflecting their molecular pathogenesis and resulting in optimized and individual therapeutic decisions. The objective of this article is to summarize some particularly important examples of genome-based classification approaches and associated therapeutic concepts. In addition to reviewing disease specific markers, we focus on potentially therapeutic or predictive markers and the relevance of molecular diagnostics in disease monitoring. [ABSTRACT FROM AUTHOR]

Background: As chemical signals of hormones, neuropeptides are essential to regulate cell growth by interacting with their receptors to achieve cell communications in cancer tissues. Previously, neuropeptide transcriptome analysis was limited to tissue-based bulk expression levels. The molecular mechanisms of neuropeptides and their receptors at the single-cell level remain unclear. We conducted a systematic single-cell transcriptome data integration analysis to clarify the similarities and variations of neuropeptide-mediated cell communication between various malignancies. Methods: Based on the single-cell expression information in 72 cancer datasets across 24 cancer types, we characterized actively expressed neuropeptides and receptors as having log values of the quantitative transcripts per million ≥ 1. Then, we created the putative cell-to-cell communication network for each dataset by using the known interaction of those actively expressed neuropeptides and receptors. To focus on the stable cell communication events, we identified neuropeptide and downstream receptors whose interactions were detected in more than half of all conceivable cell-cell interactions (square of the total cell population) in a dataset. Results: Focusing on those actively expressed neuropeptides and receptors, we built over 76 million cell-to-cell communications across 70 cancer datasets. Then the stable cell communication analyses were applied to each dataset, and about 14 million stable cell-to-cell communications could be detected based on 16 neuropeptides and 23 receptors. Further functional analysis indicates these 39 genes could regulate blood pressure and are significantly associated with patients' survival among over ten thousand The Cancer Genome Atlas (TCGA)pan-cancer samples. By zooming in lung cancer-specific clinical features, we discovered the 39 genes appeared to be enriched in the patients with smoking. In skin cancer, they may differ in the patients with the distinct histological subtype and molecular drivers. Conclusions: At the single-cell level, stable cell communications across cancer types demonstrated some common and distinct neuropeptide-receptor patterns, which could be helpful in determining the status of neuropeptide-based cell communication and developing a peptide-based therapy strategy. [ABSTRACT FROM AUTHOR]

For decades, research relating to modification of host immunity towards antitumor response activation has been ongoing, with the breakthrough discovery of immune-checkpoint blockers. Several biomarkers with potential predictive value have been reported in recent studies for these novel therapies. However, with the plethora of therapeutic options existing for a given cancer entity, modern oncology is now being confronted with multifactorial interpretation to devise "the best therapy" for the individual patient. Into the bargain come the multiverse guidelines for established and emerging diagnostic biomarkers, as well as the complex interplay between cancer cells and tumor microenvironment, provoking immense challenges in the therapy decision-making process. Through this review, we present various molecular diagnostic modalities and techniques, such as genomics, immunohistochemistry and quantitative image analysis, which have the potential of becoming powerful tools in the development of an optimal treatment regime when analogized with patient characteristics. We will summarize the underlying complexities of these methods and shed light upon the necessary considerations and requirements for data integration. It is our hope to provide compelling evidence to emphasize on the need for inclusion of integrative data analysis in modern cancer therapy, and thereupon paving a path towards precision medicine and better patient outcomes. [ABSTRACT FROM AUTHOR]

Lynch syndrome patients with synchronous endometrial and ovarian cancer (SEOC) are rare. When these cases occur, they are most often endometrioid histology and early grade. Early-grade tumors are not often sent for somatic tumor profiling. We present a 39 year old SEOC patient with germline PMS2 Lynch syndrome and clinical tumor analysis leading to insight regarding the origin and cause of these tumors, with potential therapy options. PMS2-related SEOC is less common due to lower risks for these cancers associated with germline PMS2 mutation compared to other Lynch genes. While synchronous cancers are not common, they are more likely to occur with Lynch syndrome. Tumor profiling with next-generation sequencing of 648 genes identified sixteen shared somatic actionable and biologically relevant mutations. This case is a rare example of a patient with PMS2 germline Lynch syndrome with shared somatic variants that demonstrate clonality of the two tumors arising from one common site. [ABSTRACT FROM AUTHOR]

