Pan-cancer analysis of whole genomes

The evolutionary theory of cancer: challenges and potential solutions

The clonal evolution model of cancer was developed in the 1950s-1970s and became central to cancer biology in the twenty-first century, largely through studies of cancer genetics. Although it has proven its worth, its structure has been challenged by observations of phenotypic plasticity, non-genetic forms of inheritance, non-genetic determinants of clone fitness and non-tree-like transmission of genes. There is even confusion about the definition of a clone, which we aim to resolve. The performance and value of the clonal evolution model depends on the empirical extent to which evolutionary processes are involved in cancer, and on its theoretical ability to account for those evolutionary processes. Here, we identify limits in the theoretical performance of the clonal evolution model and provide solutions to overcome those limits. Although we do not claim that clonal evolution can explain everything about cancer, we show how many of the complexities that have been identified in the dynamics of cancer can be integrated into the model to improve our current understanding of cancer.

Deep learning in the overall process of implant prosthodontics: A state-of-the-art review

Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article mainly describes the research progress of deep learning in the whole process of dental implant prosthodontics. It analyzes the limitations of current research, and looks forward to the future development direction.

The 30th Annual Prostate Cancer Foundation Scientific Retreat Report

BACKGROUND

The 30th Annual Prostate Cancer Foundation (PCF) Scientific Retreat was held at the Omni La Costa Resort in Carlsbad, CA, from October 26 to 28, 2023. A hybrid component was included for virtual attendees.

METHODS

The Annual PCF Scientific Retreat is a leading international scientific conference focused on pioneering, unpublished, and impactful studies across the spectrum of basic through clinical prostate cancer research, as well as research from related fields with significant potential for improving prostate cancer research and patient outcomes.

RESULTS

The 2023 PCF Retreat concentrated on key areas of research, including: (i) the biology of cancer stem cells and prostate cancer lineage plasticity; (ii) mechanisms of treatment resistance; (iii) emerging AI applications in diagnostic medicine; (iv) analytical and computational biology approaches in cancer research; (v) the role of nerves in prostate cancer; (vi) the biology of prostate cancer bone metastases; (vii) the contribution of ancestry and genomics to prostate cancer disparities; (viii) prostate cancer 3D genomics; (ix) progress in new targets and treatments for prostate cancer; (x) the biology and translational applications of tumor extracellular vesicles; (xi) updates from PCF TACTICAL Award teams; (xii) novel platforms for small molecule molecular glues and binding inhibitors; and (xiii) diversity, equity and inclusion strategies for advancing cancer care equity.

CONCLUSIONS

This meeting report summarizes the presentations and discussions from the 2023 PCF Scientific Retreat. We hope that sharing this information will deepen our understanding of current and emerging research and drive future advancements in prostate cancer patient care.

Multimodal Spatial Profiling Reveals Immune Suppression and Microenvironment Remodeling in Fallopian Tube Precursors to High-Grade Serous Ovarian Carcinoma

High-Grade Serous Ovarian Cancer (HGSOC) originates from fallopian tube (FT) precursors. However, the molecular changes that occur as precancerous lesions progress to HGSOC are not well understood. To address this, we integrated high-plex imaging and spatial transcriptomics to analyze human tissue samples at different stages of HGSOC development, including p53 signatures, serous tubal intraepithelial carcinomas (STIC), and invasive HGSOC. Our findings reveal immune modulating mechanisms within precursor epithelium, characterized by chromosomal instability, persistent interferon (IFN) signaling, and dysregulated innate and adaptive immunity. FT precursors display elevated expression of MHC-class I, including HLA-E, and IFN-stimulated genes, typically linked to later-stage tumorigenesis. These molecular alterations coincide with progressive shifts in the tumor microenvironment, transitioning from immune surveillance in early STICs to immune suppression in advanced STICs and cancer. These insights identify potential biomarkers and therapeutic targets for HGSOC interception and clarify the molecular transitions from precancer to cancer.

nSMase2-mediated exosome secretion shapes the tumor microenvironment to immunologically support pancreatic cancer

