Cancer driver mutations: predictions and reality

Advances in nano drug delivery systems for enhanced efficacy of emodin in cancer therapy

Cancer remains one of the leading causes of death worldwide, highlighting the urgent need for novel antitumor drugs. Natural products have long been a crucial source of anticancer agents. Among these, emodin (EMO), a multifunctional anthraquinone compound, exhibits significant anticancer effects but is hindered in clinical applications by challenges such as low solubility, rapid metabolism, poor bioavailability, and off-target toxicity. Nano drug delivery systems offer effective strategies to overcome these limitations by enhancing the solubility, stability, bioavailability, and targeting ability of EMO. While substantial progress has been made in developing EMO-loaded nanoformulations, a comprehensive review on this topic is still lacking. This paper aims to fill this gap by providing an overview of recent advancements in nanocarriers for EMO delivery and their anticancer applications. These carriers include liposomes, nanoparticles, polymeric micelles, nanogels, and others, with nanoparticle-based formulations being the most extensively explored. Nanoformulations encapsulating EMO have demonstrated promising therapeutic results against various cancers, particularly breast cancer, followed by liver and lung cancers. We systematically summarize the preparation methods, materials, and physicochemical properties of EMO-loaded nanopreparations, underscoring key findings on how nanotechnology improves the anticancer efficacy of EMO. This review provides valuable insights for researchers engaged in developing nano delivery systems for anticancer drugs.

Identification of the clinical value and biological effects of TTN mutation in liver cancer

Liver cancer, a malignant tumor of the digestive system, is a leading cause of cancer‑related mortality globally. Numerous genetic mutations associated with tumorigenesis have been identified, stemming from genomic instability. However, the clinical implications and therapeutic relevance of these mutations remain poorly understood. The present study evaluated the prognostic significance of titin (TTN) mutations in liver cancer by analyzing the mutation landscape of liver cancer tissues from The Cancer Genome Atlas (TCGA) database. The association between TTN mutations and drug susceptibility was subsequently examined using the OncoPredict algorithm and Cell Counting Kit‑8 (CCK‑8) assays. Furthermore, the impact of TTN mutations on hepatoma cell biology both in vivo and in vitro were assessed by reverse transcription‑quantitative PCR, protein stability assays, colony formation assays, tumor spheroid formation assays and subcutaneous tumor transplantation in BALB/c nude mice. Genetic analysis of the TCGA database revealed that TTN mutations are among the most frequent mutations in liver cancer. Patients with TTN mutations exhibited worse prognoses compared with those with the wild‑type allele. The OncoPredict algorithm and CCK‑8 assays revealed that TTN mutations are associated with altered drug sensitivity, particularly to GSK1904529A, nilotinib, 5‑fluorouracil (5‑FU) and sapitinib. Additionally, TTN mutations were shown to enhance TTN protein stability, decrease intracellular ferrous ion levels and significantly decrease liver cancer sensitivity to 5‑FU both in vitro and in vivo. The findings indicated that TTN mutations increase protein stability and lower intracellular ferrous ion levels, thereby suppressing ferroptosis and contributing to resistance to 5‑FU in hepatoma cells. These results suggest that TTN mutations are associated with poor prognosis in liver cancer and could serve as a predictive biomarker for liver cancer progression, prognosis and drug resistance.

Genomics in Cancer of Unknown Primary: Utility in Modern Clinical Practice

Cancer of unknown primary (CUP) is defined as metastatic cancer where the primary tumour responsible for metastatic spread cannot be identified despite thorough diagnostics. It has a very poor prognosis, is rapidly progressive, and has limited treatment options beyond empirical chemotherapy. Modern genomic advances play a role in identifying the primary tissue of origin (TOO) and in allowing molecular targeted therapies and immunotherapies to be used in the treatment of CUP patients. Whole genome and whole transcriptome sequencing produce vast amounts of data, and predictive algorithms and artificial intelligence can be used to make this data clinically actionable. Recent trials have shown that using genomic data in clinical decision-making improves outcomes for CUP patients. Liquid biopsies are an exciting development that allow for repeated genomic analysis throughout treatment or when tissue is difficult to obtain. Genomics should be used routinely in the care of CUP patients, at diagnosis and to aid treatment decisions.

Laminin-γ2-NR6A1 fusion protein promotes metastatic potential in non-small lung carcinoma cells without EGFR mutation

Laminin-γ2 fusion gene (Lm-γ2F), formed by translocation between LAMC2 and NR6A1, functions as an epidermal growth factor receptor (EGFR) ligand. However, its expression and impact on cancers beyond the initially studied contexts remain unclear. This study focused on Lm-γ2F protein secretion and its role in non-small cell lung carcinoma (NSCLC), where EGFR signaling plays a pivotal role in malignancy progression. Lm-γ2F secretion was confirmed in serum-free conditioned medium from six NSCLC cell lines by western blotting and further validated in NCI-H1650 cells. Hypothesizing that Lm-γ2F functions as an EGFR ligand, its effects in NSCLC cells lacking EGFR mutations were explored. In EKVX and RERF-LC-KJ cell lines, Lm-γ2F overexpression significantly enhanced cell growth, survival, motility, and invasiveness through EGFR signaling activation compared with controls. Conversely, no effects were observed in VMRC-LCD cells lacking EGFR expression. Additionally, increased MT1-MMP expression was detected in Lm-γ2F-expressing EKVX cells. In vivo, these cells exhibited elevated metastatic activity in a lung metastasis model. These findings suggested that ectopic Lm-γ2F expression contributes to malignant progression in NSCLC cells without EGFR mutations. Furthermore, EGFR tyrosine kinase inhibitors may suppress metastasis in these contexts. This study provides novel insights into the oncogenic role of Lm-γ2F in NSCLC, highlighting its potential as a therapeutic target to mitigate tumor progression and metastasis.

Systemic inflammation in response to radiation drives the genesis of an immunosuppressed tumor microenvironment

The composition of the tumor immune microenvironment has become a major determinant of response to therapy, particularly immunotherapy. Clinically, a tumor microenvironment lacking lymphocytes, so-called "cold" tumors, are considered poor candidates for immune checkpoint inhibition. In this review, we describe the diversity of the tumor immune microenvironment in breast cancer and how radiation exposure alters carcinogenesis. We review the development and use of a radiation-genetic mammary chimera model to clarify the mechanism by which radiation acts. Using the chimera model, we demonstrate that systemic inflammation elicited by a low dose of radiation is key to the construction of an immunosuppressive tumor microenvironment, resulting in aggressive, rapidly growing tumors lacking lymphocytes. Our experimental studies inform the non-mutagenic mechanisms by which radiation affects cancer and provide insight into the genesis of cold tumors.

A comprehensive multi-omics study reveals potential prognostic and diagnostic biomarkers for colorectal cancer

BACKGROUND AND OBJECTIVE

Colorectal cancer (CRC) is a complex disease with diverse genetic alterations and causes 10 % of cancer-related deaths worldwide. Understanding its molecular mechanisms is essential for identifying potential biomarkers and therapeutic targets for its effective management.