Pediatric oncologists' perspectives around returning and incorporating tumor and germline genomic sequencing (GS) results into cancer care are not well-described. To inform optimization of cancer genomics communication, we assessed oncologists' experiences with return of genomic results (ROR), including their preparation/readiness for ROR, collaboration with genetic counselors (GCs) during ROR, and perceived challenges. The BASIC3 study paired pediatric oncologists with GCs to return results to patients' families. We thematically analyzed 24 interviews with 12 oncologists at two post-ROR time points. Oncologists found pre-ROR meetings with GCs and geneticists essential to interpreting patients' reports and communicating results to families. Most oncologists took a collaborative ROR approach where they discussed tumor findings and GCs discussed germline findings. Oncologists perceived many roles for GCs during ROR, including answering families' questions and describing information in lay language. Challenges identified included conveying uncertain information in accessible language, limits of oncologists' genetics expertise, and navigating families' emotional responses. Oncologists emphasized how GCs' and geneticists' support was essential to ROR, especially for germline findings. GS can be successfully integrated into cancer care, but to account for the GC shortage, alternative ROR models and access to genetics resources will be needed to better support families and avoid burdening oncologists. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is a relatively chemo-resistant tumor. Several multi-kinase inhibitors have been approved for treating advanced HCC. However, most HCC patients are highly refractory to these drugs. Therefore, the development of more effective therapies for advanced HCC patients is urgently needed. Stathmin 1 (STMN1) is an oncoprotein that destabilizes microtubules and promotes cancer cell migration and invasion. In this study, cancer genomics data mining identified STMN1 as a prognosis biomarker and a therapeutic target for HCC. Co-expressed gene analysis indicated that STMN1 expression was positively associated with cell-cycle-related gene expression. Chemical sensitivity profiling of HCC cell lines suggested that High-STMN1-expressing HCC cells were the most sensitive to MST-312 (a telomerase inhibitor). Drug–gene connectivity mapping supported that MST-312 reversed the STMN1-co-expressed gene signature (especially BUB1B, MCM2/5/6, and TTK genes). In vitro experiments validated that MST-312 inhibited HCC cell viability and related protein expression (STMN1, BUB1B, and MCM5). In addition, overexpression of STMN1 enhanced the anticancer activity of MST-312 in HCC cells. Therefore, MST-312 can be used for treating STMN1-high expression HCC. [ABSTRACT FROM AUTHOR]

Simple Summary: Classical cytogenetic studies in breast cancer have identified frequent chromosomal aberrations that produce an increased gene copy number in chromosome 1q (1q-gain) and/or a decreased gene copy number in 16q (16q-loss). The understanding of the contribution of such copy number changes to the genesis and progression of cancer is of paramount importance for the design of cancer models and targeted therapies. We exploited molecular data provided by The Cancer Genome Atlas (TCGA) project in order to form different groups of breast cancers bearing 1q-gain and/or 16q-loss or devoid of such aberrations (1,16-chromogroups). An analysis of differential gene expression among 1,16-chromogroups guided the identification of transcriptionally dysregulated 1q and 16q genes. Pathway analysis revealed functional interactions that shed light on novel molecular targets for subtype-specific cancer therapy. Derivative chromosome der(1;16), isochromosome 1q, and deleted 16q—producing arm-level 1q-gain and/or 16q-loss—are recurrent cytogenetic abnormalities in breast cancer, but their exact role in determining the malignant phenotype is still largely unknown. We exploited The Cancer Genome Atlas (TCGA) data to generate and analyze groups of breast invasive carcinomas, called 1,16-chromogroups, that are characterized by a pattern of arm-level somatic copy number aberrations congruent with known cytogenetic aberrations of chromosome 1 and 16. Substantial differences were found among 1,16-chromogroups in terms of other chromosomal aberrations, aneuploidy scores, transcriptomic data, single-point mutations, histotypes, and molecular subtypes. Breast cancers with a co-occurrence of 1q-gain and 16q-loss can be distinguished in a "low aneuploidy score" group, congruent to der(1;16), and a "high aneuploidy score" group, congruent to the co-occurrence of isochromosome 1q and deleted 16q. Another three groups are formed by cancers showing separately 1q-gain or 16q-loss or no aberrations of 1q and 16q. Transcriptome comparisons among the 1,16-chromogroups, integrated with functional pathway analysis, suggested the cooperation of overexpressed 1q genes and underexpressed 16q genes in the genesis of both ductal and lobular carcinomas, thus highlighting the putative role of genes encoding gamma-secretase subunits (APH1A, PSEN2, and NCSTN) and Wnt enhanceosome components (BCL9 and PYGO2) in 1q, and the glycoprotein E-cadherin (CDH1), the E3 ubiquitin-protein ligase WWP2, the deubiquitinating enzyme CYLD, and the transcription factor CBFB in 16q. The analysis of 1,16-chromogroups is a strategy with far-reaching implications for the selection of cancer cell models and novel experimental therapies. [ABSTRACT FROM AUTHOR]

