A census of human cancer genes

Case report: TP53 c.848G>A germline mutation as a possible screening target at initial diagnosis for acute lymphoblastic leukemia

BACKGROUD

Li-Fraumeni syndrome is a hereditary tumor syndrome characterized by an elevated risk of malignancy, particularly acute lymphoblastic leukemia (ALL), which can be caused by the heterozygous germline mutation. TP53 gene germline mutation is considered a potential risk factor and crucial prognostic parameter for acute leukemia development and diagnosis, but rarely occurs in adults, and its specific pathogenic significance in acute leukemia is unclear.

CASE PRESENTATION

We describes a case of a 45-year-old woman diagnosed with ALL. Whole-exome sequencing approach identified one of the TP53 germline mutations from her bone marrow sample with possible pathogenic significance, c.848G>A (p.Arg283His) heterozygous missense mutation located on exon 8, which was further verified in her hair, oral mucous and nail samples. Family pedigree screening revealed that the same TP53 genetic variant was present in the patient's father and non-donor son, whereas not in the donor. Digital PCR observed that this point mutation frequency dropped post-transplantation but remained low during maintenance therapy when the patient was leukemia-free.

CONCLUSION

This suspected Li-Fraumeni syndrome case report with a likely pathogenic heterozygous TP53 variant expands the cancer genetic spectrum. Screening her family members for mutations facilitates identifying the optimal relative donor and avoids unnecessary treatment by monitoring TP53 germline mutations for minimal residual disease following hematopoietic stem cell transplantation. Its potential roles in hematological malignant tumor development and clinical pathogenic implications necessitate further probing.

Study on chromatin regulation patterns of expression vectors in the PhiC31 integration site

The PhiC31 integration system allows for targeted and efficient transgene integration and expression by recognizing pseudo attP sites in mammalian cells and integrating the exogenous genes into the open chromatin regions of active chromatin. In order to investigate the regulatory patterns of efficient gene expression in the open chromatin region of PhiC31 integration, this study utilized Ubiquitous Chromatin Opening Element (UCOE) and activating RNA (saRNA) to modulate the chromatin structure in the promoter region of the PhiC31 integration vector. The study analysed the effects of DNA methylation and nucleosome occupancy changes in the integrated promoter on gene expression levels. The results showed that for the OCT4 promoter with moderate CG density, DNA methylation had a smaller impact on expression compared to changes in nucleosome positioning near the transcription start site, which was crucial for enhancing downstream gene expression. On the other hand, for the SOX2 promoter with high CG density, increased methylation in the CpG island upstream of the transcription start site played a key role in affecting high expression, but the positioning and clustering of nucleosomes also had an important influence. In conclusion, analysing the DNA methylation patterns, nucleosome positioning, and quantity distribution of different promoters can determine whether the PhiC31 integration site possesses the potential to further enhance expression or overcome transgene silencing effects by utilizing chromatin regulatory elements.

Multi-regional sequencing reveals the genetic and immune heterogeneity of non-cancerous tissues in gastric cancer

Gastric cancer (GC) is one of the most heterogeneous tumors. However, research on normal tissue adjacent to the tumor (NAT) is very limited. We performed multi-regional omics sequencing on 150 samples to assess the genetic basis and immune microenvironment in NAT and matched primary tumor or lymph node metastases. NATs demonstrated different mutated genes compared with GC, and NAT genomes underwent independent evolution with low variant allele frequency. Mutation profiles were predominated by aging and smoking-associated signatures in NAT instead of signatures associated with genetic instability. Although the immune microenvironment within NATs shows substantial intra-patient heterogeneity, the proportion of shared TCR clones among NATs is five times higher than that of tumor regions. These findings support the notion that subclonal expansion is not pronounced in NATs. We also demonstrated remarkable intra-patient heterogeneity of GCs and revealed heterogeneity of focal amplification of CD274 (encoding PD-L1) that leads to differential expression. Finally, we identified that monoclonal seeding is predominant in GC, which is followed by metastasis-to-metastasis dissemination in individual lymph nodes. These results provide novel insights into GC carcinogenesis. © 2024 The Pathological Society of Great Britain and Ireland.

PARP4 interacts with hnRNPM to regulate splicing during lung cancer progression

BACKGROUND

The identification of cancer driver genes from sequencing data has been crucial in deepening our understanding of tumor biology and expanding targeted therapy options. However, apart from the most commonly altered genes, the mechanisms underlying the contribution of other mutations to cancer acquisition remain understudied. Leveraging on our whole-exome sequencing of the largest Asian lung adenocarcinoma (LUAD) cohort (n = 302), we now functionally assess the mechanistic role of a novel driver, PARP4.

METHODS

In vitro and in vivo tumorigenicity assays were used to study the functional effects of PARP4 loss and mutation in multiple lung cancer cell lines. Interactomics analysis by quantitative mass spectrometry was conducted to identify PARP4's interaction partners. Transcriptomic data from cell lines and patient tumors were used to investigate splicing alterations.

RESULTS

PARP4 depletion or mutation (I1039T) promotes the tumorigenicity of KRAS- or EGFR-driven lung cancer cells. Disruption of the vault complex, with which PARP4 is commonly associated, did not alter tumorigenicity, indicating that PARP4's tumor suppressive activity is mediated independently. The splicing regulator hnRNPM is a potentially novel PARP4 interaction partner, the loss of which likewise promotes tumor formation. hnRNPM loss results in splicing perturbations, with a propensity for dysregulated intronic splicing that was similarly observed in PARP4 knockdown cells and in LUAD cohort patients with PARP4 copy number loss.

CONCLUSIONS

PARP4 is a novel modulator of lung adenocarcinoma, where its tumor suppressive activity is mediated not through the vault complex-unlike conventionally thought, but in association with its novel interaction partner hnRNPM, thus suggesting a role for splicing dysregulation in LUAD tumorigenesis.

The genetic duet of concurrent RASAL1 and PTEN alterations promotes cancer aggressiveness by cooperatively activating the PI3K-AKT pathway

The significance of the prominent tumor suppressor gene for RAS protein activator-like 1 (RASAL1) could be better understood by combined genetic, clinical, and functional studies. Here, we investigated the oncogenic and clinical impacts of genetic alterations of RASAL1, particularly when coexisting with genetic alterations of the gene for phosphatase and tensin homolog (PTEN), in 9924 cancers of 33 types in the TCGA database. We found common concurrent genetic alterations of the two genes, which were cooperatively associated with activation of the phosphatidylinositol 3-kinase (PI3K)-AKT pathway, with cancer progression and mortality rates being 46.36% and 31.72% with concurrent gene alterations, versus 29.80% and 16.93% with neither gene alteration (HR 1.64, 95% CI 1.46-1.84 and 1.77, 95% CI 1.53-2.05), respectively. This was enhanced by additional tumor protein p53 (TP53) gene alterations, with cancer progression and mortality rates being 47.65% and 34.46% with coexisting RASAL1, PTEN, and TP53 alterations versus 25.30% and 13.11% with no alteration (HR 2.21, 95% CI 1.92-2.56 and 2.76, 95% CI 2.31-3.30), respectively. In the case of breast cancer, this genetic trio was associated with a triple-negative risk of 68.75% versus 3.83% with no genetic alteration (RR 17.94, 95% CI 9.60-33.51), consistent with the aggressive nature of triple-negative breast cancer. Mice with double knockouts of Rasal1 and Pten displayed robust Pi3k pathway activation, with the development of metastasizing malignancies, while single gene knockout resulted in only benign neoplasma. These results suggest that RASAL1, like PTEN, is a critical player in negatively regulating the PI3K-AKT pathway; defect in RASAL1 causes RAS activation, thus initiating the PI3K-AKT pathway signaling, which cannot terminate with concurrent PTEN defects. Thus, the unique concurrent RASAL1 and PTEN defects drive oncogenesis and cancer aggressiveness by cooperatively activating the PI3K-AKT pathway. This represents a robust genetic mechanism to promote human cancer.