METHODS

We integrated copy number alterations (CNA) and mutation data via their differentially expressed genes termed as candidate genes (CGs) computed using bioinformatics approaches. Then, using the CGs, we perform Weighted correlation network analysis (WGCNA) and utilise several hazard models such as Univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO) Cox and multivariate Cox to identify the key genes involved in CRC progression. We used different machine-learning models to demonstrate the discriminative power of selected hub genes among normal and CRC (early and late-stage) samples.

RESULTS

The integration of CNA with mRNA expression identified over 3000 CGs, including CRC-specific driver genes like MYC and APC. In addition, pathway analysis revealed that the CGs are mainly enriched in endocytosis, cell cycle, wnt signalling and mTOR signalling pathways. Hazard models identified four key genes, CASP2, HCN4, LRRC69 and SRD5A1, that were significantly associated with CRC progression and predicted the 1-year, 3-years, and 5-years survival times. WGCNA identified seven hub genes: DSCC1, ETV4, KIAA1549, NOP56, RRS1, TEAD4 and ANKRD13B, which exhibited strong predictive performance in distinguishing normal from CRC (early and late-stage) samples.

CONCLUSIONS

Integrating regulatory information with gene expression improved early versus late-stage prediction. The identified potential prognostic and diagnostic biomarkers in this study may guide us in developing effective therapeutic strategies for CRC management.

Element-specific estimation of background mutation rates in whole cancer genomes through transfer learning

Mutational burden tests are essential for detecting signals of positive selection in cancer driver discovery by comparing observed mutation rates with background mutation rates (BMRs). However, accurate BMR estimation is challenging due to the diversity of mutational processes across genomes, complicating driver discovery efforts. Existing methods rely on various genomic regions and features for BMR estimation but lack a model that integrates both intergenic intervals and functional genomic elements on a comprehensive set of genomic features. Here, we introduce eMET (element-specific Mutation Estimator with boosted Trees), which employs 1372 (epi)genomic features from intergenic data and fine-tunes it with element-specific data through transfer learning. Applied to PCAWG somatic mutations, eMET significantly improves BMR accuracy and has potential to enhance driver discovery. Additionally, we provide an extensive analysis of BMR estimation, examining different machine learning models, genomic interval strategies, feature categories, and dimensionality reduction techniques.

Integrative In Silico Analysis to Identify Functional and Structural Impacts of nsSNPs on Programmed Cell Death Protein 1 (PD-1) Protein and UTRs: Potential Biomarkers for Cancer Susceptibility

Background: Programmed cell death protein 1 (PD-1), encoded by the PDCD1 gene, is critical in immune checkpoint regulation and cancer immune evasion. Variants in PDCD1 may alter its function, impacting cancer susceptibility and disease progression. Objectives: This study evaluates the structural, functional, and regulatory impacts of non-synonymous single-nucleotide polymorphisms (nsSNPs) in the PDCD1 gene, focusing on their pathogenic and oncogenic roles. Methods: Computational tools, including PredictSNP1.0, I-Mutant2.0, MUpro, HOPE, MutPred2, Cscape, Cscape-Somatic, GEPIA2, cBioPortal, and STRING, were used to analyze 695 nsSNPs in the PD1 protein. The analysis covered structural impacts, stability changes, regulatory effects, and oncogenic potential, focusing on conserved domains and protein-ligand interactions. Results: The analysis identified 84 deleterious variants, with 45 mapped to conserved regions like the Ig V-set domain essential for ligand-binding interactions. Stability analyses identified 78 destabilizing variants with significant protein instability (ΔΔG values). Ten nsSNPs were identified as potential cancer drivers. Expression profiling showed differential PDCD1 expression in tumor versus normal tissues, correlating with improved survival in skin melanoma but limited value in ovarian cancer. Regulatory SNPs disrupted miRNA-binding sites and transcriptional regulation, affecting PDCD1 expression. STRING analysis revealed key PD-1 protein partners within immune pathways, including PD-L1 and PD-L2. Conclusions: This study highlights the significance of PDCD1 nsSNPs as potential biomarkers for cancer susceptibility, advancing the understanding of PD-1 regulation. Experimental validation and multi-omics integration are crucial to refine these findings and enhance theraputic strategies.

Decoding the functional impact of the cancer genome through protein-protein interactions

Acquisition of genomic mutations enables cancer cells to gain fitness advantages under selective pressure and, ultimately, leads to oncogenic transformation. Interestingly, driver mutations, even within the same gene, can yield distinct phenotypes and clinical outcomes, necessitating a mutation-focused approach. Conversely, cellular functions are governed by molecular machines and signalling networks that are mostly controlled by protein-protein interactions (PPIs). The functional impact of individual genomic alterations could be transmitted through regulated nodes and hubs of PPIs. Oncogenic mutations may lead to modified residues of proteins, enabling interactions with other proteins that the wild-type protein does not typically interact with, or preventing interactions with proteins that the wild-type protein usually interacts with. This can result in the rewiring of molecular signalling cascades and the acquisition of an oncogenic phenotype. Here, we review the altered PPIs driven by oncogenic mutations, discuss technologies for monitoring PPIs and provide a functional analysis of mutation-directed PPIs. These driver mutation-enabled PPIs and mutation-perturbed PPIs present a new paradigm for the development of tumour-specific therapeutics. The intersection of cancer variants and altered PPI interfaces represents a new frontier for understanding oncogenic rewiring and developing tumour-selective therapeutic strategies.

Advancements in proteogenomics for preclinical targeted cancer therapy research

Advancements in molecular characterization technologies have accelerated targeted cancer therapy research at unprecedented resolution and dimensionality. Integrating comprehensive multi-omic molecular profiling of a tumor, proteogenomics, marks a transformative milestone for preclinical cancer research. In this paper, we initially provided an overview of proteogenomics in cancer research, spanning genomics, transcriptomics, and proteomics. Subsequently, the applications were introduced and examined from different perspectives, including but not limited to genetic alterations, molecular quantifications, single-cell patterns, different post-translational modification levels, subtype signatures, and immune landscape. We also paid attention to the combined multi-omics data analysis and pan-cancer analysis. This paper highlights the crucial role of proteogenomics in preclinical targeted cancer therapy research, including but not limited to elucidating the mechanisms of tumorigenesis, discovering effective therapeutic targets and promising biomarkers, and developing subtype-specific therapies.

RNA sensing at the crossroads of autoimmunity and autoinflammation

Immune-mediated diseases are common in humans. The immune system is a complex host defense system that evolved to protect us from pathogens, but also plays an important role in homeostatic processes, removing dead or senescent cells, and participating in tumor surveillance. The human immune system has two arms: the older innate immune system and the newer adaptive immune system. Sensing of foreign RNA is critical to the innate immune system's ability to recognize pathogens, especially viral infections. However, RNA sensors are also strongly implicated in autoimmune and autoinflammatory diseases, highlighting the importance of balancing pathogen recognition with tolerance to host RNAs that can resemble their viral counterparts. We describe how RNA sensors bind their ligands, how this binding is coupled to upregulation of type I interferon-stimulated genes, and the ways in which mutations in RNA sensors and genes that play important roles in RNA homeostasis have been linked to autoimmune and autoinflammatory diseases.