Many proteins contain intrinsically disordered regions (IDRs) which carry out important functions without relying on a single well-defined conformation. IDRs are increasingly recognized as critical elements of regulatory networks and have been also associated with cancer. However, it is unknown whether mutations targeting IDRs represent a distinct class of driver events associated with specific molecular and system-level properties, cancer types and treatment options. Here, we used an integrative computational approach to explore the direct role of intrinsically disordered protein regions driving cancer. We showed that around 20% of cancer drivers are primarily targeted through a disordered region. These IDRs can function in multiple ways which are distinct from the functional mechanisms of ordered drivers. Disordered drivers play a central role in context-dependent interaction networks and are enriched in specific biological processes such as transcription, gene expression regulation and protein degradation. Furthermore, their modulation represents an alternative mechanism for the emergence of all known cancer hallmarks. Importantly, in certain cancer patients, mutations of disordered drivers represent key driving events. However, treatment options for such patients are currently severely limited. The presented study highlights a largely overlooked class of cancer drivers associated with specific cancer types that need novel therapeutic options. [ABSTRACT FROM AUTHOR]

Genomic structural variants comprise a significant fraction of somatic mutations driving cancer onset and progression. However, such variants are not readily revealed by standard next-generation sequencing. Optical genome mapping (OGM) surpasses short-read sequencing in detecting large (>500 bp) and complex structural variants (SVs) but requires isolation of ultra-high-molecular-weight DNA from the tissue of interest. We have successfully applied a protocol involving a paramagnetic nanobind disc to a wide range of solid tumors. Using as little as 6.5 mg of input tumor tissue, we show successful extraction of high-molecular-weight genomic DNA that provides a high genomic map rate and effective coverage by optical mapping. We demonstrate the system's utility in identifying somatic SVs affecting functional and cancer-related genes for each sample. Duplicate/triplicate analysis of select samples shows intra-sample reliability but also intra-sample heterogeneity. We also demonstrate that simply filtering SVs based on a GRCh38 human control database provides high positive and negative predictive values for true somatic variants. Our results indicate that the solid tissue DNA extraction protocol, OGM and SV analysis can be applied to a wide variety of solid tumors to capture SVs across the entire genome with functional importance in cancer prognosis and treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: The cancer-immunity cycle (CIC) is a series of self-sustaining stepwise events to fight cancer growth by the immune system. We hypothesized that immunofunctional phenotyping that represent the malfunction of the CIC is clinically relevant in breast cancer (BC) utilizing total of 2979 BC cases; 1075 from TCGA cohort, 1904 from METABRIC cohort were analyzed. The immunofunctional phenotype was classified as follows: hot T-cell infiltrated, high immune cytolytic activity (CYT), cold T-cell infiltrated, high frequency of CD8+ T cells and low CYT, and non-inflamed, low frequency of CD8+ T cells and low CYT. We demonstrated that immunofunctional phenotyping not only indicated the degree of anti-cancer immune dysfunction, but also served as a prognostic biomarker and HTI was inversely related to estrogen response. The cancer-immunity cycle (CIC) is a series of self-sustaining stepwise events to fight cancer growth by the immune system. We hypothesized that immunofunctional phenotyping that represent the malfunction of the CIC is clinically relevant in breast cancer (BC). Total of 2979 BC cases; 1075 from TCGA cohort, 1904 from METABRIC cohort were analyzed. The immunofunctional phenotype was classified as follows: hot T-cell infiltrated (HTI), high immune cytolytic activity (CYT), Cold T-cell infiltrated (CTI), high frequency of CD8+ T cells and low CYT, and non-inflamed, low frequency of CD8+ T cells and low CYT. The analysis of tumor immune microenvironment in the immunofunctional phenotype revealed that not only immunostimulatory factors, but also immunosuppressive factors were significantly elevated and immunosuppressive cells were significantly decreased in HTI. Patients in HTI were significantly associated with better survival in whole cohort and patients in CTI were significantly associated with worse survival in triple negative. Furthers, HTI was inversely related to estrogen responsive signaling. We demonstrated that immunofunctional phenotype not only indicated the degree of anti-cancer immune dysfunction, but also served as a prognostic biomarker and HTI was inversely related to estrogen response. [ABSTRACT FROM AUTHOR]