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.

Inverted triplications formed by iterative template switches generate structural variant diversity at genomic disorder loci

The duplication-triplication/inverted-duplication (DUP-TRP/INV-DUP) structure is a complex genomic rearrangement (CGR). Although it has been identified as an important pathogenic DNA mutation signature in genomic disorders and cancer genomes, its architecture remains unresolved. Here, we studied the genomic architecture of DUP-TRP/INV-DUP by investigating the DNA of 24 patients identified by array comparative genomic hybridization (aCGH) on whom we found evidence for the existence of 4 out of 4 predicted structural variant (SV) haplotypes. Using a combination of short-read genome sequencing (GS), long-read GS, optical genome mapping, and single-cell DNA template strand sequencing (strand-seq), the haplotype structure was resolved in 18 samples. The point of template switching in 4 samples was shown to be a segment of ∼2.2-5.5 kb of 100% nucleotide similarity within inverted repeat pairs. These data provide experimental evidence that inverted low-copy repeats act as recombinant substrates. This type of CGR can result in multiple conformers generating diverse SV haplotypes in susceptible dosage-sensitive loci.

Mutations in cancer-relevant genes are ubiquitous in histologically normal endometrial tissue

OBJECTIVE

Endometrial cancer (EndoCA) is the most common gynecologic cancer and incidence and mortality rate continue to increase. Despite well-characterized knowledge of EndoCA-defining mutations, no effective diagnostic or screening tests exist. To lay the foundation for testing development, our study focused on defining the prevalence of somatic mutations present in non-cancerous uterine tissue.

METHODS

We obtained ≥8 uterine samplings, including separate endometrial and myometrial layers, from each of 22 women undergoing hysterectomy for non-cancer conditions. We ultra-deep sequenced (>2000× coverage) samples using a 125 cancer-relevant gene panel.

RESULTS

All women harbored complex mutation patterns. In total, 308 somatic mutations were identified with mutant allele frequencies ranging up to 96.0%. These encompassed 56 unique mutations from 24 genes. The majority of samples possessed predicted functional cancer mutations but curiously no growth advantage over non-functional mutations was detected. Functional mutations were enriched with increasing patient age (p = 0.045) and BMI (p = 0.0007) and in endometrial versus myometrial layers (68% vs 39%, p = 0.0002). Finally, while the somatic mutation landscape shared similar mutation prevalence in key TCGA-defined EndoCA genes, notably PIK3CA, significant differences were identified, including NOTCH1 (77% vs 10%), PTEN (9% vs 61%), TP53 (0% vs 37%) and CTNNB1 (0% vs 26%).

CONCLUSIONS

An important caveat for future liquid biopsy/DNA-based cancer diagnostics is the repertoire of shared and distinct mutation profiles between histologically unremarkable and EndoCA tissues. The lack of selection pressure between functional and non-functional mutations in histologically unremarkable uterine tissue may offer a glimpse into an unrecognized EndoCA protective mechanism.

Genomic Newborn Screening for Pediatric Cancer Predisposition Syndromes: A Holistic Approach

As next-generation sequencing (NGS) has become more widely used, germline and rare genetic variations responsible for inherited illnesses, including cancer predisposition syndromes (CPSs) that account for up to 10% of childhood malignancies, have been found. The CPSs are a group of germline genetic disorders that have been identified as risk factors for pediatric cancer development. Excluding a few "classic" CPSs, there is no agreement regarding when and how to conduct germline genetic diagnostic studies in children with cancer due to the constant evolution of knowledge in NGS technologies. Various clinical screening tools have been suggested to aid in the identification of individuals who are at greater risk, using diverse strategies and with varied outcomes. We present here an overview of the primary clinical and molecular characteristics of various CPSs and summarize the existing clinical genomics data on the prevalence of CPSs in pediatric cancer patients. Additionally, we discuss several ethical issues, challenges, limitations, cost-effectiveness, and integration of genomic newborn screening for CPSs into a healthcare system. Furthermore, we assess the effectiveness of commonly utilized decision-support tools in identifying patients who may benefit from genetic counseling and/or direct genetic testing. This investigation highlights a tailored and systematic approach utilizing medical newborn screening tools such as the genome sequencing of high-risk newborns for CPSs, which could be a practical and cost-effective strategy in pediatric cancer care.

Circadian transcriptome of pancreatic adenocarcinoma unravels chronotherapeutic targets

Pancreatic ductal adenocarcinoma (PDA) is a lethal cancer characterized by a poor outcome and an increasing incidence. A significant majority (>80%) of newly diagnosed cases are deemed unresectable, leaving chemotherapy as the sole viable option, though with only moderate success. This necessitates the identification of improved therapeutic options for PDA. We hypothesized that there are temporal variations in cancer-relevant processes within PDA tumors, offering insights into the optimal timing of drug administration - a concept termed chronotherapy. In this study, we explored the presence of the circadian transcriptome in PDA using patient-derived organoids and validated these findings by comparing PDA data from The Cancer Genome Atlas with noncancerous healthy pancreas data from GTEx. Several PDA-associated pathways (cell cycle, stress response, Rho GTPase signaling) and cancer driver hub genes (EGFR and JUN) exhibited a cancer-specific rhythmic pattern intricately linked to the circadian clock. Through the integration of multiple functional measurements for rhythmic cancer driver genes, we identified top chronotherapy targets and validated key findings in molecularly divergent pancreatic cancer cell lines. Testing the chemotherapeutic efficacy of clinically relevant drugs further revealed temporal variations that correlated with drug-target cycling. Collectively, our study unravels the PDA circadian transcriptome and highlights a potential approach for optimizing chrono-chemotherapeutic efficacy.

Identifying therapeutic targets for cancer among 2074 circulating proteins and risk of nine cancers

Circulating proteins can reveal key pathways to cancer and identify therapeutic targets for cancer prevention. We investigate 2,074 circulating proteins and risk of nine common cancers (bladder, breast, endometrium, head and neck, lung, ovary, pancreas, kidney, and malignant non-melanoma) using cis protein Mendelian randomisation and colocalization. We conduct additional analyses to identify adverse side-effects of altering risk proteins and map cancer risk proteins to drug targets. Here we find 40 proteins associated with common cancers, such as PLAUR and risk of breast cancer [odds ratio per standard deviation increment: 2.27, 1.88-2.74], and with high-mortality cancers, such as CTRB1 and pancreatic cancer [0.79, 0.73-0.85]. We also identify potential adverse effects of protein-altering interventions to reduce cancer risk, such as hypertension. Additionally, we report 18 proteins associated with cancer risk that map to existing drugs and 15 that are not currently under clinical investigation. In sum, we identify protein-cancer links that improve our understanding of cancer aetiology. We also demonstrate that the wider consequence of any protein-altering intervention on well-being and morbidity is required to interpret any utility of proteins as potential future targets for therapeutic prevention.