The pleiotropic roles of nSMase2-generated ceramide include regulation of intracellular ceramide signaling and exosome biogenesis. We investigated the effects of eliminating nSMase2 on early and advanced PDA, including its influence on the microenvironment. Employing the KPC mouse model of pancreatic cancer, we demonstrate that pancreatic epithelial nSMase2 ablation reduces neoplasia and promotes a PDA subtype switch from aggressive basal-like to classical. nSMase2 elimination prolongs survival of KPC mice, hinders vasculature development, and fosters a robust immune response. nSMase2 loss leads to recruitment of cytotoxic T cells, N1-like neutrophils, and abundant infiltration of anti-tumorigenic macrophages in the pancreatic preneoplastic microenvironment. Mechanistically, we demonstrate that nSMase2-expressing PDA cell small extracellular vesicles (sEVs) reduce survival of KPC mice; PDA cell sEVs generated independently of nSMase2 prolong survival of KPC mice and reprogram macrophages to a proinflammatory phenotype. Collectively, our study highlights previously unappreciated opposing roles for exosomes, based on biogenesis pathway, during PDA progression.

Graphical abstract

Integrative identification of non-coding regulatory regions driving metastatic prostate cancer

Large-scale sequencing efforts have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of variants occur within non-coding genomic regions. We designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Applying this framework to sequencing data from a large prostate cancer patient cohort revealed a large set of candidate drivers. We used (1) in silico analyses, (2) massively parallel reporter assays, and (3) in vivo CRISPR interference screens to systematically validate metastatic castration-resistant prostate cancer (mCRPC) drivers. One identified enhancer region, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of the U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. SF3A1 and CCDC157 promote tumor growth in vivo. We nominated a number of transcription factors, notably SOX6, to regulate expression of SF3A1 and CCDC157. Our integrative approach enables the systematic detection of non-coding regulatory regions that drive human cancers.

Ancestry and somatic profile predict acral melanoma origin and prognosis

Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, here we characterise the genome and transcriptome of 128 acral melanoma tumours from 96 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF, NRAS or NF1. While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower number of structural variants. These BRAF-mutated tumours have a transcriptional profile similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and transcriptional profiling defined three expression clusters; these characteristics were associated with overall survival. We highlight novel low-frequency drivers, such as SPHKAP, which correlate with a distinct genomic profile and clinical characteristics. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

Deciphering gene expression patterns using large-scale transcriptomic data and its applications

Gene expression varies stochastically across genders, racial groups, and health statuses. Deciphering these patterns is crucial for identifying informative genes, classifying samples, and understanding diseases like cancer. This study analyzes 11,252 bulk RNA-seq samples to explore expression patterns of 19,156 genes, including 10,512 cancer tissue samples and 740 normal samples. Additionally, 4,884 single-cell RNA-seq samples are examined. Statistical analysis using 16 probability distributions shows that normal samples display a wider range of distributions compared to cancer samples. Cancer samples tend to favor asymmetric distributions such as generalized extreme value, logarithmic normal, and Gaussian mixture distributions. In contrast, certain genes in normal samples exhibit symmetric distributions. Remarkably, more than 95.5% of genes exhibit non-normal distributions, which challenges traditional assumptions. Furthermore, distributions differ significantly between bulk and single-cell RNA-seq data. Many cancer driver genes exhibit distinct distribution patterns across sample types, suggesting potential for gene selection and classification based on distribution characteristics. A novel skewness-based metric is proposed to quantify distribution variation across datasets, showing genes with significant skewness differences have biological relevance. Finally, an improved naïve Bayes method incorporating gene-specific distributions demonstrates superior performance in simulations over traditional methods. This work enhances understanding of gene expression and its application in omics-based gene selection and sample classification.

Mediating kinase activity in Ras-mutant cancer: potential for an individualised approach?

It is widely acknowledged that there is a considerable number of oncogenic mutations within the Ras superfamily of small GTPases which are the driving force behind a multitude of cancers. Ras proteins mediate a plethora of kinase pathways, including the MAPK, PI3K, and Ral pathways. Since Ras was considered undruggable until recently, pharmacological targeting of pathways downstream of Ras has been attempted to varying success, though drug resistance has often proven an issue. Nuances between kinase pathway activation in the presence of various Ras mutants are thought to contribute to the resistance, however, the reasoning behind activation of different pathways in different Ras mutational contexts is yet to be fully elucidated. Indeed, such disparities often depend on cancer type and disease progression. However, we are in a revolutionary age of Ras mutant targeted therapy, with direct-targeting KRAS-G12C inhibitors revolutionising the field and achieving FDA-approval in recent years. However, these are only beneficial in a subset of patients. Approximately 90% of Ras-mutant cancers are not KRAS-G12C mutant, and therefore raises the question as to whether other distinct amino acid substitutions within Ras may one day be targetable in a similar manner, and indeed whether better understanding of the downstream pathways these various mutants activate could further improve therapy. Here, we discuss the favouring of kinase pathways across an array of Ras-mutant oncogenic contexts and assess recent advances in pharmacological targeting of various Ras mutants. Ultimately, we will examine the utility of individualised pharmacological approaches to Ras-mediated cancer.