Recent public policy, governmental regulatory and economic trends have motivated the establishment and deepening of community health and academic medical center alliances. Accordingly, community oncology practices now deliver a significant portion of their oncology care in association with academic cancer centers. In the age of precision medicine, this alliance has acquired critical importance; novel advances in nucleic acid sequencing, the generation and analysis of immense data sets, the changing clinical landscape of hereditary cancer predisposition and ongoing discovery of novel, targeted therapies challenge community-based oncologists to deliver molecularly-informed health care. The active engagement of community oncology practices with academic partners helps with meeting these challenges; community/academic alliances result in improved cancer patient care and provider efficacy. Here, we review the community oncology and academic medical center alliance. We examine how practitioners may leverage academic center precision medicine-based cancer genetics and genomics programs to advance their patients' needs. We highlight a number of project initiatives at the City of Hope Comprehensive Cancer Center that seek to optimize community oncology and academic cancer center precision medicine interactions. [ABSTRACT FROM AUTHOR]

Astrocytomas are primary human brain tumors including diffuse or anaplastic astrocytomas that develop towards secondary glioblastomas over time. However, only little is known about molecular alterations that drive this progression. We measured multi-omics profiles of patient-matched astrocytoma pairs of initial and recurrent tumors from 22 patients to identify molecular alterations associated with tumor progression. Gene copy number profiles formed three major subcluters, but more than half of the patient-matched astrocytoma pairs differed in their gene copy number profiles like astrocytomas from different patients. Chromosome 10 deletions were not observed for diffuse astrocytomas, but occurred in corresponding recurrent tumors. Gene expression profiles formed three other major subclusters and patient-matched expression profiles were much more heterogeneous than their copy number profiles. Still, recurrent tumors showed a strong tendency to switch to the mesenchymal subtype. The direct progression of diffuse astrocytomas to secondary glioblastomas showed the largest number of transcriptional changes. Astrocytoma progression groups were further distinguished by signaling pathway expression signatures affecting cell division, interaction and differentiation. As expected, IDH1 was most frequently mutated closely followed by TP53, but also MUC4 involved in the regulation of apoptosis and proliferation was frequently mutated. Astrocytoma progression groups differed in their mutation frequencies of these three genes. Overall, patient-matched astrocytomas can differ substantially within and between patients, but still molecular signatures associated with the progression to secondary glioblastomas exist and should be analyzed for their potential clinical relevance in future studies. [ABSTRACT FROM AUTHOR]