Enhanced Detection of Novel Low-Frequency Gene Fusions via High-Yield Ligation and Multiplexed Enrichment Sequencing

Panel-based methods are commonly employed for the analysis of novel gene fusions in precision diagnostics and new drug development in cancer. However, these methods are constrained by limitations in ligation yield and the enrichment of novel gene fusions with low variant allele frequencies. In this study, we conducted a pioneering investigation into the stability of double-stranded adapter DNA, resulting in improved ligation yield and enhanced conversion efficiency. Additionally, we implemented blocker displacement amplification, achieving a remarkable 7-fold enrichment of novel gene fusions. Leveraging the pre-enrichment achieved with this approach, we successfully applied it to Nanopore sequencing, enabling ultra-fast analysis of novel gene fusions within one hour with high sensitivity. This method offers a robust and remarkably sensitive mean of analyzing novel gene fusions, promising the discovery of pivotal biomarkers that can significantly improve cancer diagnostics and the development of new therapeutic strategies.

Improving the pharmacokinetics, biodistribution and plasma stability of monobodies

Cancer is a leading cause of death worldwide. Several targeted anticancer drugs entered clinical practice and improved survival of cancer patients with selected tumor types, but therapy resistance and metastatic disease remains a challenge. A major class of targeted anticancer drugs are therapeutic antibodies, but their use is limited to extracellular targets. Hence, alternative binding scaffolds have been investigated for intracellular use and better tumor tissue penetration. Among those, monobodies are small synthetic protein binders that were engineered to bind with high affinity and selectivity to central intracellular oncoproteins and inhibit their signaling. Despite their use as basic research tools, the potential of monobodies as protein therapeutics remains to be explored. In particular, the pharmacological properties of monobodies, including plasma stability, toxicity and pharmacokinetics have not been investigated. Here, we show that monobodies have high plasma stability, are well-tolerated in mice, but have a short half-life in vivo due to rapid renal clearance. Therefore, we engineered monobody fusions with an albumin-binding domain (ABD), which showed enhanced pharmacological properties without affecting their target binding: We found that ABD-monobody fusions display increased stability in mouse plasma. Most importantly, ABD-monobodies have a dramatically prolonged in vivo half-life and are not rapidly excreted by renal clearance, remaining in the blood significantly longer, while not accumulating in specific internal organs. Our results demonstrate the promise and versatility of monobodies to be developed into future therapeutics for cancer treatment. We anticipate that monobodies may be able to extend the spectrum of intracellular targets, resulting in a significant benefit to patient outcome.

Targeting C21orf58 is a Novel Treatment Strategy of Hepatocellular Carcinoma by Disrupting the Formation of JAK2/C21orf58/STAT3 Complex

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Functionally uncharacterized genes are an attractive repository to explore candidate oncogenes. It is demonstrated that C21orf58 displays an oncogenic role in promoting cell growth, tumorigenesis and sorafenib resistance of HCC cells by abnormal activation of STAT3 signaling. Mechanistically, a novel manner to regulate STAT3 signaling that adaptor C21orf58 forms a ternary complex is reveal with N-terminal domain of STAT3 and SH2 domain of JAK2, by which C21orf58 overactivates wild-type STAT3 by facilitating its phosphorylation mediated by JAK2, and hyper-activates of constitutively mutated STAT3 due to preferred binding with C21orf58 and JAK2. Moreover, it is validated that inhibition of C21orf58 with drug alminoprofen, selected by virtual screening, could effectively repress the viability and tumorigenesis of HCC cells. Therefore, it is identified that C21orf58 functions as an oncogenic adaptor, reveal a novel regulatory mechanism of JAK2/STAT3 signaling, explain the cause of abnormal activity of activated mutants of STAT3, and explore the attractive therapeutic potential by targeting C21orf58 in HCC.

Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields

Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery of driver genes at the individual level is becoming more valuable but is a great challenge. Although there have been some computational methods proposed to tackle this challenge, few can cover all patient samples well, and there is still room for performance improvement. In this study, to identify individual-level driver genes more efficiently, we propose the PDGCN method. PDGCN integrates multiple types of data features, including mutation, expression, methylation, copy number data, and system-level gene features, along with network structural features extracted using Node2vec in order to construct a sample-gene interaction network. Prediction is performed using a graphical convolutional neural network model with a conditional random field layer, which is able to better combine the network structural features with biological attribute features. Experiments on the ACC (Adrenocortical Cancer) and KICH (Kidney Chromophobe) datasets from TCGA (The Cancer Genome Atlas) demonstrated that the method performs better compared to other similar methods. It can identify not only frequently mutated driver genes, but also rare candidate driver genes and novel biomarker genes. The results of the survival and enrichment analyses of these detected genes demonstrate that the method can identify important driver genes at the individual level.

Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact

BACKGROUND

Cancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine.

RESULTS

In the present work, we propose a method for identifying driver genes using a Generalized Linear Regression Model (GLM) with Shrinkage and double-Weighted strategies based on Functional Impact, which is named GSW-FI. Firstly, an estimating model is proposed for assessing the background functional impacts of genes based on GLM, utilizing gene features as predictors. Secondly, the shrinkage and double-weighted strategies as two revising approaches are integrated to ensure the rationality of the identified driver genes. Lastly, a statistical method of hypothesis testing is designed to identify driver genes by leveraging the estimated background function impacts. Experimental results conducted on 31 The Cancer Genome Altas datasets demonstrate that GSW-FI outperforms ten other prediction methods in terms of the overlap fraction with well-known databases and consensus predictions among different methods.

CONCLUSIONS

GSW-FI presents a novel approach that efficiently identifies driver genes with functional impact mutations using computational methods, thereby advancing the development of precision medicine for cancer.

Uncovering driver genes in breast cancer through an innovative machine learning mutational analysis method

Breast cancer has become a severe public health concern and one of the leading causes of cancer-related death in women worldwide. Several genes and mutations in these genes linked to breast cancer have been identified using sophisticated techniques, despite the fact that the exact cause of breast cancer is still unknown. A commonly used feature for identifying driver mutations is the recurrence of a mutation in patients. Nevertheless, some mutations are more likely to occur than others for various reasons. Sequencing analysis has shown that cancer-driving genes operate across complex networks, often with mutations appearing in a modular pattern. In this work, as a retrospective study, we used TCGA data, which is gathered from breast cancer patients. We introduced a new machine-learning approach to examine gene functionality in networks derived from mutation associations, gene-gene interactions, and graph clustering for breast cancer analysis. These networks have uncovered crucial biological components in critical pathways, particularly those that exhibit low-frequency mutations. The statistical strength of the clinical study is significantly boosted by evaluating the network as a whole instead of just single gene effects. Our method successfully identified essential driver genes with diverse mutation frequencies. We then explored the functions of these potential driver genes and their related pathways. By uncovering low-frequency genes, we shed light on understudied pathways associated with breast cancer. Additionally, we present a novel Monte Carlo-based algorithm to identify driver modules in breast cancer. Our findings highlight the significance and role of these modules in critical signaling pathways in breast cancer, providing a comprehensive understanding of breast cancer development. Materials and implementations are available at: [https://github.com/MahnazHabibi/BreastCancer].

Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer

Since trastuzumab was approved in 2012 for the first-line treatment of gastric cancer (GC), no significant advancement in GC targeted therapies has occurred. Synthetic lethality refers to the concept that simultaneous dysfunction of a pair of genes results in a lethal effect on cells, while the loss of an individual gene does not cause this effect. Through exploiting synthetic lethality, novel targeted therapies can be developed for the individualized treatment of GC. In this study, we proposed a computational strategy named Gastric cancer Specific Synthetic Lethality inference (GSSL) to identify synthetic lethal interactions in GC. GSSL analysis was used to infer probable synthetic lethality in GC using four accessible clinical datasets. In addition, prediction results were confirmed by experiments. GSSL analysis identified a total of 34 candidate synthetic lethal pairs, which included 33 unique targets. Among the synthetic lethal gene pairs, TP53-CHEK1 was selected for further experimental validation. Both computational and experimental results indicated that inhibiting CHEK1 could be a potential therapeutic strategy for GC patients with TP53 mutation. Meanwhile, in vitro experimental validation of two novel synthetic lethal pairs TP53-AURKB and ARID1A-EP300 further proved the universality and reliability of GSSL. Collectively, GSSL has been shown to be a reliable and feasible method for comprehensive analysis of inferring synthetic lethal interactions of GC, which may offer novel insight into the precision medicine and individualized treatment of GC.

XL1R-Net: Explainable AI-driven improved L1-regularized deep neural architecture for NSCLC biomarker identification

BACKGROUND AND OBJECTIVE

Non-small cell lung cancer (NSCLC) exhibits intrinsic molecular heterogeneity, primarily driven by the mutation of specific biomarkers. Identification of these biomarkers would assist not only in distinguishing NSCLC into its major subtypes - Adenocarcinoma and Squamous Cell Carcinoma, but also in developing targeted therapy. Medical practitioners use one or more types of omic data to identify these biomarkers, copy number variation (CNV) being one such type. CNV provides a measure of genomic instability, which is considered a hallmark of carcinoma. However, the CNV data has not received much attention for biomarker identification. This paper aims to identify biomarkers for NSCLC using CNV data.

METHODS

An eXplainable AI (XAI)-driven L1-regularized deep learning architecture, XL1R-Net, is proposed that introduces a novel modification of the standard L1-regularized gradient descent algorithm to arrive at an improved deep neural classifier for NSCLC subtyping. Further, XAI-based feature identification has been used to leverage the trained classifier to uncover a set of twenty NCSLC-relevant biomarkers.

RESULTS

The identified biomarkers are evaluated based on their classification performance and clinical relevance. Using Multilayer Perceptron (MLP)-based model, a classification accuracy of 84.95% using 10-fold cross-validation is achieved. Moreover, the statistical significance test on the classification performance also revealed the superiority of the MLP model over the competitive machine learning models. Further, the publicly available Drug-Gene Interaction Database reveals twelve of the identified biomarkers as potentially druggable. The K-M Plotter tool was used to verify eighteen of the identified biomarkers with a high probability of predicting NSCLC patients' likelihood of survival. While nine of the identified biomarkers confirm the recent literature, five find mention in the OncoKB Gene List.

CONCLUSION

A set of seven novel biomarkers that have not been reported in the literature could be investigated for their potential contribution towards NSCLC therapy. Given NSCLC's genetic diversity, using only one omics data type may not adequately capture the tumor's complexity. Multiomics data and its integration with other sources will be examined in the future to better understand NSCLC heterogeneity.

Recent drug design strategies and identification of key heterocyclic scaffolds for promising anticancer targets

Cancer, a noncommunicable disease, is the leading cause of mortality worldwide and is anticipated to rise by 75% in the next two decades, reaching approximately 25 million cases. Traditional cancer treatments, such as radiotherapy and surgery, have shown limited success in reducing cancer incidence. As a result, the focus of cancer chemotherapy has switched to the development of novel small molecule antitumor agents as an alternate strategy for combating and managing cancer rates. Heterocyclic compounds are such agents that bind to specific residues in target proteins, inhibiting their function and potentially providing cancer treatment. This review focuses on privileged heterocyclic pharmacophores with potent activity against carbonic anhydrases and kinases, which are important anticancer targets. Evaluation of ongoing pre-clinical and clinical research of heterocyclic compounds with potential therapeutic value against a variety of malignancies as well as the provision of a concise summary of the role of heterocyclic scaffolds in various chemotherapy protocols have also been discussed. The main objective of the article is to highlight key heterocyclic scaffolds involved in recent anticancer drug design that demands further attention from the drug development community to find more effective and safer targeted small-molecule anticancer agents.

The role of competing endogenous RNA network in the development of hepatocellular carcinoma: potential therapeutic targets

The current situation of hepatocellular carcinoma (HCC) management is challenging due to its high incidence, mortality, recurrence and metastasis. Recent advances in gene genetic and expression regulation have unveiled the significant role of non-coding RNA (ncRNA) in various cancers. This led to the formulation of the competing endogenous RNA (ceRNA) hypothesis, which posits that both coding RNA and ncRNA, containing miRNA response elements (MRE), can share the same miRNA sequence. This results in a competitive network between ncRNAs, such as lncRNA and mRNA, allowing them to regulate each other. Extensive research has highlighted the crucial role of the ceRNA network in HCC development, impacting various cellular processes including proliferation, metastasis, cell death, angiogenesis, tumor microenvironment, organismal immunity, and chemotherapy resistance. Additionally, the ceRNA network, mediated by lncRNA or circRNA, offers potential in early diagnosis and prevention of HCC. Consequently, ceRNAs are emerging as therapeutic targets for HCC. The complexity of these gene networks aligns with the multi-target approach of traditional Chinese medicine (TCM), presenting a novel perspective for TCM in combating HCC. Research is beginning to show that TCM compounds and prescriptions can affect HCC progression through the ceRNA network, inhibiting proliferation and metastasis, and inducing apoptosis. Currently, the lncRNAs TUG1, NEAT1, and CCAT1, along with their associated ceRNA networks, are among the most promising ncRNAs for HCC research. However, this field is still in its infancy, necessitating advanced technology and extensive basic research to fully understand the ceRNA network mechanisms of TCM in HCC treatment.

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation

BACKGROUND

Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes.

RESULTS

Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden.

CONCLUSIONS

LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver .

Integrating computational methods guided the discovery of phytochemicals as potential Pin1 inhibitors for cancer: pharmacophore modeling, molecular docking, MM-GBSA calculations and molecular dynamics studies

Pin1 is a pivotal player in interactions with a diverse array of phosphorylated proteins closely linked to critical processes such as carcinogenesis and tumor suppression. Its axial role in cancer initiation and progression, coupled with its overexpression and activation in various cancers render it a potential candidate for the development of targeted therapeutics. While several known Pin1 inhibitors possess favorable enzymatic profiles, their cellular efficacy often falls short. Consequently, the pursuit of novel Pin1 inhibitors has gained considerable attention in the field of medicinal chemistry. In this study, we employed the Phase tool from Schrödinger to construct a structure-based pharmacophore model. Subsequently, 449,008 natural products (NPs) from the SN3 database underwent screening to identify compounds sharing pharmacophoric features with the native ligand. This resulted in 650 compounds, which then underwent molecular docking and binding free energy calculations. Among them, SN0021307, SN0449787 and SN0079231 showed better docking scores with values of -9.891, -7.579 and -7.097 kcal/mol, respectively than the reference compound (-6.064 kcal/mol). Also, SN0021307, SN0449787 and SN0079231 exhibited lower free binding energies (-57.12, -49.81 and -46.05 kcal/mol, respectively) than the reference ligand (-37.75 kcal/mol). Based on these studies, SN0021307, SN0449787, and SN0079231 showed better binding affinity that the reference compound. Further the validation of these findings, molecular dynamics simulations confirmed the stability of the ligand-receptor complex for 100 ns with RMSD ranging from 0.6 to 1.8 Å. Based on these promising results, these three phytochemicals emerge as promising lead compounds warranting comprehensive biological screening in future investigations. These compounds hold great potential for further exploration regarding their efficacy and safety as Pin1 inhibitors, which could usher in new avenues for combating cancer.