Amyloids in bladder cancer hijack cancer-related proteins and are positive correlated to tumor stage

Despite the current diagnostic and therapeutic approaches to bladder cancer being widely accepted, there have been few significant advancements in this field over the past decades. This underscores the necessity for a paradigm shift in the approach to bladder cancer. The role of amyloids in cancer remains unclear despite their identification in several other pathologies. In this study, we present evidence of amyloids in bladder cancer, both in vitro and in vivo. In a murine model of bladder cancer, a positive correlation was observed between amyloids and tumor stage, indicating an association between amyloids and bladder cancer progression. Subsequently, the amyloid proteome of the RT4 non-invasive and HT1197 invasive bladder cancer cell lines was identified and included oncogenes, tumor suppressors, and highly expressed cancer-related proteins. It is proposed that amyloids function as structures that sequester key proteins. Therefore, amyloids should be considered in the study and diagnosis of bladder cancer.

Bioelectricity is a universal multifaced signaling cue in living organisms

The cellular electrical signals of living organisms were discovered more than a century ago and have been extensively investigated in the neuromuscular system. Neuronal depolarization and hyperpolarization are essential for our neuromuscular physiological and pathological functions. Bioelectricity is being recognized as an ancient, intrinsic, fundamental property of all living cells, and it is not limited to the neuromuscular system. Instead, emerging evidence supports a view of bioelectricity as an instructional signaling cue for fundamental cellular physiology, embryonic development, regeneration, and human diseases, including cancers. Here, we highlight the current understanding of bioelectricity and share our views on the challenges and perspectives.

Identifying key genes in cancer networks using persistent homology

Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. These methods can overlook the high-dimensional interactions that cancer genes have within cancer networks. This study presents a novel method using Persistent Homology to analyze the role of driver genes in higher-order structures within Cancer Consensus Networks derived from main cellular pathways. We integrate mutation data from six cancer types and three biological functions: DNA Repair, Chromatin Organization, and Programmed Cell Death. We systematically evaluated the impact of gene removal on topological voids ([Formula: see text] structures) within the Cancer Consensus Networks. Our results reveal that only known driver genes and cancer-associated genes influence these structures, while passenger genes do not. Although centrality measures alone proved insufficient to fully characterize impact genes, combining higher-order topological analysis with traditional network metrics can improve the precision of distinguishing between drivers and passengers. This work shows that cancer genes play an important role in higher-order structures, going beyond pairwise measures, and provides an approach to distinguish drivers and cancer-associated genes from passenger genes.

A forward genetic screen identifies potassium channel essentiality in SHH medulloblastoma maintenance

Distinguishing tumor maintenance genes from initiation, progression, and passenger genes is critical for developing effective therapies. We employed a functional genomic approach using the Lazy Piggy transposon to identify tumor maintenance genes in vivo and applied this to sonic hedgehog (SHH) medulloblastoma (MB). Combining Lazy Piggy screening in mice and transcriptomic profiling of human MB, we identified the voltage-gated potassium channel KCNB2 as a candidate maintenance driver. KCNB2 governs cell volume of MB-propagating cells (MPCs), with KCNB2 depletion causing osmotic swelling, decreased plasma membrane tension, and elevated endocytic internalization of epidermal growth factor receptor (EGFR), thereby mitigating proliferation of MPCs to ultimately impair MB growth. KCNB2 is largely dispensable for mouse development and KCNB2 knockout synergizes with anti-SHH therapy in treating MB. These results demonstrate the utility of the Lazy Piggy functional genomic approach in identifying cancer maintenance drivers and elucidate a mechanism by which potassium homeostasis integrates biomechanical and biochemical signaling to promote MB aggression.

Molecularly redefining small bowel adenocarcinoma to accelerate precision patient care - protocol of a multicenter observational cohort biomarker study

BACKGROUND

Small Bowel Adenocarcinoma (SBA) is a rare gastrointestinal cancer with a limited understanding of the molecular pathology. This study aims to bridge the knowledge gap, providing a robust molecular foundation for SBA and addressing the clinical challenges inherent in treating this orphan disease. The study proposes to redefine the clinical management for SBA patients through advanced molecular profiling techniques to improve potential precision medicine.

METHODS/DESIGN

This National multicenter, observational cohort study combines retrospective and prospective analyses across Danish University Hospitals. The study enrolls patients diagnosed with SBA, retrospectively from 2009 and prospectively from 2022 onwards. Molecular profiling, including DNA, RNA, and T-cell receptor sequencing, will be conducted on SBA tissue samples. The primary outcome is to categorize SBA into consensus molecular-guided subgroups. Secondary outcomes include correlating these subgroups with clinical features, treatment responses, and patient outcomes. Machine learning algorithms will be employed for bioinformatic analyses to interpret molecular data. Ethical approval has been obtained, and patient consent will be secured for the retrospective study component.

DISCUSSION

The molecular and clinical characterization of SBA is expected to add novel insights into the heterogeneity of this rare disease. By identifying molecular subgroups, the research could enable the development of personalized treatment strategies, a paradigm shift within SBA. The study acknowledges the challenges of working with orphan diseases, including limited patient numbers and diverse clinical presentations. However, its findings will have the potential to substantially impact future clinical practices and guide targeted therapies for SBA patients.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06234306.

Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics

Large-scale omics profiling has uncovered a vast array of somatic mutations and cancer-associated proteins, posing substantial challenges for their functional interpretation. Here we present a network-based approach centered on FunMap, a pan-cancer functional network constructed using supervised machine learning on extensive proteomics and RNA sequencing data from 1,194 individuals spanning 11 cancer types. Comprising 10,525 protein-coding genes, FunMap connects functionally associated genes with unprecedented precision, surpassing traditional protein-protein interaction maps. Network analysis identifies functional protein modules, reveals a hierarchical structure linked to cancer hallmarks and clinical phenotypes, provides deeper insights into established cancer drivers and predicts functions for understudied cancer-associated proteins. Additionally, applying graph-neural-network-based deep learning to FunMap uncovers drivers with low mutation frequency. This study establishes FunMap as a powerful and unbiased tool for interpreting somatic mutations and understudied proteins, with broad implications for advancing cancer biology and informing therapeutic strategies.

Prospects and challenges of neoantigen applications in oncology

Neoantigen, unique peptides resulting from tumor-specific mutations, represent a promising frontier in oncology for personalized cancer immunotherapy. Their unique features allow for the development of highly specific and effective cancer treatments, which can potentially overcome the limitations of conventional therapies. This paper explores the current prospects and challenges associated with the application of neoantigens in oncology. We examine the latest advances in neoantigen identification, vaccine development, and adoptive T cell therapy. Additionally, we discuss the obstacles related to neoantigen heterogeneity, immunogenicity prediction, and the tumor microenvironment. Through a comprehensive analysis of current research and clinical trials, this paper aims to provide a detailed overview of how neoantigens could revolutionize cancer treatment and the hurdles that must be overcome to realize their full potential.