Non-epithelial ovarian tumors are heterogeneous and account for approximately 10% of ovarian malignancies. The most common subtypes of non-epithelial ovarian tumors arise from germ cells or sex cord and stromal cells of the gonads. These tumors are usually detected at an early stage, and management includes surgical staging and debulking. When indicated for advanced disease, most respond to chemotherapy; however, options for patients with refractory disease are limited, and regimens can be associated with significant toxicities, including permanent organ dysfunction, secondary malignancies, and death. Targeted therapies that potentially decrease chemotherapy-related adverse effects and improve outcomes for patients with chemotherapy-refractory disease are needed. Here, we review the molecular landscape of non-epithelial ovarian tumors for the purpose of informing rational clinical trial design. Recent genomic discoveries have uncovered recurring somatic alterations and germline mutations in subtypes of non-epithelial ovarian tumors. Though there is a paucity of efficacy data on targeted therapies, such as kinase inhibitors, antibody–drug conjugates, immunotherapy, and hormonal therapy, exceptional responses to some compounds have been reported. The rarity and complexity of non-epithelial ovarian tumors warrant collaboration and efficient clinical trial design, including high-quality molecular characterization, to guide future efforts. [ABSTRACT FROM AUTHOR]

Rapid development of high throughput genome analysis technologies accompanied by significant reduction in costs has led to the accumulation of an incredible amount of data during the last decade. The emergence of big data has had a particularly significant impact in biomedical research by providing unprecedented, systems-level access to many disease states including cancer, and has created promising opportunities as well as new challenges. Arguably, the most significant challenge cancer research currently faces is finding effective ways to use big data to improve our understanding of molecular mechanisms underlying tumorigenesis and developing effective new therapies. Functional exploration of these datasets and testing predictions from computational approaches using experimental models to interrogate their biological relevance is a key step towards achieving this goal. Given the daunting scale and complexity of the big data available, experimental systems like Drosophila that allow large-scale functional studies and complex genetic manipulations in a rapid, cost-effective manner will be of particular importance for this purpose. Findings from these large-scale exploratory functional studies can then be used to formulate more specific hypotheses to be explored in mammalian models. Here, I will discuss several strategies for functional exploration of big cancer data using Drosophila cancer models. [ABSTRACT FROM AUTHOR]

Cancer is one of the leading causes of mortality worldwide. Breast cancer is the most frequent cancer in women, and in recent years it has become a serious public health problem in Colombia. The development of large-scale omic techniques allows simultaneous analysis of all active genes in tumor cells versus normal cells, providing new ways to discover the drivers of malignant transformations. Whole exome sequencing (WES) was obtained to provide a deep view of the mutational genomic profile in a set of cancer samples from Southwest Colombian women. WES was performed on 52 tumor samples from patients diagnosed with invasive breast cancer, which in most cases (33/52) were ductal luminal breast carcinomas (IDC-LM-BRCA). Global variant call was calculated, and six different algorithms were applied to filter out false positives and identify pathogenic variants. To compare and expand the somatic tumor variants found in the Colombian cohort, exome mutations and genome-wide expression alterations were detected in a larger set of tumor samples of the same breast cancer subtype from TCGA (that included DNA-seq and RNA-seq data). Genes with significant changes in both the mutational and expression profiles were identified, providing a set of genes and mutations associated with the etiology of ductal luminal breast cancer. This set included 19 single mutations identified as tumor driver mutations in 17 genes. Some of the genes (ATM, ERBB3, ESR1, TP53) are well-known cancer genes, while others (CBLB, PRPF8) presented driver mutations that had not been reported before. In the case of the CBLB gene, several mutations were identified in TCGA IDC-LM-BRCA samples associated with overexpression of this gene and repression of tumor suppressive activity of TGF-β pathway. [ABSTRACT FROM AUTHOR]