Characterization of prevalent tyrosine kinase inhibitors and their challenges in glioblastoma treatment

Glioblastoma multiforme (GBM) is a highly aggressive malignant primary tumor in the central nervous system. Despite extensive efforts in radiotherapy, chemotherapy, and neurosurgery, there remains an inadequate level of improvement in treatment outcomes. The development of large-scale genomic and proteomic analysis suggests that GBMs are characterized by transcriptional heterogeneity, which is responsible for therapy resistance. Hence, knowledge about the genetic and epigenetic heterogeneity of GBM is crucial for developing effective treatments for this aggressive form of brain cancer. Tyrosine kinases (TKs) can act as signal transducers, regulate important cellular processes like differentiation, proliferation, apoptosis and metabolism. Therefore, TK inhibitors (TKIs) have been developed to specifically target these kinases. TKIs are categorized into allosteric and non-allosteric inhibitors. Irreversible inhibitors form covalent bonds, which can lead to longer-lasting effects. However, this can also increase the risk of off-target effects and toxicity. The development of TKIs as therapeutics through computer-aided drug design (CADD) and bioinformatic techniques enhance the potential to improve patients' survival rates. Therefore, the continued exploration of TKIs as drug targets is expected to lead to even more effective and specific therapeutics in the future.

Evolutionary mode and timing of dissemination of high-grade serous carcinomas

Dissemination within the peritoneal cavity is a main determinant of poor patient outcomes from high-grade serous carcinomas (HGSCs). The dissemination process is poorly understood from a cancer evolutionary perspective. We reconstructed the evolutionary trajectories across a median of 5 tumor sites and regions from each of 23 patients based on deep whole-exome sequencing. Polyclonal cancer origin was detected in 1 patient. Ovarian tumors had more complex subclonal architectures than other intraperitoneal tumors in each patient, which indicated that tumors developed earlier in the ovaries. Three common modes of dissemination were identified, including monoclonal or polyclonal dissemination of monophyletic (linear) or polyphyletic (branched) subclones. Mutation profiles of initial or disseminated clones varied greatly among cancers, but recurrent mutations were found in 7 cancer-critical genes, including TP53, BRCA1, BRCA2, and DNMT3A, and in the PI3K/AKT1 pathway. Disseminated clones developed late in the evolutionary trajectory models of most cancers, in particular in cancers with DNA damage repair deficiency. Polyclonal dissemination was predicted to occur predominantly as a single and rapid wave, but chemotherapy exposure was associated with higher genomic diversity of disseminated clones. In conclusion, we described three common evolutionary dissemination modes across HGSCs and proposed factors associated with dissemination diversity.

Integrated analysis of FHIT gene alterations in cancer

The Fragile Histidine Triad Diadenosine Triphosphatase (FHIT) gene is located in the Common Fragile Site FRA3B and encodes an enzyme that hydrolyzes the dinucleotide Ap3A. Although FHIT loss is one of the most frequent copy number alterations in cancer, its relevance for cancer initiation and progression remains unclear. FHIT is frequently lost in cancers from the digestive tract, which is compatible with being a cancer driver event in these tissues. However, FHIT loss could also be a passenger event due to the inherent fragility of the FRA3B locus. Moreover, the physiological relevance of FHIT enzymatic activity and the levels of Ap3A is largely unclear. We have conducted here a systematic pan-cancer analysis of FHIT status in connection with other mutations and phenotypic alterations, and we have critically discussed our findings in connection with the literature to provide an overall view of FHIT implications in cancer.

Somatostatin and Somatostatin Receptors in Tumour Biology

Somatostatin (SST), a growth hormone inhibitory peptide, is expressed in endocrine and non-endocrine tissues, immune cells and the central nervous system (CNS). Post-release from secretory or immune cells, the first most appreciated role that SST exhibits is the antiproliferative effect in target tissue that served as a potential therapeutic intervention in various tumours of different origins. The SST-mediated in vivo and/or in vitro antiproliferative effect in the tumour is considered direct via activation of five different somatostatin receptor subtypes (SSTR1-5), which are well expressed in most tumours and often more than one receptor in a single cell. Second, the indirect effect is associated with the regulation of growth factors. SSTR subtypes are crucial in tumour diagnosis and prognosis. In this review, with the recent development of new SST analogues and receptor-specific agonists with emerging functional consequences of signaling pathways are promising therapeutic avenues in tumours of different origins that are discussed.

Systematic analysis of Mendelian disease-associated gene variants reveals new classes of cancer-predisposing genes

BACKGROUND

Despite the acceleration of somatic driver gene discovery facilitated by recent large-scale tumor sequencing data, the contribution of inherited variants remains largely unexplored, primarily focusing on previously known cancer predisposition genes (CPGs) due to the low statistical power associated with detecting rare pathogenic variant-phenotype associations.

METHODS

Here, we introduce a generalized log-regression model to measure the excess of pathogenic variants within genes in cancer patients compared to control samples. It aims to measure gene-level cancer risk enrichment by collapsing rare pathogenic variants after controlling the population differences across samples.

RESULTS

In this study, we investigate whether pathogenic variants in Mendelian disease-associated genes (OMIM genes) are enriched in cancer patients compared to controls. Utilizing data from PCAWG and the 1,000 Genomes Project, we identify 103 OMIM genes demonstrating significant enrichment of pathogenic variants in cancer samples (FDR 20%). Through an integrative approach considering three distinct properties, we classify these CPG-like OMIM genes into four clusters, indicating potential diverse mechanisms underlying tumor progression. Further, we explore the function of PAH (a key metabolic enzyme associated with Phenylketonuria), the gene exhibiting the highest prevalence of pathogenic variants in a pan-cancer (1.8%) compared to controls (0.6%).

CONCLUSIONS

Our findings suggest a possible cancer progression mechanism through metabolic profile alterations. Overall, our data indicates that pathogenic OMIM gene variants contribute to cancer progression and introduces new CPG classifications potentially underpinning diverse tumorigenesis mechanisms.