Gene choice in cancer cells is exclusive in ion transport but concurrent in DNA replication

Cancers share common cellular and physiological features. Little is known about whether distinctive gene expression patterns can be displayed at the single-cell level by gene families in cancer cells. The expression of gene homologs within a family can exhibit concurrence and exclusivity. Concurrence can promote all-or-none expression patterns of related genes and underlie alternative physiological states. Conversely, exclusive gene families express the same or similar number of homologs in each cell, allowing a broad repertoire of cell identities to be generated. We show that gene families involved in the cell-cycle and antigen presentation are expressed concurrently. Concurrence in the DNA replication complex MCM reflects the replicative status of cells, including cell lines and cancer-derived organoids. Exclusive expression requires precise regulatory mechanism, but cancer cells retain this form of control for ion homeostasis and extend it to gene families involved in cell migration. Thus, the cell adhesion-based identity of healthy cells is transformed to an identity based on migration in the population of cancer cells, reminiscent of epithelial-mesenchymal transition.

Protein structural context of cancer mutations reveals molecular mechanisms and candidate driver genes

Advances in protein structure determination and modeling allow us to study the structural context of human genetic variants on an unprecedented scale. Here, we analyze millions of cancer-associated missense mutations based on their structural locations and predicted perturbative effects. By considering the collective properties of mutations at the level of individual proteins, we identify distinct patterns associated with tumor suppressors and oncogenes. Tumor suppressors are enriched in structurally damaging mutations, consistent with loss-of-function mechanisms, while oncogene mutations tend to be structurally mild, reflecting selection for gain-of-function driver mutations and against loss-of-function mutations. Although oncogenes are difficult to distinguish from genes with no role in cancer using only structural damage, we find that the three-dimensional clustering of mutations is highly predictive. These observations allow us to identify candidate driver genes and speculate about their molecular roles, which we expect will have general utility in the analysis of cancer sequencing data.

Emerging Gene-editing nano-therapeutics for Cancer

Remarkable progress has been made in the field of genome engineering after the discovery of CRISPR/Cas9 in 2012 by Jennifer Doudna and Emmanuelle Charpentier. Compared to any other gene-editing tools, CRISPR/Cas9 attracted the attention of the scientific community because of its simplicity, specificity, and multiplex editing possibilities for which the inventors were awarded the Nobel prize for chemistry in 2020. CRISPR/Cas9 allows targeted alteration of the genomic sequence, gene regulation, and epigenetic modifications using an RNA-guided site-specific endonuclease. Though the impact of CRISPR/Cas9 was undisputed, some of its limitations led to key modifications including the use of miniature-Cas proteins, Cas9 Retron precise Parallel Editing via homologY (CRISPEY), Cas-Clover, or development of alternative methods including retron-recombineering, Obligate Mobile Element Guided Activity(OMEGA), Fanzor, and Argonaute proteins. As cancer is caused by genetic and epigenetic alterations, gene-editing was found to be highly useful for knocking out oncogenes, editing mutations to regain the normal functioning of tumor suppressor genes, knock-out immune checkpoint blockade in CAR-T cells, producing 'off-the-shelf' CAR-T cells, identify novel tumorigenic genes and functional analysis of multiple pathways in cancer, etc. Advancements in nanoparticle-based delivery of guide-RNA and Cas9 complex to the human body further enhanced the potential of CRISPR/Cas9 for clinical translation. Several studies are reported for developing novel delivery methods to enhance the tumor-specific application of CRISPR/Cas9 for anticancer therapy. In this review, we discuss new developments in novel gene editing techniques and recent progress in nanoparticle-based CRISPR/Cas9 delivery specific to cancer applications.

From Genomic Exploration to Personalized Treatment: Next-Generation Sequencing in Oncology

Next-generation sequencing (NGS) has revolutionized personalized oncology care by providing exceptional insights into the complex genomic landscape. NGS offers comprehensive cancer profiling, which enables clinicians and researchers to better understand the molecular basis of cancer and to tailor treatment strategies accordingly. Targeted therapies based on genomic alterations identified through NGS have shown promise in improving patient outcomes across various cancer types, circumventing resistance mechanisms and enhancing treatment efficacy. Moreover, NGS facilitates the identification of predictive biomarkers and prognostic indicators, aiding in patient stratification and personalized treatment approaches. By uncovering driver mutations and actionable alterations, NGS empowers clinicians to make informed decisions regarding treatment selection and patient management. However, the full potential of NGS in personalized oncology can only be realized through bioinformatics analyses. Bioinformatics plays a crucial role in processing raw sequencing data, identifying clinically relevant variants, and interpreting complex genomic landscapes. This comprehensive review investigates the diverse NGS techniques, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and single-cell RNA sequencing (sc-RNA-Seq), elucidating their roles in understanding the complex genomic/transcriptomic landscape of cancer. Furthermore, the review explores the integration of NGS data with bioinformatics tools to facilitate personalized oncology approaches, from understanding tumor heterogeneity to identifying driver mutations and predicting therapeutic responses. Challenges and future directions in NGS-based cancer research are also discussed, underscoring the transformative impact of these technologies on cancer diagnosis, management, and treatment strategies.

Multi-scale brain attributes contribute to the distribution of diffuse glioma subtypes

Gliomas are primary brain tumors and are among the most malignant types. Adult-type diffuse gliomas can be classified based on their histological and molecular signatures as IDH-wildtype glioblastoma, IDH-mutant astrocytoma, and IDH-mutant and 1p/19q-codeleted oligodendroglioma. Recent studies have shown that each subtype of glioma has its own specific distribution pattern. However, the mechanisms underlying the specific distributions of glioma subtypes are not entirely clear despite partial explanations such as cell origin. To investigate the impact of multi-scale brain attributes on glioma distribution, we constructed cumulative frequency maps for diffuse glioma subtypes based on T1w structural images and evaluated the spatial correlation between tumor frequency and diverse brain attributes, including postmortem gene expression, functional connectivity metrics, cerebral perfusion, glucose metabolism, and neurotransmitter signaling. Regression models were constructed to evaluate the contribution of these factors to the anatomic distribution of different glioma subtypes. Our findings revealed that the three different subtypes of gliomas had distinct distribution patterns, showing spatial preferences toward different brain environmental attributes. Glioblastomas were especially likely to occur in regions enriched with synapse-related pathways and diverse neurotransmitter receptors. Astrocytomas and oligodendrogliomas preferentially occurred in areas enriched with genes associated with neutrophil-mediated immune responses. The functional network characteristics and neurotransmitter distribution also contributed to oligodendroglioma distribution. Our results suggest that different brain transcriptomic, neurotransmitter, and connectomic attributes are the factors that determine the specific distributions of glioma subtypes. These findings highlight the importance of bridging diverse scales of biological organization when studying neurological dysfunction.

Nanomedicine for cancer patient-centered care

Cancer is a leading cause of morbidity and mortality worldwide, and an increase in incidence is estimated in the next future, due to population aging, which requires the development of highly tolerable and low-toxicity cancer treatment strategies. The use of nanotechnology to tailor treatments according to the genetic and immunophenotypic characteristics of a patient's tumor, and to allow its targeted release, can meet this need, improving the efficacy of treatment and minimizing side effects. Nanomedicine-based approach for the diagnosis and treatment of cancer is a rapidly evolving field. Several nanoformulations are currently in clinical trials, and some have been approved and marketed. However, their large-scale production and use are still hindered by an in-depth debate involving ethics, intellectual property, safety and health concerns, technical issues, and costs. Here, we survey the key approaches, with specific reference to organ-on chip technology, and cutting-edge tools, such as CRISPR/Cas9 genome editing, through which nanosystems can meet the needs for personalized diagnostics and therapy in cancer patients. An update is provided on the nanopharmaceuticals approved and marketed for cancer therapy and those currently undergoing clinical trials. Finally, we discuss the emerging avenues in the field and the challenges to be overcome for the transfer of nano-based precision oncology into clinical daily life.

