Essential genes shape cancer genomes through linear limitation of homozygous deletions

Copy number losses of oncogenes and gains of tumor suppressor genes generate common driver mutations

Cancer driver genes can undergo positive selection for various types of genetic alterations, including gain-of-function or loss-of-function mutations and copy number alterations (CNA). We investigated the landscape of different types of alterations affecting driver genes in 17,644 cancer exomes and genomes. We find that oncogenes may simultaneously exhibit signatures of positive selection and also negative selection in different gene segments, suggesting a method to identify additional tumor types where an oncogene is a driver or a vulnerability. Next, we characterize the landscape of CNA-dependent selection effects, revealing a general trend of increased positive selection on oncogene mutations not only upon CNA gains but also upon CNA deletions. Similarly, we observe a positive interaction between mutations and CNA gains in tumor suppressor genes. Thus, two-hit events involving point mutations and CNA are universally observed regardless of the type of CNA and may signal new therapeutic opportunities. An analysis with focus on the somatic CNA two-hit events can help identify additional driver genes relevant to a tumor type. By a global inference of point mutation and CNA selection signatures and interactions thereof across genes and tissues, we identify 9 evolutionary archetypes of driver genes, representing different mechanisms of (in)activation by genetic alterations.

Paralog dispensability shapes homozygous deletion patterns in tumor genomes

Genomic instability is a hallmark of cancer, resulting in tumor genomes having large numbers of genetic aberrations, including homozygous deletions of protein coding genes. That tumor cells remain viable in the presence of such gene loss suggests high robustness to genetic perturbation. In model organisms and cancer cell lines, paralogs have been shown to contribute substantially to genetic robustness-they are generally more dispensable for growth than singletons. Here, by analyzing copy number profiles of > 10,000 tumors, we test the hypothesis that the increased dispensability of paralogs shapes tumor genome evolution. We find that genes with paralogs are more likely to be homozygously deleted and that this cannot be explained by other factors known to influence copy number variation. Furthermore, features that influence paralog dispensability in cancer cell lines correlate with paralog deletion frequency in tumors. Finally, paralogs that are broadly essential in cancer cell lines are less frequently deleted in tumors than non-essential paralogs. Overall, our results suggest that homozygous deletions of paralogs are more frequently observed in tumor genomes because paralogs are more dispensable.

CD8/PD-L1 immunohistochemical reactivity and gene alterations in cutaneous squamous cell carcinoma

In recent years, several immune checkpoint inhibitors targeting programmed death-ligand 1 (PD-L1) or PD-1 have been developed for cancer therapy. The genetic background of tumors and factors that influence PD-L1 expression in tumor tissues are not yet elucidated in cutaneous squamous cell carcinoma (cSCC). CD8-positive tumor-infiltrating lymphocytes (TILs) are known to be related to tumor immunity. Here, we aimed to study the relationship between CD8/PD-L1 immunohistochemical reactivity and gene alterations in cSCC. Tumorigenic genes were examined to identify gene alterations using next-generation sequencing (NGS). We collected 27 cSCC tissue samples (from 13 metastatic and 14 non-metastatic patients at primary diagnosis). We performed immunohistochemical staining for CD8 and PD-L1, and NGS using a commercially available sequencing panel (Illumina Cancer Hotspot Panel V2) that targets 50 cancer-associated genes. Immunohistochemically, CD8-positive TILs showed a high positive score in cSCC without metastasis; in these cases, cSCC occurred predominantly in sun-exposed areas, the tumor size was smaller, and the total gene variation numbers were notably low. The tumor depth, PD-L1 positivity, and gene variation number with or without tumor metastasis were not related, but the gene variation number tended to be higher in cSCCs arising in non-sun-exposed areas. Tumor metastasis was more common in cSCC arising in non-sun-exposed areas, which decreased the number of TILs or CD8-positive cells. From a genetic perspective, the total gene alterations were higher in cSCC with metastasis. Among them, ERBB4 and NPM1 are presumably involved in cSCC tumorigenesis; in addition, GNAQ, GNAS, JAK2, NRAS, IDH2, and CTNNB1 may be related to tumor metastasis. These results provide information on potential genes that can be targeted for cSCC therapy and on immune checkpoint inhibitors that may be used for cSCC therapy.