A machine learning and directed network optimization approach to uncover TP53 regulatory patterns

TP53, the Guardian of the Genome, is the most frequently mutated gene in human cancers and the functional characterization of its regulation is fundamental. To address this we employ two strategies: machine learning to predict the mutation status of TP53from transcriptomic data, and directed regulatory networks to reconstruct the effect of mutations on the transcipt levels of TP53 targets. Using data from established databases (Cancer Cell Line Encyclopedia, The Cancer Genome Atlas), machine learning could predict the mutation status, but not resolve different mutations. On the contrary, directed network optimization allowed to infer the TP53 regulatory profile across: (1) mutations, (2) irradiation in lung cancer, and (3) hypoxia in breast cancer, and we could observe differential regulatory profiles dictated by (1) mutation type, (2) deleterious consequences of the mutation, (3) known hotspots, (4) protein changes, (5) stress condition (irradiation/hypoxia). This is an important first step toward using regulatory networks for the characterization of the functional consequences of mutations, and could be extended to other perturbations, with implications for drug design and precision medicine.

PRO-seq: Precise Mapping of Engaged RNA Pol II at Single-Nucleotide Resolution

Gene regulation is dependent on the production of mRNAs and a repertoire of non-coding RNAs by RNA polymerase II (RNAPII). Precision run-on sequencing (PRO-seq) maps the position of engaged RNAPII complexes at single-nucleotide resolution and can reveal direct targets of regulation, locations of enhancers, and transcription mechanisms that are difficult or impossible to measure by analysis of total cellular RNA. Briefly, this method first involves permeabilizing cells with mild detergents to remove intracellular NTPs and halt transcription. Transcription is then resumed in the presence of biotin-NTPs and sarkosyl to allow transcriptional incorporation of a single biotinylated NTP by RNAPII. The biotin moiety is then bound to streptavidin beads to stringently enrich for nascent RNAs. Sequencing libraries are then generated such that the first base read corresponds to the 3' end of the nascent transcript. Here, we describe our current protocol for generating PRO-seq libraries from metazoan cells, including adaptations of previously published protocols to incorporate unique molecular identifiers, reduce ligation bias, and improve library yields. Additional commentary describes quality control and processing of PRO-seq data and references for more advanced downstream analysis such as gene and enhancer identification. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Cell permeabilization for PRO-seq Basic Protocol 2: Construction of PRO-seq libraries Support Protocol: Adenylation of 3' adapter.

In Vitro Prostate Cancer Treatment via CRISPR-Cas9 Gene Editing Facilitated by Polyethyleneimine-Derived Graphene Quantum Dots

CRISPR-Cas9 is a programmable gene editing tool with a promising potential for cancer gene therapy. This therapeutic function is enabled in the present work via the non-covalent delivery of CRISPR ribonucleic protein (RNP) by cationic glucosamine/PEI-derived graphene quantum dots (PEI-GQD) that aid in overcoming physiological barriers and tracking genes of interest. PEI-GQD/RNP complex targeting the TP53 mutation overexpressed in ~50% of cancers successfully produces its double-stranded breaks in solution and in PC3 prostate cancer cells. Restoring this cancer "suicide" gene can promote cellular repair pathways and lead to cancer cell apoptosis. Its repair to the healthy form performed by simultaneous PEI-GQD delivery of CRISPR RNP and a gene repair template leads to a successful therapeutic outcome: 40% apoptotic cancer cell death, while having no effect on non-cancerous HeK293 cells. The translocation of PEI-GQD/RNP complex into PC3 cell cytoplasm is tracked via GQD intrinsic fluorescence, while EGFP-tagged RNP is detected in the cell nucleus, showing the successful detachment of the gene editing tool upon internalization. Using GQDs as non-viral delivery and imaging agents for CRISPR-Cas9 RNP sets the stage for image-guided cancer-specific gene therapy.

Accurate identification of structural variations from cancer samples

Structural variations (SVs) are commonly found in cancer genomes. They can cause gene amplification, deletion and fusion, among other functional consequences. With an average read length of hundreds of kilobases, nano-channel-based optical DNA mapping is powerful in detecting large SVs. However, existing SV calling methods are not tailored for cancer samples, which have special properties such as mixed cell types and sub-clones. Here we propose the Cancer Optical Mapping for detecting Structural Variations (COMSV) method that is specifically designed for cancer samples. It shows high sensitivity and specificity in benchmark comparisons. Applying to cancer cell lines and patient samples, COMSV identifies hundreds of novel SVs per sample.

Genomic profiling and pre-clinical modelling of breast cancer leptomeningeal metastasis reveals acquisition of a lobular-like phenotype

Breast cancer leptomeningeal metastasis (BCLM), where tumour cells grow along the lining of the brain and spinal cord, is a devastating development for patients. Investigating this metastatic site is hampered by difficulty in accessing tumour material. Here, we utilise cerebrospinal fluid (CSF) cell-free DNA (cfDNA) and CSF disseminated tumour cells (DTCs) to explore the clonal evolution of BCLM and heterogeneity between leptomeningeal and extracranial metastatic sites. Somatic alterations with potential therapeutic actionability were detected in 81% (17/21) of BCLM cases, with 19% detectable in CSF cfDNA only. BCLM was enriched in genomic aberrations in adherens junction and cytoskeletal genes, revealing a lobular-like breast cancer phenotype. CSF DTCs were cultured in 3D to establish BCLM patient-derived organoids, and used for the successful generation of BCLM in vivo models. These data reveal that BCLM possess a unique genomic aberration profile and highlight potential cellular dependencies in this hard-to-treat form of metastatic disease.

Mutational processes of tobacco smoking and APOBEC activity generate protein-truncating mutations in cancer genomes

Mutational signatures represent a genomic footprint of endogenous and exogenous mutational processes through tumor evolution. However, their functional impact on the proteome remains incompletely understood. We analyzed the protein-coding impact of single-base substitution (SBS) signatures in 12,341 cancer genomes from 18 cancer types. Stop-gain mutations (SGMs) (i.e., nonsense mutations) were strongly enriched in SBS signatures of tobacco smoking, APOBEC cytidine deaminases, and reactive oxygen species. These mutational processes alter specific trinucleotide contexts and thereby substitute serines and glutamic acids with stop codons. SGMs frequently affect cancer hallmark pathways and tumor suppressors such as TP53, FAT1, and APC. Tobacco-driven SGMs in lung cancer correlate with smoking history and highlight a preventable determinant of these harmful mutations. APOBEC-driven SGMs are enriched in YTCA motifs and associate with APOBEC3A expression. Our study exposes SGM expansion as a genetic mechanism by which endogenous and carcinogenic mutational processes directly contribute to protein loss of function, oncogenesis, and tumor heterogeneity.