An elevated rate of whole-genome duplications in cancers from Black patients

In the United States, Black individuals have higher rates of cancer mortality than any other racial group. Here, we examine chromosome copy number changes in cancers from more than 1800 self-reported Black patients. We find that tumors from self-reported Black patients are significantly more likely to exhibit whole-genome duplications (WGDs), a genomic event that enhances metastasis and aggressive disease, compared to tumors from self-reported white patients. This increase in WGD frequency is observed across multiple cancer types, including breast, endometrial, and lung cancer, and is associated with shorter patient survival. We further demonstrate that combustion byproducts are capable of inducing WGDs in cell culture, and cancers from self-reported Black patients exhibit mutational signatures consistent with exposure to these carcinogens. In total, these findings identify a type of genomic alteration that is associated with environmental exposures and that may influence racial disparities in cancer outcomes.

Prognostic determinants in cancer survival: a multidimensional evaluation of clinical and genetic factors across 10 cancer types in the participants of Genomics England's 100,000 Genomes Project

BACKGROUND

Cancer is a complex disease, caused and impacted by a combination of genetic, demographic, clinical, environmental and lifestyle factors. Analysis of cancer characteristics, risk factors, treatment options and the heterogeneity across cancer types has been the focus of medical research for years. The aim of this study is to describe and summarise genetic, clinicopathological, behavioural and demographic characteristics and their differences across ten common cancer types and evaluate their impact on overall survival outcomes.

METHODS

This study included data from 9977 patients with bladder, breast, colorectal, endometrial, glioma, leukaemia, lung, ovarian, prostate, and renal cancers. Genetic data collected through the 100,000 Genomes Project was linked with clinical and demographic data provided by the National Cancer Registration and Analysis Service (NCRAS), Hospital Episode Statistics (HES) and Office for National Statistics (ONS). Descriptive and Kaplan Meier survival analyses were performed to visualise similarities and differences across cancer types. Cox proportional hazards regression models were applied to identify statistically significant prognostic factor associations with overall survival.

RESULTS

161 clinical and 124 genetic factors were evaluated for prognostic association with overall survival. Of these, 116 unique factors were found to have significant prognostic effect for overall survival across ten cancer types when adjusted for age, sex and stage. The findings confirmed prognostic associations with overall survival identified in previous studies in factors such as multimorbidity, tumour mutational burden, and mutations in genes BRAF, CDH1, NF1, NRAS, PIK3CA, PTEN, TP53. The results also identified new prognostic associations with overall survival in factors such as mental health conditions, female health-related conditions, previous hospital encounters and mutations in genes FANCE, FBXW7, GATA3, MSH6, PTPN11, RB1, RNF43.

CONCLUSION

This study provides a comprehensive view of clinicopathological and genetic prognostic factors across different cancer types and draws attention to less commonly known factors which might help produce more precise prognosis and survival estimates. The results from this study contribute to the understanding of cancer disease and could be used by researchers to develop complex prognostic models, which in turn could help predict cancer prognosis more accurately and improve patient outcomes.

An integrated pan-cancer assessment of prognosis, immune infiltration, and immunotherapy response for B7 family using multi-omics data

AIMS

B7 molecules (B7s) are crucial synergistic signals for effective immune surveillance against tumor cells. While previous studies have explored the association between the B7 family and cancer, most have been limited to specific genes or cancer subtypes.

MAIN METHODS

Our study utilized multi-omics data to investigate potential correlations between B7s expression (B7s exp.) and prognosis, clinicopathological features, somatic mutations (SMs), copy number variations (CNVs), immune characteristics, tumor microenvironment (TME), microsatellite instability, tumor mutation burden, immune checkpoint gene (ICG), and drug responsiveness in TCGA tumors. Furthermore, the connection between B7s exp. and immunotherapy (IT) performance assessed in various validated datasets. Following this, immune infiltration analysis (IIA) was conducted based on B7s exp., CNVs, or SMs in bladder cancer (BLCA), complemented by real-time PCR (RT-PCR) and protein confirmation of B7-H3.