Internationally accepted classifications of malignant tumors, developed by the World Health Organization (WHO) and the Union for International Cancer Control (UICC), are based on the histotype, site of origin, morphologic grade, and spread of cancer throughout the body. The WHO classifications are the foundation of cancer diagnosis and the starting point for cancer management. Starting in 2000, the WHO classifications began to include biologic and molecular–genetic features. These developments are having a strong impact on cancer diagnosis and treatment, and this impact is amplifying, given the advances in cancer genomics. Molecular–genetic profiling can be used to refine existing classifications of tumors and, for a small but increasing number of cancers, even determine the treatment irrespective of histotype. Here I discuss how cancer classifications may change in the era of cancer genomics. [ABSTRACT FROM AUTHOR]

Breast cancer is the most commonly occurring cancer in women. There were over two-million new cases in world in 2018. It is the second leading cause of death from cancer in western countries. At the molecular level, breast cancer is a heterogeneous disease, which is characterized by high genomic instability evidenced by somatic gene mutations, copy number alterations, and chromosome structural rearrangements. The genomic instability is caused by defects in DNA damage repair, transcription, DNA replication, telomere maintenance and mitotic chromosome segregation. According to molecular features, breast cancers are subdivided in subtypes, according to activation of hormone receptors (estrogen receptor and progesterone receptor), of human epidermal growth factors receptor 2 (HER2), and or BRCA mutations. In-depth analyses of the molecular features of primary and metastatic breast cancer have shown the great heterogeneity of genetic alterations and their clonal evolution during disease development. These studies have contributed to identify a repertoire of numerous disease-causing genes that are altered through different mutational processes. While early-stage breast cancer is a curable disease in about 70% of patients, advanced breast cancer is largely incurable. However, molecular studies have contributed to develop new therapeutic approaches targeting HER2, CDK4/6, PI3K, or involving poly(ADP-ribose) polymerase inhibitors for BRCA mutation carriers and immunotherapy. [ABSTRACT FROM AUTHOR]

The cancer-immunity cycle (CIC) is a series of self-sustaining stepwise events to fight cancer growth by the immune system. We hypothesized that immunofunctional phenotyping that represent the malfunction of the CIC is clinically relevant in breast cancer (BC). Total of 2979 BC cases; 1075 from TCGA cohort, 1904 from METABRIC cohort were analyzed. The immunofunctional phenotype was classified as follows: hot T-cell infiltrated (HTI), high immune cytolytic activity (CYT), Cold T-cell infiltrated (CTI), high frequency of CD8+ T cells and low CYT, and non-inflamed, low frequency of CD8+ T cells and low CYT. The analysis of tumor immune microenvironment in the immunofunctional phenotype revealed that not only immunostimulatory factors, but also immunosuppressive factors were significantly elevated and immunosuppressive cells were significantly decreased in HTI. Patients in HTI were significantly associated with better survival in whole cohort and patients in CTI were significantly associated with worse survival in triple negative. Furthers, HTI was inversely related to estrogen responsive signaling. We demonstrated that immunofunctional phenotype not only indicated the degree of anti-cancer immune dysfunction, but also served as a prognostic biomarker and HTI was inversely related to estrogen response.

BRCA1 and BRCA2 are the genes most frequently associated with hereditary breast and ovarian cancer (HBOC). They are crucial for the maintenance of genome stability, particularly in the homologous recombination-mediated repair pathway of DNA double-strand breaks (HR-DSBR). Widespread BRCA1/2 next-generation sequencing (NGS) screening has revealed numerous variants of uncertain significance. Assessing the clinical significance of these variants is challenging, particularly regarding the clinical management of patients. Here, we report the functional characterization of the unclassified BRCA2 c.8299C > T variant, identified in a young breast cancer patient during BRCA1/2 NGS screening. This variant causes the change of Proline 2767 to Serine in the DNA binding domain (DBD) of the BRCA2 protein, necessary for the loading of RAD51 on ssDNA during the HR-DSBR. Our in silico analysis and 3D-structure modeling predicted that the p.Pro2767Ser substitution is likely to alter the BRCA2 DBD structure and function. Therefore, to evaluate the functional impact of the p.Pro2767Ser variant, we used a minigene encoding a truncated protein that contains the BRCA2 DBD and the nearby nuclear localization sequence. We found that the ectopically expressed truncated protein carrying the normal DBD, which retains the DNA binding function and lacks the central RAD51 binding domain, interferes with endogenous wild-type BRCA2 mediator functions in the HR-DSBR. We also demonstrated that the BRCA2 Pro2767Ser DBD is unable to compete with endogenous BRCA2 DNA binding, thereby suggesting that the p.Pro2767Ser substitution in the full-length protein causes the functional loss of BRCA2. Consequently, our data suggest that the p.Pro2767Ser variant should be considered pathogenic, thus supporting a revision of the ClinVar interpretation. Moreover, our experimental strategy could be a valid method with which to preliminarily evaluate the pathogenicity of the unclassified BRCA2 germline variants in the DBD and their risk of predisposing to HBOC. [ABSTRACT FROM AUTHOR]