Metabolic collateral lethal target identification reveals MTHFD2 paralogue dependency in ovarian cancer

Recurrent loss-of-function deletions cause frequent inactivation of tumour suppressor genes but often also involve the collateral deletion of essential genes in chromosomal proximity, engendering dependence on paralogues that maintain similar function. Although these paralogues are attractive anticancer targets, no methodology exists to uncover such collateral lethal genes. Here we report a framework for collateral lethal gene identification via metabolic fluxes, CLIM, and use it to reveal MTHFD2 as a collateral lethal gene in UQCR11-deleted ovarian tumours. We show that MTHFD2 has a non-canonical oxidative function to provide mitochondrial NAD+, and demonstrate the regulation of systemic metabolic activity by the paralogue metabolic pathway maintaining metabolic flux compensation. This UQCR11-MTHFD2 collateral lethality is confirmed in vivo, with MTHFD2 inhibition leading to complete remission of UQCR11-deleted ovarian tumours. Using CLIM's machine learning and genome-scale metabolic flux analysis, we elucidate the broad efficacy of targeting MTHFD2 despite distinct cancer genetic profiles co-occurring with UQCR11 deletion and irrespective of stromal compositions of tumours.

Investigating the shared genetic architecture of uterine leiomyoma and breast cancer: A genome-wide cross-trait analysis

Little is known regarding the shared genetic architecture or causality underlying the phenotypic association observed for uterine leiomyoma (UL) and breast cancer (BC). Leveraging summary statistics from the hitherto largest genome-wide association study (GWAS) conducted in each trait, we investigated the genetic overlap and causal associations of UL with BC overall, as well as with its subtypes defined by the status of estrogen receptor (ER). We observed a positive genetic correlation between UL and BC overall (rg = 0.09, p = 6.00 × 10-3), which was consistent in ER+ subtype (rg = 0.06, p = 0.01) but not in ER- subtype (rg = 0.06, p = 0.08). Partitioning the whole genome into 1,703 independent regions, local genetic correlation was identified at 22q13.1 for UL with BC overall and with ER+ subtype. Significant genetic correlation was further discovered in 9 out of 14 functional categories, with the highest estimates observed in coding, H3K9ac, and repressed regions. Cross-trait meta-analysis identified 9 novel loci shared between UL and BC. Mendelian randomization demonstrated a significantly increased risk of BC overall (OR = 1.09, 95% CI = 1.01-1.18) and ER+ subtype (OR = 1.09, 95% CI = 1.01-1.17) for genetic liability to UL. No reverse causality was found. Our comprehensive genome-wide cross-trait analysis demonstrates a shared genetic basis, pleiotropic loci, as well as a putative causal relationship between UL and BC, highlighting an intrinsic link underlying these two complex female diseases.

Refinement of computational identification of somatic copy number alterations using DNA methylation microarrays illustrated in cancers of unknown primary

High-throughput genomic technologies are increasingly used in personalized cancer medicine. However, computational tools to maximize the use of scarce tissues combining distinct molecular layers are needed. Here we present a refined strategy, based on the R-package 'conumee', to better predict somatic copy number alterations (SCNA) from deoxyribonucleic acid (DNA) methylation arrays. Our approach, termed hereafter as 'conumee-KCN', improves SCNA prediction by incorporating tumor purity and dynamic thresholding. We trained our algorithm using paired DNA methylation and SNP Array 6.0 data from The Cancer Genome Atlas samples and confirmed its performance in cancer cell lines. Most importantly, the application of our approach in cancers of unknown primary identified amplified potentially actionable targets that were experimentally validated by Fluorescence in situ hybridization and immunostaining, reaching 100% specificity and 93.3% sensitivity.

CRISPR-Cas9 library screening approach for anti-cancer drug discovery: overview and perspectives

CRISPR-Cas9 is a Nobel Prize-winning robust gene-editing tool developed in the last decade. This technique enables a stable genetic engineering method with high precision on the genomes of all organisms. The latest advances in the technology include a genome library screening approach, which can detect survival-essential and drug resistance genes via gain or loss of function. The versatile machinery allows genomic screening for gene activation or inhibition, and targets non-coding sequences, such as promoters, miRNAs, and lncRNAs. In this review, we introduce the emerging high-throughput CRISPR-Cas9 library genome screening technology and its working principles to detect survival and drug resistance genes through positive and negative selection. The technology is compared with other existing approaches while focusing on the advantages of its variable applications in anti-cancer drug discovery, including functions and target identification, non-coding RNA information, actions of small molecules, and drug target discoveries. The combination of the CRISPR-Cas9 system with multi-omic platforms represents a dynamic field expected to advance anti-cancer drug discovery and precision medicine in the clinic.