Towards functional maps of non-coding variants in cancer

Large scale cancer genomic studies in patients have unveiled millions of non-coding variants. While a handful have been shown to drive cancer development, the vast majority have unknown function. This review describes the challenges of functionally annotating non-coding cancer variants and understanding how they contribute to cancer. We summarize recently developed high-throughput technologies to address these challenges. Finally, we outline future prospects for non-coding cancer genetics to help catalyze personalized cancer therapy.

Multiomics in cancer biomarker discovery and cancer subtyping

The advent of multiomics has ushered in a new era of cancer research characterized by integrated genomic, transcriptomic and proteomic analysis to unravel the complexities of cancer biology and facilitate the discovery of novel biomarkers. This chapter provides a comprehensive overview of the concept of multiomics, detailing the significant advances in the underlying technologies and their contributions to our understanding of cancer. It delves into the evolution of genomics and transcriptomics, breakthroughs in proteomics, and overarching progress in multiomic methodologies, highlighting their collective impact on cancer biomarker discovery. Furthermore, this chapter explores the computational methods essential for multiomic studies, including clustering techniques for delineating cancer subtypes, strategies for estimating molecular features and activities, and utility of pathway enrichment analyses for interpreting multiomic datasets. Particular focus has been placed on the application of these methods for identifying distinct cancer subtypes, thereby enabling a more personalized approach to cancer treatment. Through a detailed discussion of the scientific principles, technological advancements, and practical applications of multiomics, this chapter aims to underscore the pivotal role of multiomics in advancing cancer research and paving the way for personalized medicine. The insights provided herein not only illuminate the current landscape of cancer biomarker discovery, but also forecast future directions of multiomics research in oncology, advocating for a more integrated and nuanced approach to understanding and combating cancer.

Tumor immunogenicity regulates host immune responses, and conventional dendritic cell type 2 uptakes the majority of tumor antigens in an orthotopic lung cancer model

Human lung cancer carries high genetic alterations, expressing high tumor-specific neoantigens. Although orthotopic murine lung cancer models recapitulate many characteristics of human lung cancers, genetically engineered mouse models have fewer somatic mutations than human lung cancer, resulting in scarce immune cell infiltration and deficient immune responses. The endogenous mouse lung cancer model driven by Kras mutation and Trp53 deletion (KP model) has minimal immune infiltration because of a scarcity of neoantigens. Fine-tuning tumor antigenicity to trigger the appropriate level of antitumor immunity would be key to investigating immune responses against human lung cancer. We engineered the KP model to express antigens of OVA peptides (minOVA) as neoantigens along with ZsGreen, a traceable fluorescent conjugate. The KP model expressing minOVA exhibited stronger immunogenicity with higher immune cell infiltration comprised of CD8+ T cells and CD11c+ dendritic cells (DCs). Consequently, the KP model expressing minOVA exhibits suppressed tumor growth compared to its origin. We further analyzed tumor-infiltrated DCs. The majority of ZsGreen conjugated with minOVA was observed in the conventional type 2 DCs (cDC2), whereas cDC1 has minimal. These data indicate that tumor immunogenicity regulates host immune responses, and tumor neoantigen is mostly recognized by cDC2 cells, which may play a critical role in initiating antitumor immune responses in an orthotopic murine lung cancer model.

ASM variants in the spotlight: A structure-based atlas for unraveling pathogenic mechanisms in lysosomal acid sphingomyelinase

Lysosomal acid sphingomyelinase (ASM), a critical enzyme in lipid metabolism encoded by the SMPD1 gene, plays a crucial role in sphingomyelin hydrolysis in lysosomes. ASM deficiency leads to acid sphingomyelinase deficiency, a rare genetic disorder with diverse clinical manifestations, and the protein can be found mutated in other diseases. We employed a structure-based framework to comprehensively understand the functional implications of ASM variants, integrating pathogenicity predictions with molecular insights derived from a molecular dynamics simulation in a lysosomal membrane environment. Our analysis, encompassing over 400 variants, establishes a structural atlas of missense variants of lysosomal ASM, associating mechanistic indicators with pathogenic potential. Our study highlights variants that influence structural stability or exert local and long-range effects at functional sites. To validate our predictions, we compared them to available experimental data on residual catalytic activity in 135 ASM variants. Notably, our findings also suggest applications of the resulting data for identifying cases suited for enzyme replacement therapy. This comprehensive approach enhances the understanding of ASM variants and provides valuable insights for potential therapeutic interventions.

Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States

Prostate cancer is a significant health concern and the most commonly diagnosed cancer in men worldwide. Understanding the complex process of prostate tumor evolution and progression is crucial for improved diagnosis, treatments, and patient outcomes. Previous studies have focused on unraveling the dynamics of prostate cancer evolution using phylogenetic or lineage analysis approaches. However, those approaches have limitations in capturing the complete disease process or incorporating genomic and transcriptomic variations comprehensively. In this study, we applied a novel computational approach to derive a prostate cancer progression model using multidimensional data from 497 prostate tumor samples and 52 tumor-adjacent normal samples obtained from The Cancer Genome Atlas study. The model was validated using data from an independent cohort of 545 primary tumor samples. By integrating transcriptomic and genomic data, our model provides a comprehensive view of prostate tumor progression, identifies crucial signaling pathways and genetic events, and uncovers distinct transcription signatures associated with disease progression. Our findings have significant implications for cancer research and hold promise for guiding personalized treatment strategies in prostate cancer.

SIGNIFICANCE

We developed and validated a progression model of prostate cancer using >1,000 tumor and normal tissue samples. The model provided a comprehensive view of prostate tumor evolution and progression.

Machine learning optimized DriverDetect software for high precision prediction of deleterious mutations in human cancers

The detection of cancer-driving mutations is important for understanding cancer pathology and therapeutics development. Prediction tools have been created to streamline the computation process. However, most tools available have heterogeneous sensitivity or specificity. We built a machine learning-derived algorithm, DriverDetect that combines the outputs of seven pre-existing tools to improve the prediction of candidate driver cancer mutations. The algorithm was trained with cancer gene-specific mutation datasets of cancer patients to identify cancer drivers. DriverDetect performed better than the individual tools or their combinations in the validation test. It has the potential to incorporate future novel prediction algorithms and can be retrained with new datasets, offering an expanded application to pan-cancer analysis for cross-cancer study. (115 words).

CDMPred: a tool for predicting cancer driver missense mutations with high-quality passenger mutations

Most computational methods for predicting driver mutations have been trained using positive samples, while negative samples are typically derived from statistical methods or putative samples. The representativeness of these negative samples in capturing the diversity of passenger mutations remains to be determined. To tackle these issues, we curated a balanced dataset comprising driver mutations sourced from the COSMIC database and high-quality passenger mutations obtained from the Cancer Passenger Mutation database. Subsequently, we encoded the distinctive features of these mutations. Utilizing feature correlation analysis, we developed a cancer driver missense mutation predictor called CDMPred employing feature selection through the ensemble learning technique XGBoost. The proposed CDMPred method, utilizing the top 10 features and XGBoost, achieved an area under the receiver operating characteristic curve (AUC) value of 0.83 and 0.80 on the training and independent test sets, respectively. Furthermore, CDMPred demonstrated superior performance compared to existing state-of-the-art methods for cancer-specific and general diseases, as measured by AUC and area under the precision-recall curve. Including high-quality passenger mutations in the training data proves advantageous for CDMPred's prediction performance. We anticipate that CDMPred will be a valuable tool for predicting cancer driver mutations, furthering our understanding of personalized therapy.