KEY FINDINGS

Across most cancer types, B7s exp. was related to prognosis, clinicopathological characteristics, mutations, CNVs, ICG, TMB, TME. The examination of sensitivity to anticancer drugs unveiled correlations between B7 molecules and different drug sensitivities. Specific B7s exp. patterns were linked to the clinical effectiveness of IT. Using GSEA, several enriched immune-related functions and pathways were identified. Particularly in BLCA, IIA revealed significant connections between B7 CNVs, mutation status, and various immune cell infiltrates. RT-PCR confirmed elevated B7-H3 gene levels in BLCA tumor tissues.

SIGNIFICANCE

This study confirmed the significance of B7s exp. and genomic changes in predicting outcomes and treatment across different cancer types. Moreover, they indicate a critical function of B7s in BLCA and their potential as IT biomarkers.

Worldwide analysis of actionable genomic alterations in lung cancer and targeted pharmacogenomic strategies

Based on data from the Global Cancer Statistics 2022, lung cancer stands as the most lethal cancer worldwide, with age-adjusted incidence and mortality rates of 23.6 and 16.9 per 100,000 people, respectively. Despite significant strides in precision oncology driven by large-scale international research consortia, there remains a critical need to deepen our understanding of the genomic landscape across diverse racial and ethnic groups. To address this challenge, we performed comprehensive in silico analyses and data mining to identify pathogenic variants in genes that drive lung cancer. We subsequently calculated the allele frequencies and assessed the deleteriousness of these oncogenic variants among populations such as African, Amish, Ashkenazi Jewish, East and South Asian, Finnish and non-Finnish European, Latino, and Middle Eastern. Our analysis examined 117,707 variants within 86 lung cancer-associated genes across 75,109 human genomes, uncovering 8042 variants that are known or predicted to be pathogenic. We prioritized variants based on their allele frequencies and deleterious scores, and identified those with potential significance for response to anti-cancer therapies through in silico drug simulations, current clinical pharmacogenomic guidelines, and ongoing late-stage clinical trials targeting lung cancer-driving proteins. In conclusion, it is crucial to unite global efforts to create public health policies that emphasize prevention strategies and ensure access to clinical trials, pharmacogenomic testing, and cancer research for these groups in developed nations.

Integrative computational analyses implicate regulatory genomic elements contributing to spina bifida

Purpose

Spina bifida (SB) arises from complex genetic interactions that converge to interfere with neural tube closure. Understanding the precise patterns conferring SB risk requires a deep exploration of the genomic networks and molecular pathways that govern neurulation. This study aims to delineate genome-wide regulatory signatures underlying SB pathophysiology.

Methods

An untargeted, genome-wide approach was used to interrogate regulatory regions for rare single-nucleotide and copy-number variants (rSNVs and rCNVs, respectively) predicted to affect gene expression, comparing results from SB patients with healthy controls. Qualifying variants were subjected to a deep learning prioritization framework to identify the most functionally relevant variants, as well as the likely target genes affected by these rare regulatory variants.

Results

This ensemble of computational tools identified rSNVs in specific transcription factor binding sites (TFBSs) that distinguish SB cases from controls. rSNV enrichment was found in specific TFBSs, especially CCCTC-binding factor binding sites. These TFBSs were subjected to a deep learning prioritization framework to identify the most functionally relevant variants, as well as the likely target genes affected by these rSNVs. The functional pathways or modules implicated by these regulated genes serve protein transport, cilia assembly, and central nervous system development. Moreover, the detected rare copy-number variants in SB cases are positioned to disrupt gene regulatory networks and alter 3-dimensional genomic architectures, including brain-specific enhancers and topologically associated domain boundaries of relevant cell types.

Conclusion

Our study provides a resource for identifying and interpreting genomic regulatory DNA variant contributions to human SB genetic predisposition.