In silico drug prescription tools for precision cancer medicine can match molecular alterations with tailored candidate treatments. These methodologies require large and well-annotated datasets to systematically evaluate their performance, but this is currently constrained by the lack of complete patient clinicopathological data. Moreover, in silico drug prescription performance could be improved by integrating additional tumour information layers like intra-tumour heterogeneity (ITH) which has been related to drug response and tumour progression. PanDrugs is an in silico drug prescription method which prioritizes anticancer drugs combining both biological and clinical evidence. We have systematically evaluated PanDrugs in the Genomic Data Commons repository (GDC). Our results showed that PanDrugs is able to establish an a priori stratification of cancer patients treated with Epidermal Growth Factor Receptor (EGFR) inhibitors. Patients labelled as responders according to PanDrugs predictions showed a significantly increased overall survival (OS) compared to non-responders. PanDrugs was also able to suggest alternative tailored treatments for non-responder patients. Additionally, PanDrugs usefulness was assessed considering spatial and temporal ITH in cancer patients and showed that ITH can be approached therapeutically proposing drugs or combinations potentially capable of targeting the clonal diversity. In summary, this study is a proof of concept where PanDrugs predictions have been correlated to OS and can be useful to manage ITH in patients while increasing therapeutic options and demonstrating its clinical utility. [ABSTRACT FROM AUTHOR]

CDK8 and CDK19 Mediator kinases are transcriptional co-regulators implicated in several types of cancer. Small-molecule CDK8/19 inhibitors have recently entered or are entering clinical trials, starting with breast cancer and acute myeloid leukemia (AML). To identify other cancers where these novel drugs may provide benefit, we queried genomic and transcriptomic databases for potential impact of CDK8, CDK19, or their binding partner CCNC. sgRNA analysis of a panel of tumor cell lines showed that most tumor types represented in the panel, except for some central nervous system tumors, were not dependent on these genes. In contrast, analysis of clinical samples for alterations in these genes revealed a high frequency of gene amplification in two highly aggressive subtypes of prostate cancer and in some cancers of the GI tract, breast, bladder, and sarcomas. Analysis of survival correlations identified a group of cancers where CDK8 expression correlated with shorter survival (notably breast, prostate, cervical cancers, and esophageal adenocarcinoma). In some cancers (AML, melanoma, ovarian, and others), such correlations were limited to samples with a below-median tumor mutation burden. These results suggest that Mediator kinases are especially important in cancers that are driven primarily by transcriptional rather than mutational changes and warrant an investigation of their role in additional cancer types. [ABSTRACT FROM AUTHOR]