CIBRA identifies genomic alterations with a system-wide impact on tumor biology

MOTIVATION

Genomic instability is a hallmark of cancer, leading to many somatic alterations. Identifying which alterations have a system-wide impact is a challenging task. Nevertheless, this is an essential first step for prioritizing potential biomarkers. We developed CIBRA (Computational Identification of Biologically Relevant Alterations), a method that determines the system-wide impact of genomic alterations on tumor biology by integrating two distinct omics data types: one indicating genomic alterations (e.g. genomics), and another defining a system-wide expression response (e.g. transcriptomics). CIBRA was evaluated with genome-wide screens in 33 cancer types using primary and metastatic cancer data from the Cancer Genome Atlas and Hartwig Medical Foundation.

RESULTS

We demonstrate the capability of CIBRA by successfully confirming the impact of point mutations in experimentally validated oncogenes and tumor suppressor genes (0.79 AUC). Surprisingly, many genes affected by structural variants were identified to have a strong system-wide impact (30.3%), suggesting that their role in cancer development has thus far been largely under-reported. Additionally, CIBRA can identify impact with only 10 cases and controls, providing a novel way to prioritize genomic alterations with a prominent role in cancer biology. Our findings demonstrate that CIBRA can identify cancer drivers by combining genomics and transcriptomics data. Moreover, our work shows an unexpected substantial system-wide impact of structural variants in cancer. Hence, CIBRA has the potential to preselect and refine current definitions of genomic alterations to derive more nuanced biomarkers for diagnostics, disease progression, and treatment response.

AVAILABILITY AND IMPLEMENTATION

The R package CIBRA is available at https://github.com/AIT4LIFE-UU/CIBRA.

Exploring Cancer Immunotherapy and the Promise of Cancer Vaccine

The goal of immunotherapy is to enhance the immune system by managing the immunological-mediated microenvironment, which makes it possible for immune cells to locate and destroy tumour cells at vital nodes. In the tumor microenvironment, immune responses against tumour cells are reduced when these cells take up immune-regulatory mechanisms. An environment that suppresses the immune system is facilitated by immune cells, including regulatory T cells, regulatory B cells, dendritic cells, and myeloid-derived suppressor cells. In a number of cancer types, adoptive immune cells and immune checkpoint modulators have shown impressive anticancer benefits. Tumour growth is facilitated in large part by immune cells found in the tumour microenvironment (TME). Tumour growth may be stimulated or inhibited by these cells. The ability of the immune system to elude detection by cancer cells offers new possibilities for innovative cancer treatment strategies.

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.

Are Next-Generation Pathogenicity Predictors Applicable to Cancer?

Next-generation pathogenicity predictors are designed to identify pathogenic mutations in genetic disorders but are increasingly used to detect driver mutations in cancer. Despite this, their suitability for cancer is not fully established. Here we have assessed the effectiveness of next-generation pathogenicity predictors when applied to cancer by using a comprehensive experimental benchmark of cancer driver and neutral mutations. Our findings indicate that state-of-the-art methods AlphaMissense and VARITY demonstrate commendable performance despite generally underperforming compared to cancer-specific methods. This is notable considering that these methods do not explicitly incorporate cancer-related data in their training and have made concerted efforts to prevent data leakage from the human-curated training and test sets. Nevertheless, it should be mentioned that a significant limitation of using pathogenicity predictors for cancer arises from their inability to detect cancer potential driver mutations specific for a particular cancer type.

Expression and Clinical Significance of NUDCD1, PI3K/AKT/mTOR Signaling Pathway-Related Molecules and Immune Infiltration in Breast Cancer

BACKGROUND

NUDCD1 (NudC Domain Containing 1) performs an essential function in biological processes such as cell progression, migration, cell cycle, and intracellular material transport. Many solid tumors express it highly, which is a prospective biomarker and therapeutic approach. However, the expression and clinical importance of NUDCD1 across breast cancer is unclear.

METHODS

The expressions of NUDCD1 in breast cancers and normal breast tissues were studied utilizing the TIMER database and immunohistochemical analysis. Subsequently, we validate the association between the expression of NUDCD1 and clinicopathologic features and prognosis of breast cancer. The immunohistochemical experiments of pathway-related molecules were done on 214 breast cancer tissue microarrays. The investigation of correlation between NUDCD1 expression and tumor immune infiltration was subsequently conducted.

RESULTS

Through the utilization of bioinformatics analysis and immunohistochemical experiments, it was determined that NUDCD1 exhibited upregulation within breast cancer. Furthermore, it was discovered that an elevated expression of NUDCD1 may potentially be linked to a worse prognosis in breast cancer. Our study reveals that the PI3K/AKT/mTOR signaling pathway may perform a function in NUDCD1 regulating breast cancer progression via enrichment analysis. Furthermore, the expression of NUDCD1 may be associated with the degree of immunological infiltration.

CONCLUSION

The expression of NUDCD1 was explored to be elevated in breast cancer and was observed to be correlated with a poorer prognosis. p-AKT, PI3K, AKT, mTOR, and p-mTOR expression levels underwent significant elevation in breast cancer. The function of NUDCD1 within breast cancer might be associated with the PI3K/AKT/mTOR signaling pathway.

IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification

The identification of cancer driver genes is crucial for early detection, effective therapy, and precision medicine of cancer. Cancer is caused by the dysregulation of several genes at various levels of regulation. However, current techniques only capture a limited amount of regulatory information, which may hinder their efficacy. In this study, we present IMI-driver, a model that integrates multi-omics data into eight biological networks and applies Multi-view Collaborative Network Embedding to embed the gene regulation information from the biological networks into a low-dimensional vector space to identify cancer drivers. We apply IMI-driver to 29 cancer types from The Cancer Genome Atlas (TCGA) and compare its performance with nine other methods on nine benchmark datasets. IMI-driver outperforms the other methods, demonstrating that multi-level network integration enhances prediction accuracy. We also perform a pan-cancer analysis using the genes identified by IMI-driver, which confirms almost all our selected candidate genes as known or potential drivers. Case studies of the new positive genes suggest their roles in cancer development and progression.