Noncoding RNA mutations in cancer

Cancer is driven by both germline and somatic genetic changes. Efforts have been devoted to characterizing essential genetic variations in cancer initiation and development. Most attention has been given to mutations in protein-coding genes and associated regulatory elements such as promoters and enhancers. The development of sequencing technologies and in silico and experimental methods has allowed further exploration of cancer predisposition variants and important somatic mutations in noncoding RNAs, mainly for long noncoding RNAs and microRNAs. Association studies including GWAS have revealed hereditary variations including SNPs and indels in lncRNA or miRNA genes and regulatory regions. These mutations altered RNA secondary structures, expression levels, and target recognition and then conferred cancer predisposition to carriers. Whole-exome/genome sequencing comparing cancer and normal tissues has revealed important somatic mutations in noncoding RNA genes. Mutation hotspots and somatic copy number alterations have been identified in various tumor-associated noncoding RNAs. Increasing focus and effort have been devoted to studying the noncoding region of the genome. The complex genetic network of cancer initiation is being unveiled. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Inverse correlation between Alzheimer's disease and cancer from the perspective of hypoxia

Sporadic Alzheimer's disease and cancer remain epidemiologically inversely related, and exploring the reverse pathogenesis is important for our understanding of both. Cognitive dysfunctions in Alzheimer's disease (AD) might result from the depletion of adaptive reserves in the brain. Energy storage in the brain is limited and is dynamically regulated by neurovascular and neurometabolic coupling. The research on neurodegenerative diseases has been dominated by the neurocentric view that neuronal defects cause the diseases. However, the proposal of the 2-hit vascular hypothesis in AD led us to focus on alterations in the vasculature, especially hypoperfusion. Chronic hypoxia is a feature shared by AD and cancer. It is interesting how contradicting chronic hypoxia's effects on both cancer and AD are. In this article, we discuss the potential links between the 2 diseases' etiology, from comparable upstream circumstances to diametrically opposed downstream effects. We suggest opposing potential mechanisms, including upregulation and downregulation of hypoxia-inducible factor-1α, the Warburg and reverse-Warburg effects, lactate-mediated intracellular acidic and alkaline conditions, and VDAC1-mediated apoptosis and antiapoptosis, and search for regulators that may be identified as the crossroads between cancer and AD.

A Mouse-Specific Model to Detect Genes under Selection in Tumors

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.

Discovery of Novel Fedratinib-Based HDAC/JAK/BRD4 Triple Inhibitors with Remarkable Antitumor Activity against Triple Negative Breast Cancer

Multitarget HDAC inhibitors capable of simultaneously blocking the BRD4-LIFR-JAK1-STAT3 signaling pathway hold great potential for the treatment of TNBC and other solid tumors. Herein, novel Fedratinib-based multitarget HDAC inhibitors were rationally designed, synthesized, and biologically evaluated, among which compound 25ap stood out as a potent HDAC/JAK/BRD4 triple inhibitor. Satisfyingly, compound 25ap led to concurrent inhibition of HDACs and the BRD4-LIFR-JAK1-STAT3 signaling pathway, which was validated by hyper-acetylation of histone and α-tubulin, hypo-phosphorylation of STAT3, downregulation of LIFR, MCL-1, and c-Myc in MDA-MB-231 cells. The multitarget effects of 25ap contributed to its robust antitumor response, including potent antiproliferative activity, remarkable apoptosis-inducing activity, and inhibition of colony formation. Notably, 25ap possessed an acceptable therapeutic window between normal and cancerous cells, desirable in vitro metabolic stability in mouse microsome, and sufficient in vivo exposure via intraperitoneal administration. Additionally, the in vivo antitumor potency of 25ap was demonstrated in an MDA-MB-231 xenograft model.

Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets

Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using a newly established multicenter breast cancer radio-multiomic dataset (n = 1474) encompassing radiomic features extracted from dynamic contrast-enhanced magnetic resonance images, we formulated a noninvasive radiomics methodology to effectively investigate ITH. Imaging ITH (IITH) was associated with genomic and pathological ITH, predicting poor prognosis independently in breast cancer. Through multiomic analysis, we identified activated oncogenic pathways and metabolic dysregulation in high-IITH tumors. Integrated metabolomic and transcriptomic analyses highlighted ferroptosis as a vulnerability and potential therapeutic target of high-IITH tumors. Collectively, this work emphasizes the superiority of radiomics in capturing ITH. Furthermore, we provide insights into the biological basis of IITH and propose therapeutic targets for breast cancers with elevated IITH.

RNA-Based Strategies for Cancer Therapy: In Silico Design and Evaluation of ASOs for Targeted Exon Skipping

Precision medicine in oncology has made significant progress in recent years by approving drugs that target specific genetic mutations. However, many cancer driver genes remain challenging to pharmacologically target ("undruggable"). To tackle this issue, RNA-based methods like antisense oligonucleotides (ASOs) that induce targeted exon skipping (ES) could provide a promising alternative. In this work, a comprehensive computational procedure is presented, focused on the development of ES-based cancer treatments. The procedure aims to produce specific protein variants, including inactive oncogenes and partially restored tumor suppressors. This novel computational procedure encompasses target-exon selection, in silico prediction of ES products, and identification of the best candidate ASOs for further experimental validation. The method was effectively employed on extensively mutated cancer genes, prioritized according to their suitability for ES-based interventions. Notable genes, such as NRAS and VHL, exhibited potential for this therapeutic approach, as specific target exons were identified and optimal ASO sequences were devised to induce their skipping. To the best of our knowledge, this is the first computational procedure that encompasses all necessary steps for designing ASO sequences tailored for targeted ES, contributing with a versatile and innovative approach to addressing the challenges posed by undruggable cancer driver genes and beyond.

Break-induced replication underlies formation of inverted triplications and generates unexpected diversity in haplotype structures

Background

The duplication-triplication/inverted-duplication (DUP-TRP/INV-DUP) structure is a type of complex genomic rearrangement (CGR) hypothesized to result from replicative repair of DNA due to replication fork collapse. It is often mediated by a pair of inverted low-copy repeats (LCR) followed by iterative template switches resulting in at least two breakpoint junctions in cis . Although it has been identified as an important mutation signature of pathogenicity for genomic disorders and cancer genomes, its architecture remains unresolved and is predicted to display at least four structural variation (SV) haplotypes.

Results

Here we studied the genomic architecture of DUP-TRP/INV-DUP by investigating the genomic DNA of 24 patients with neurodevelopmental disorders identified by array comparative genomic hybridization (aCGH) on whom we found evidence for the existence of 4 out of 4 predicted SV haplotypes. Using a combination of short-read genome sequencing (GS), long- read GS, optical genome mapping and StrandSeq the haplotype structure was resolved in 18 samples. This approach refined the point of template switching between inverted LCRs in 4 samples revealing a DNA segment of ∼2.2-5.5 kb of 100% nucleotide similarity. A prediction model was developed to infer the LCR used to mediate the non-allelic homology repair.

Conclusions

These data provide experimental evidence supporting the hypothesis that inverted LCRs act as a recombinant substrate in replication-based repair mechanisms. Such inverted repeats are particularly relevant for formation of copy-number associated inversions, including the DUP-TRP/INV-DUP structures. Moreover, this type of CGR can result in multiple conformers which contributes to generate diverse SV haplotypes in susceptible loci .

A Graph Convolution Network-Based Model for Prioritizing Personalized Cancer Driver Genes of Individual Patients

Cancer driver genes are mutated genes that play a key role in the growth of cancer cells. Accurately identifying the cancer driver genes helps us understand cancer's pathogenesis and develop effective treatment strategies. However, cancers are highly heterogeneous diseases; patients with the same cancer type may have different genomic characteristics and clinical symptoms. Hence, it is urgent to devise effective methods to identify personalized cancer driver genes of individual patients to help determine whether a patient can be treated with a certain targeted drug. This work presents a method for predicting personalized cancer Driver genes of individual patients based on Graph Convolution Networks and Neighbor Interactions called NIGCNDriver. NIGCNDriver first constructs a gene-sample association matrix using the associations between a sample and its known driver genes. Then, it employs graph convolution models on the gene-sample network to aggregate neighbor node features, and themself features, and then combines with the element-wise level interactions between neighbors to learn new feature representations for the samples and gene nodes. Finally, a linear correlation coefficient decoder is used to reconstruct the association between the sample and the mutant gene, enabling the prediction of a personalized driver gene for the individual sample. We applied the NIGCNDriver method to predict cancer driver genes for individual samples in the TCGA and cancer cell line datasets. The results show that our method outperforms the baseline methods in cancer driver gene prediction for individual samples.