The burden of somatic mutations and neoantigens has been associated with improved survival in cancer treated with immunotherapies, especially non-small cell lung cancer (NSCLC). However, there is uncertainty about their effect on outcome in early-stage untreated cases. We posited that the burden of mutations in a specific set of genes may also contribute to the prognosis of early NSCLC patients. From a small cohort of 36 NSCLC cases, we were able to identify somatic mutations and copy number alterations in 865 genes that contributed to patient overall survival. Simply, the number of altered genes (NAG) among these 865 genes was associated with longer disease-free survival (hazard ratio (HR) = 0.153, p = 1.48 × 10−4). The gene expression signature distinguishing patients with high/low NAG was also prognostic in three independent datasets. Patients with a high NAG could be further stratified based on the presence of immunogenic mutations, revealing a further subgroup of stage I NSCLC with even better prognosis (85% with >5 years survival), and associated with cytotoxic T-cell expression. Importantly, 95% of the highly-altered genes lacked direct relation to cancer, but were implicated in pathways regulating cell proliferation, motility and immune response. [ABSTRACT FROM AUTHOR]

Because traditional treatment strategies for advanced gastrointestinal (GI) cancers often have a limited therapeutic effect, immunotherapy could be a viable approach for the therapy of advanced GI cancers, considering the recent success of immunotherapy in treating various refractory malignancies, including the DNA mismatch repair-deficient GI cancers. However, only a subset of cancer patients currently respond to immunotherapy. Thus, it is important to identify useful biomarkers for predicting cancer immunotherapy response. The tumor suppressor gene ARID1A has a high mutation rate in GI cancers and its deficiency is correlated with the microsatellite instability (MSI) genomic feature of cancer. We investigated the correlation between ARID1A mutations and tumor immunity using three GI cancer genomics datasets by the bioinformatic approach, and found that diverse antitumor immune signatures were more highly enriched in ARID1A-mutated GI cancers than in ARID1A-wildtype GI cancers. The elevated immune activity in ARID1A-mutated GI cancers was associated with the higher tumor mutation burden and lower tumor aneuploidy level, as well as a higher proportion of MSI cancers in this GI cancer subtype. Moreover, we found that ARID1A-mutated GI cancers more highly expressed PD-L1 than ARID1A-wildtype GI cancers. The elevated antitumor immune signatures and PD-L1 expression could contribute to the more active immunotherapeutic responsiveness and better survival prognosis in ARID1A-mutated GI cancers than in ARID1A-wildtype GI cancers in the immunotherapy setting, as evidenced in three cancer cohorts receiving immunotherapy. Thus, the ARID1A mutation could be a useful biomarker for identifying GI cancer patients responsive to immunotherapy. [ABSTRACT FROM AUTHOR]

Prostate cancer (CaP) is the most commonly diagnosed non-cutaneous cancer and the second leading cause of male cancer deaths in the United States. Among African American (AA) men, CaP is the most prevalent malignancy, with disproportionately higher incidence and mortality rates. Even after discounting the influence of socioeconomic factors, the effect of molecular and genetic factors on racial disparity of CaP is evident. Earlier studies on the molecular basis for CaP disparity have focused on the influence of heritable mutations and single-nucleotide polymorphisms (SNPs). Most CaP susceptibility alleles identified based on genome-wide association studies (GWAS) were common, low-penetrance variants. Germline CaP-associated mutations that are highly penetrant, such as those found in HOXB13 and BRCA2 , are usually rare. More recently, genomic studies enabled by Next-Gen Sequencing (NGS) technologies have focused on the identification of somatic mutations that contribute to CaP tumorigenesis. These studies confirmed the high prevalence of ERG gene fusions and PTEN deletions among Caucasian Americans and identified novel somatic alterations in SPOP and FOXA1 genes in early stages of CaP. Individuals with African ancestry and other minorities are often underrepresented in these large-scale genomic studies, which are performed primarily using tumors from men of European ancestry. The insufficient number of specimens from AA men and other minority populations, together with the heterogeneity in the molecular etiology of CaP across populations, challenge the generalizability of findings from these projects. Efforts to close this gap by sequencing larger numbers of tumor specimens from more diverse populations, although still at an early stage, have discovered distinct genomic alterations. These research findings can have a direct impact on the diagnosis of CaP, the stratification of patients for treatment, and can help to address the disparity in incidence and mortality of CaP. This review examines the progress of understanding in CaP genetics and genomics and highlight the need to increase the representation from minority populations., Competing Interests: The authors declare no conflict of interest.