Establishment of patient-derived organoids for guiding personalized therapies in breast cancer patients

Breast cancer has become the most commonly diagnosed cancer. The intra- and interpatient heterogeneity induced a considerable variation in treatment efficacy. There is an urgent requirement for preclinical models to anticipate the effectiveness of individualized drug responses. Patient-derived organoids (PDOs) can accurately recapitulate the architecture and biological characteristics of the origin tumor, making them a promising model that can overtake many limitations of cell lines and PDXs. However, it is still unclear whether PDOs-based drug testing can benefit breast cancer patients, particularly those with tumor recurrence or treatment resistance. Fresh tumor samples were surgically resected for organoid culture. Primary tumor samples and PDOs were subsequently subjected to H&E staining, immunohistochemical (IHC) analysis, and whole-exome sequencing (WES) to make comparisons. Drug sensitivity tests were performed to evaluate the feasibility of this model for predicting patient drug response in clinical practice. We established 75 patient-derived breast cancer organoid models. The results of H&E staining, IHC, and WES revealed that PDOs inherited the histologic and genetic characteristics of their parental tumor tissues. The PDOs successfully predicted the patient's drug response, and most cases exhibited consistency between PDOs' drug susceptibility test results and the clinical response of the matched patient. We conclude that the breast cancer organoids platform can be a potential preclinical tool used for the selection of effective drugs and guided personalized therapies for patients with advanced breast cancer.

Protein folding, cellular stress and cancer

Proteins are acknowledged as the phenotypical manifestation of the genotype, because protein-coding genes carry the information for the strings of amino acids that constitute the proteins. It is widely accepted that protein function depends on the corresponding "native" structure or folding achieved within the cell, and that native protein folding corresponds to the lowest free energy minimum for a given protein. However, protein folding within the cell is a non-deterministic dissipative process that from the same input may produce different outcomes, thus conformational heterogeneity of folded proteins is the rule and not the exception. Local changes in the intracellular environment promote variation in protein folding. Hence protein folding requires "supervision" by a host of chaperones and co-chaperones that help their client proteins to achieve the folding that is most stable according to the local environment. Such environmental influence on protein folding is continuously transduced with the help of the cellular stress responses (CSRs) and this may lead to changes in the rules of engagement between proteins, so that the corresponding protein interactome could be modified by the environment leading to an alternative cellular phenotype. This allows for a phenotypic plasticity useful for adapting to sudden and/or transient environmental changes at the cellular level. Starting from this perspective, hereunder we develop the argument that the presence of sustained cellular stress coupled to efficient CSRs may lead to the selection of an aberrant phenotype as the resulting adaptation of the cellular proteome (and the corresponding interactome) to such stressful conditions, and this can be a common epigenetic pathway to cancer.

Clonal expansion of cancer driver gene mutants investigated using advanced sequencing technologies

Advanced sequencing technologies (ASTs) have revolutionized the quantitation of cancer driver mutations (CDMs) as rare events, which has utility in clinical oncology, cancer research, and cancer risk assessment. This review focuses on studies that have used ASTs to characterize clonal expansion (CE) of cells carrying CDMs and to explicate the selective pressures that shape CE. Importantly, high-sensitivity ASTs have made possible the characterization of mutant clones and CE in histologically normal tissue samples, providing the means to investigate nascent tumor development. Some ASTs can identify mutant clones in a spatially defined context; others enable integration of mutant data with analyses of gene expression, thereby elaborating immune, inflammatory, metabolic, and/or stromal microenvironmental impacts on CE. As a whole, these studies make it clear that a startlingly large fraction of cells in histologically normal tissues carry CDMs, CDMs may confer a context-specific selective advantage leading to CE, and only a small fraction of cells carrying CDMs eventually result in neoplasia. These observations were integrated with available literature regarding the mechanisms underlying clonal selection to interpret how measurements of CDMs and CE can be interpreted as biomarkers of cancer risk. Given the stochastic nature of carcinogenesis, the potential functional latency of driver mutations, the complexity of potential mutational and microenvironmental interactions, and involvement of other types of genetic and epigenetic changes, it is concluded that CDM-based measurements should be viewed as probabilistic rather than deterministic biomarkers. Increasing inter-sample variability in CDM levels (as a consequence of CE) may be interpretable as a shift away from normal tissue homeostasis and an indication of increased future cancer risk, a process that may reflect normal aging or carcinogen exposure. Consequently, analyses of variability in levels of CDMs have the potential to bolster existing approaches for carcinogenicity testing.

Tumor immunogenicity regulates host immune responses, and conventional dendritic cell type 2 uptakes the majority of tumor antigens in an orthotopic lung cancer model

Human lung cancer carries high genetic alterations, expressing high tumor-specific neoantigens. Although orthotopic murine lung cancer models recapitulate many characteristics of human lung cancers, genetically engineered mouse models have fewer somatic mutations than human lung cancer, resulting in scarce immune cell infiltration and deficient immune responses. The endogenous mouse lung cancer model driven by Kras mutation and Trp53 deletion (KP model) has minimal immune infiltration because of a scarcity of neoantigens. Fine-tuning tumor antigenicity to trigger the appropriate level of antitumor immunity would be key to investigating immune responses against human lung cancer. We engineered the KP model to express antigens of OVA peptides (minOVA) as neoantigens along with ZsGreen, a traceable fluorescent conjugate. The KP model expressing minOVA exhibited stronger immunogenicity with higher immune cell infiltration comprised of CD8+ T cells and CD11c+ dendritic cells (DCs). Consequentially, the KP model expressing minOVA exhibits suppressed tumor growth compared to its origin. We further analyzed tumor-infiltrated DCs. The majority of ZsGreen conjugated with minOVA was observed in the conventional type 2 DCs (cDC2), where cDC1 has minimal. These data indicate that tumor immunogenicity regulates host immune responses, and tumor neoantigen is mostly recognized by cDC2 cells, which may play a critical role in initiating anti-tumor immune responses in an orthotopic murine lung cancer model.

Analysis of 3760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes

BACKGROUND

Rare oncogenic driver events, particularly affecting the expression or splicing of driver genes, are suspected to substantially contribute to the large heterogeneity of hematologic malignancies. However, their identification remains challenging.

METHODS

To address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from 3760 patients spanning 24 disease entities. Taking advantage of our dataset size, we focused on discovering rare regulatory aberrations. Therefore, we called expression and splicing outliers using an extension of the workflow DROP (Detection of RNA Outliers Pipeline) and AbSplice, a variant effect predictor that identifies genetic variants causing aberrant splicing. We next trained a machine learning model integrating these results to prioritize new candidate disease-specific driver genes.

RESULTS

We found a median of seven expression outlier genes, two splicing outlier genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for already well-characterized driver genes, with odds ratios exceeding three among genes called in more than five samples. On held-out data, our integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Remarkably, we found a truncated form of the low density lipoprotein receptor LRP1B transcript to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B as a novel marker for a HCL-V subclass and a yet unreported functional role of LRP1B within these rare entities.

CONCLUSIONS

Altogether, our census of expression and splicing outliers for 24 hematologic malignancy entities and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.

Driver Mutations in Pancreatic Cancer and Opportunities for Targeted Therapy

Pancreatic cancer is the sixth leading cause of cancer-related mortality globally. As the most common form of pancreatic cancer, pancreatic ductal adenocarcinoma (PDAC) represents up to 95% of all pancreatic cancer cases, accounting for more than 300,000 deaths annually. Due to the lack of early diagnoses and the high refractory response to the currently available treatments, PDAC has a very poor prognosis, with a 5-year overall survival rate of less than 10%. Targeted therapy and immunotherapy are highly effective and have been used for the treatment of many types of cancer; however, they offer limited benefits in pancreatic cancer patients due to tumor-intrinsic and extrinsic factors that culminate in drug resistance. The identification of key factors responsible for PDAC growth and resistance to different treatments is highly valuable in developing new effective therapeutic strategies. In this review, we discuss some molecules which promote PDAC initiation and progression, and their potential as targets for PDAC treatment. We also evaluate the challenges associated with patient outcomes in clinical trials and implications for future research.