Intratumor heterogeneity in colorectal cancer: Distribution of tumor suppressor gene variants with regard to patient lymph node status

Intratumor heterogeneity (ITH) results from accumulation of somatic mutations in the fractions of successive cancer cell generations. We aimed to use deep sequencing to investigate ITH in colorectal tumors with particular emphasis on variants in oncogenes (ONC) and tumor suppressor genes (TSG). Samples were collected from 16 patients with colorectal cancer and negative or positive lymph node status (n = 8 each). We deep-sequenced a panel of 56 cancer-related genes in the central and peripheral locations of T3 size primary tumors and healthy mucosa. The central region of T3 tumors has a different frequency profile and composition of genetic variants. This mutation profile is capable of independently discriminating patients with different lymph node status (p = 0.028) in the central region. We noted an increasing number of mutations outside of the central region of the tumor and a higher number of mutations in tumors from node-positive patients. Unexpectedly, in the healthy mucosa, we identified somatic mutations with variant allele frequencies, characteristic not only of heterozygotes and homozygotes but also of other discrete peaks (e.g., around 10%, 20%), suggestive of clonal expansion of certain mutant alleles. We found differences in the distribution of variant allele frequencies in TSGs when comparing node-negative and node-positive tumors (p = 0.029), as well as central and peripheral regions (p = 0.00399). TSGs may play an important role in the escape of the tumor toward metastatic colonization.

A computational framework for the inference of protein complex remodeling from whole-proteome measurements

Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets.

Recapitulation of patient-specific 3D chromatin conformation using machine learning

Regulatory networks containing enhancer-gene edges define cellular states. Multiple efforts have revealed these networks for reference tissues and cell lines by integrating multi-omics data. However, the methods developed cannot be applied for large patient cohorts due to the infeasibility of chromatin immunoprecipitation sequencing (ChIP-seq) for limited biopsy material. We trained machine-learning models using chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) and high-throughput chromosome conformation capture combined with chromatin immunoprecipitation (HiChIP) data that can predict connections using only assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA-seq data as input, which can be generated from biopsies. Our method overcomes limitations of correlation-based approaches that cannot distinguish between distinct target genes of given enhancers or between active vs. poised states in different samples, a hallmark of network rewiring in cancer. Application of our model on 371 samples across 22 cancer types revealed 1,780 enhancer-gene connections for 602 cancer genes. Using CRISPR interference (CRISPRi), we validated enhancers predicted to regulate ESR1 in estrogen receptor (ER)+ breast cancer and A1CF in liver hepatocellular carcinoma.

Convergent TP53 loss and evolvability in cancer

Cancer cell populations evolve by a stepwise process involving natural selection of the fittest variants within a tissue ecosystem context and as modified by therapy. Genomic scrutiny of patient samples reveals an extraordinary diversity of mutational profiles both between patients with similar cancers and within the cancer cell population of individual patients. Does this signify highly divergent evolutionary trajectories or are there repetitive and predictable patterns?Major evolutionary innovations or adaptations in different species are frequently repeated, or convergent, reflecting both common selective pressures and constraints on optimal solutions. We argue this is true of evolving cancer cells, especially with respect to the TP53 gene. Functional loss variants in TP53 are the most common genetic change in cancer. We discuss the likely microenvironmental selective pressures involved and the profound impact this has on cell fitness, evolvability and probability of subsequent drug resistance.

Pathogenic mutation hotspots in protein kinase domain structure

Control of eukaryotic cellular function is heavily reliant on the phosphorylation of proteins at specific amino acid residues, such as serine, threonine, tyrosine, and histidine. Protein kinases that are responsible for this process comprise one of the largest families of evolutionarily related proteins. Dysregulation of protein kinase signaling pathways is a frequent cause of a large variety of human diseases including cancer, autoimmune, neurodegenerative, and cardiovascular disorders. In this study, we mapped all pathogenic mutations in 497 human protein kinase domains from the ClinVar database to the reference structure of Aurora kinase A (AURKA) and grouped them by the relevance to the disease type. Our study revealed that the majority of mutation hotspots associated with cancer are situated within the catalytic and activation loops of the kinase domain, whereas non-cancer-related hotspots tend to be located outside of these regions. Additionally, we identified a hotspot at residue R371 of the AURKA structure that has the highest number of exclusively non-cancer-related pathogenic mutations (21) and has not been previously discussed.

In-depth understanding of higher-order genome architecture in orphan cancer

The human genome is intertwined, folded, condensed, and gradually constitutes the 3D architecture, thereby affecting transcription and widely involving in tumorigenesis. Incidence and mortality rates for orphan cancers increase due to poor early diagnosis and lack of effective medical treatments, which are now getting attention. In-depth understanding in tumorigenesis has fast-tracked over the last decade, however, the further role and mechanism of 3D genome organization in variant orphan tumorigenesis remains to be fully understood. We summarize for the first time that higher-order genome organization can provide novel insights into the occurrence mechanisms of orphan cancers, and discuss probable future research directions for drug development and anti-tumor therapies.

Genome-guided discovery of cancer therapeutic targets

The success of precision oncology-which aims to match the right therapies to the right patients based on molecular status-is predicated on a robust pipeline of molecular targets against which therapies can be developed. Recent advances in genomics and functional genetics have enabled the unbiased discovery of novel molecular targets at scale. We summarize the promise and challenges in integrating genomic and functional genetic landscapes of cancer to establish the next generation of cancer targets.

Cross-species oncogenomics offers insight into human muscle-invasive bladder cancer

BACKGROUND

In humans, muscle-invasive bladder cancer (MIBC) is highly aggressive and associated with a poor prognosis. With a high mutation load and large number of altered genes, strategies to delineate key driver events are necessary. Dogs and cats develop urothelial carcinoma (UC) with histological and clinical similarities to human MIBC. Cattle that graze on bracken fern also develop UC, associated with exposure to the carcinogen ptaquiloside. These species may represent relevant animal models of spontaneous and carcinogen-induced UC that can provide insight into human MIBC.

RESULTS

Whole-exome sequencing of domestic canine (n = 87) and feline (n = 23) UC, and comparative analysis with human MIBC reveals a lower mutation rate in animal cases and the absence of APOBEC mutational signatures. A convergence of driver genes (ARID1A, KDM6A, TP53, FAT1, and NRAS) is discovered, along with common focally amplified and deleted genes involved in regulation of the cell cycle and chromatin remodelling. We identify mismatch repair deficiency in a subset of canine and feline UCs with biallelic inactivation of MSH2. Bovine UC (n = 8) is distinctly different; we identify novel mutational signatures which are recapitulated in vitro in human urinary bladder UC cells treated with bracken fern extracts or purified ptaquiloside.

CONCLUSION

Canine and feline urinary bladder UC represent relevant models of MIBC in humans, and cross-species analysis can identify evolutionarily conserved driver genes. We characterize mutational signatures in bovine UC associated with bracken fern and ptaquiloside exposure, a human-linked cancer exposure. Our work demonstrates the relevance of cross-species comparative analysis in understanding both human and animal UC.