On the relation of gene essentiality to intron structure: a computational and deep learning approach

Essential genes have been studied by copy number variants and deletions, both associated with introns. The premise of our work is that introns of essential genes have distinct characteristic properties. We provide support for this by training a deep learning model and demonstrating that introns alone can be used to classify essentiality. The model, limited to first introns, performs at an increased level, implicating first introns in essentiality. We identify unique properties of introns of essential genes, finding that their structure protects against deletion and intron-loss events, especially centered on the first intron. We show that GC density is increased in the first introns of essential genes, allowing for increased enhancer activity, protection against deletions, and improved splice site recognition. We find that first introns of essential genes are of remarkably smaller size than their nonessential counterparts, and to protect against common 3' end deletion events, essential genes carry an increased number of (smaller) introns. To demonstrate the importance of the seven features we identified, we train a feature-based model using only these features and achieve high performance.

In-vitro tumor microenvironment models containing physical and biological barriers for modelling multidrug resistance mechanisms and multidrug delivery strategies

The complexity and heterogeneity of the three-dimensional (3D) tumor microenvironment have brought challenges to tumor studies and cancer treatment. The complex functions and interactions of cells involved in tumor microenvironment have led to various multidrug resistance (MDR) and raised challenges for cancer treatment. Traditional tumor models are limited in their ability to simulate the resistance mechanisms and not conducive to the discovery of multidrug resistance and delivery processes. New technologies for making 3D tissue models have shown the potential to simulate the 3D tumor microenvironment and identify mechanisms underlying the MDR. This review overviews the main barriers against multidrug delivery in the tumor microenvironment and highlights the advances in microfluidic-based tumor models with the success in simulating several drug delivery barriers. It also presents the progress in modeling various genetic and epigenetic factors involved in regulating the tumor microenvironment as a noticeable insight in 3D microfluidic tumor models for recognizing multidrug resistance and delivery mechanisms. Further correlation between the results obtained from microfluidic drug resistance tumor models and the clinical MDR data would open up avenues to gain insight into the performance of different multidrug delivery treatment strategies.

DEG 15, an update of the Database of Essential Genes that includes built-in analysis tools

Essential genes refer to genes that are required by an organism to survive under specific conditions. Studies of the minimal-gene-set for bacteria have elucidated fundamental cellular processes that sustain life. The past five years have seen a significant progress in identifying human essential genes, primarily due to the successful use of CRISPR/Cas9 in various types of human cells. DEG 15, a new release of the Database of Essential Genes (www.essentialgene.org), has provided major advancements, compared to DEG 10. Specifically, the number of eukaryotic essential genes has increased by more than fourfold, and that of prokaryotic ones has more than doubled. Of note, the human essential-gene number has increased by more than tenfold. Moreover, we have developed built-in analysis modules by which users can perform various analyses, such as essential-gene distributions between bacterial leading and lagging strands, sub-cellular localization distribution, enrichment analysis of gene ontology and KEGG pathways, and generation of Venn diagrams to compare and contrast gene sets between experiments. Additionally, the database offers customizable BLAST tools for performing species- and experiment-specific BLAST searches. Therefore, DEG comprehensively harbors updated human-curated essential-gene records among prokaryotes and eukaryotes with built-in tools to enhance essential-gene analysis.

BRCA2, ATM, and CDK12 Defects Differentially Shape Prostate Tumor Driver Genomics and Clinical Aggression

PURPOSE

DNA damage repair (DDR) defects are common across cancer types and can indicate therapeutic vulnerability. Optimal exploitation of DDR defects in prostate cancer requires new diagnostic strategies and a better understanding of associated clinical genomic features.

EXPERIMENTAL DESIGN

We performed targeted sequencing of 1,615 plasma cell-free DNA samples from 879 patients with metastatic prostate cancer. Depth-based copy-number calls and heterozygous SNP imbalance were leveraged to expose DDR-mutant allelic configuration and categorize mechanisms of biallelic loss. We used split-read structural variation analysis to characterize tumor suppressor rearrangements. Patient-matched archival primary tissue was analyzed identically.

RESULTS

BRCA2, ATM, and CDK12 were the most frequently disrupted DDR genes in circulating tumor DNA (ctDNA), collectively mutated in 15% of evaluable cases. Biallelic gene disruption via second somatic alteration or mutant allele-specific imbalance was identified in 79% of patients. A further 2% exhibited homozygous BRCA2 deletions. Tumor suppressors TP53, RB1, and PTEN were controlled via disruptive chromosomal rearrangements in BRCA2-defective samples, but via oncogene amplification in context of CDK12 defects. TP53 mutations were rare in cases with ATM defects. DDR mutations were re-detected across 94% of serial ctDNA samples and in all available archival primary tissues, indicating they arose prior to metastatic progression. Loss of BRCA2 and CDK12, but not ATM, was associated with poor clinical outcomes.

CONCLUSIONS

BRCA2, ATM, and CDK12 defects are each linked to distinct prostate cancer driver genomics and aggression. The consistency of DDR status in longitudinal samples and resolution of allelic status underscores the potential for ctDNA as a diagnostic tool.