A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer

A key challenge in cancer genomics is understanding the functional relationships and dependencies between combinations of somatic mutations that drive cancer development. Such driver mutations frequently exhibit patterns of mutual exclusivity or co-occurrence across tumors, and many methods have been developed to identify such dependency patterns from bulk DNA sequencing data of a cohort of patients. However, while mutual exclusivity and co-occurrence are described as properties of driver mutations, existing methods do not explicitly disentangle functional, driver mutations from neutral, passenger mutations. In particular, nearly all existing methods evaluate mutual exclusivity or co-occurrence at the gene level, marking a gene as mutated if any mutation - driver or passenger - is present. Since some genes have a large number of passenger mutations, existing methods either restrict their analyses to a small subset of suspected driver genes - limiting their ability to identify novel dependencies - or make spurious inferences of mutual exclusivity and co-occurrence involving genes with many passenger mutations. We introduce DIALECT, an algorithm to identify dependencies between pairs of driver mutations from somatic mutation counts. We derive a latent variable mixture model for drivers and passengers that combines existing probabilistic models of passenger mutation rates with a latent variable describing the unknown status of a mutation as a driver or passenger. We use an expectation maximization (EM) algorithm to estimate the parameters of our model, including the rates of mutually exclusivity and co-occurrence between drivers. We demonstrate that DIALECT more accurately infers mutual exclusivity and co-occurrence between driver mutations compared to existing methods on both simulated mutation data and somatic mutation data from 5 cancer types in The Cancer Genome Atlas (TCGA).

Discovery of Novel Potential Prognostic Markers and Targeted Therapy to Overcome Chemotherapy Resistance in an Advanced-Stage Wilms Tumor

Wilms tumor (WT), the most prevalent type of renal cancer in children, exhibits overall survival rates exceeding 90%. However, chemotherapy resistance, which occurs in approximately 10% of WT cases, is a major challenge for the treatment of WT, particularly for advanced-stage patients. In this study, we aimed to discover potential mutation markers and drug targets associated with chemotherapy resistance in advanced-stage WT. We performed exome sequencing to detect somatic mutations and molecular targets in 43 WT samples, comprising 26 advanced-stage WTs, of which 7 cases were chemotherapy-resistant. Our analysis revealed four genes (ALPK2, C16orf96, PRKDC, and SVIL) that correlated with chemotherapy resistance and reduced disease-free survival in advanced-stage WT. Additionally, we identified driver mutations in 55 genes within the chemotherapy-resistant group, including 14 druggable cancer driver genes. Based on the mutation profiles of the resistant WT samples, we propose potential therapeutic strategies involving platinum-based agents, PARP inhibitors, and antibiotic/antineoplastic agents. Our findings provide insights into the genetic landscape of WT and offer potential avenues for targeted treatment, particularly for patients with chemotherapy resistance.

FiTMuSiC: leveraging structural and (co)evolutionary data for protein fitness prediction

Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information. We show that FiTMuSiC predicts fitness with high accuracy despite the simplicity of its underlying model: it was among the top predictors on the hydroxymethylbilane synthase (HMBS) target of the sixth round of the Critical Assessment of Genome Interpretation challenge (CAGI6) and performs as well as much more complex deep learning models such as AlphaMissense. To further demonstrate FiTMuSiC's robustness, we compared its predictions with in vitro activity data on HMBS, variant fitness data on human glucokinase (GCK), and variant deleteriousness data on HMBS and GCK. These analyses further confirm FiTMuSiC's qualities and accuracy, which compare favorably with those of other predictors. Additionally, FiTMuSiC returns two scores that separately describe the functional and structural effects of the variant, thus providing mechanistic insight into why the variant leads to fitness loss or gain. We also provide an easy-to-use webserver at https://babylone.ulb.ac.be/FiTMuSiC , which is freely available for academic use and does not require any bioinformatics expertise, which simplifies the accessibility of our tool for the entire scientific community.

Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma

The poor prognosis of glioma patients brought attention to the need for effective therapeutic approaches for precision therapy. Here, we deployed algorithms relying on network medicine and artificial intelligence to design the framework for subtype-specific target identification and drug response prediction in glioma. We identified the driver mutations that were differentially expressed in each subtype of lower-grade glioma and glioblastoma multiforme and were linked to cancer-specific processes. Driver mutations that were differentially expressed were also subjected to subtype-specific disease module identification. The drugs from the drug bank database were retrieved to target these disease modules. However, the efficacy of anticancer drugs depends on the molecular profile of the cancer and varies among cancer patients due to intratumor heterogeneity. Hence, we developed a deep-learning-based drug response prediction framework using the experimental drug screening data. Models for 30 drugs that can target the disease module were developed, where drug response measured by IC50 was considered a response and gene expression and mutation data were considered predictor variables. The model construction consists of three steps: feature selection, data integration, and classification. We observed the consistent performance of the models in training, test, and validation datasets. Drug responses were predicted for particular cell lines derived from distinct subtypes of gliomas. We found that subtypes of gliomas respond differently to the drug, highlighting the importance of subtype-specific drug response prediction. Therefore, the development of personalized therapy by integrating network medicine and a deep learning-based approach can lead to cancer-specific treatment and improved patient care.

Early Detection of Molecular Residual Disease and Risk Stratification for Children with Acute Myeloid Leukemia via Circulating Tumor DNA

PURPOSE

Patient-tailored minimal residual disease (MRD) monitoring based on circulating tumor DNA (ctDNA) sequencing of leukemia-specific mutations enables early detection of relapse for pre-emptive treatment, but its utilization in pediatric acute myelogenous leukemia (AML) is scarce. Thus, we aim to examine the role of ctDNA as a prognostic biomarker in monitoring response to the treatment of pediatric AML.

EXPERIMENTAL DESIGN

A prospective longitudinal study with 50 children with AML was launched, and sequential bone marrow (BM) and matched plasma samples were collected. The concordance of mutations by next-generation sequencing-based BM-DNA and ctDNA was evaluated. In addition, progression-free survival (PFS) and overall survival (OS) were estimated.

RESULTS

In 195 sample pairs from 50 patients, the concordance of leukemia-specific mutations between ctDNA and BM-DNA was 92.8%. Patients with undetectable ctDNA were linked to improved OS and PFS versus detectable ctDNA in the last sampling (both P < 0.001). Patients who cleared their ctDNA post three cycles of treatment had similar PFS compared with persistently negative ctDNA (P = 0.728). In addition, patients with >3 log reduction but without clearance in ctDNA were associated with an improved PFS as were patients with ctDNA clearance (P = 0.564).

CONCLUSIONS

Thus, ctDNA-based MRD monitoring appears to be a promising option to complement the overall assessment of pediatric patients with AML, wherein patients with continuous ctDNA negativity have the option for treatment de-escalation in subsequent therapy. Importantly, patients with >3 log reduction but without clearance in ctDNA may not require an aggressive treatment plan due to improved survival, but this needs further study to delineate.