Arrest and Attack: Microtubule-Targeting Agents and Oncolytic Viruses Employ Complementary Mechanisms to Enhance Anti-Tumor Therapy Efficacy

Oncolytic viruses (OVs) are promising cancer immunotherapy agents that stimulate anti-tumor immunity through the preferential infection and killing of tumor cells. OVs are currently under limited clinical usage, due in part to their restricted efficacy as monotherapies. Current efforts for enhancement of the therapeutic potency of OVs involve their combination with other therapy modalities, aiming at the concomitant exploitation of complementary tumor weaknesses. In this context, microtubule-targeting agents (MTAs) pose as an enticing option, as they perturb microtubule dynamics and function, induce cell-cycle arrest, and cause mitotic cell death. MTAs induce therapeutic benefit through cancer-cell-autonomous and non-cell-autonomous mechanisms and are a main component of the standard of care for different malignancies. However, off-target effects and acquired resistance involving distinct cellular and molecular mechanisms may limit the overall efficacy of MTA-based therapy. When combined, OVs and MTAs may enhance therapeutic efficacy through increases in OV infection and immunogenic cell death and a decreased probability of acquired resistance. In this review, we introduce OVs and MTAs, describe molecular features of their activity in cancer cells, and discuss studies and clinical trials in which the combination has been tested.

ONCOLINER: A new solution for monitoring, improving, and harmonizing somatic variant calling across genomic oncology centers

The characterization of somatic genomic variation associated with the biology of tumors is fundamental for cancer research and personalized medicine, as it guides the reliability and impact of cancer studies and genomic-based decisions in clinical oncology. However, the quality and scope of tumor genome analysis across cancer research centers and hospitals are currently highly heterogeneous, limiting the consistency of tumor diagnoses across hospitals and the possibilities of data sharing and data integration across studies. With the aim of providing users with actionable and personalized recommendations for the overall enhancement and harmonization of somatic variant identification across research and clinical environments, we have developed ONCOLINER. Using specifically designed mosaic and tumorized genomes for the analysis of recall and precision across somatic SNVs, insertions or deletions (indels), and structural variants (SVs), we demonstrate that ONCOLINER is capable of improving and harmonizing genome analysis across three state-of-the-art variant discovery pipelines in genomic oncology.

Mitochondrial reprogramming by activating OXPHOS via glutamine metabolism in African American patients with bladder cancer

Bladder cancer (BLCA) mortality is higher in African American (AA) patients compared with European American (EA) patients, but the molecular mechanism underlying race-specific differences are unknown. To address this gap, we conducted comprehensive RNA-Seq, proteomics, and metabolomics analysis of BLCA tumors from AA and EA. Our findings reveal a distinct metabolic phenotype in AA BLCA characterized by elevated mitochondrial oxidative phosphorylation (OXPHOS), particularly through the activation of complex I. The results provide insight into the complex I activation-driven higher OXPHOS activity resulting in glutamine-mediated metabolic rewiring and increased disease progression, which was also confirmed by [U]13C-glutamine tracing. Mechanistic studies further demonstrate that knockdown of NDUFB8, one of the components of complex I in AA BLCA cells, resulted in reduced basal respiration, ATP production, GLS1 expression, and proliferation. Moreover, preclinical studies demonstrate the therapeutic potential of targeting complex I, as evidenced by decreased tumor growth in NDUFB8-depleted AA BLCA tumors. Additionally, genetic and pharmacological inhibition of GLS1 attenuated mitochondrial respiration rates and tumor growth potential in AA BLCA. Taken together, these findings provide insight into BLCA disparity for targeting GLS1-Complex I for future therapy.

HYENA detects oncogenes activated by distal enhancers in cancer

Somatic structural variations (SVs) in cancer can shuffle DNA content in the genome, relocate regulatory elements, and alter genome organization. Enhancer hijacking occurs when SVs relocate distal enhancers to activate proto-oncogenes. However, most enhancer hijacking studies have only focused on protein-coding genes. Here, we develop a computational algorithm 'HYENA' to identify candidate oncogenes (both protein-coding and non-coding) activated by enhancer hijacking based on tumor whole-genome and transcriptome sequencing data. HYENA detects genes whose elevated expression is associated with somatic SVs by using a rank-based regression model. We systematically analyze 1146 tumors across 25 types of adult tumors and identify a total of 108 candidate oncogenes including many non-coding genes. A long non-coding RNA TOB1-AS1 is activated by various types of SVs in 10% of pancreatic cancers through altered 3-dimensional genome structure. We find that high expression of TOB1-AS1 can promote cell invasion and metastasis. Our study highlights the contribution of genetic alterations in non-coding regions to tumorigenesis and tumor progression.

RCC1 depletion drives protein transport defects and rupture in micronuclei

Micronuclei (MN) are a commonly used marker of chromosome instability that form when missegregated chromatin recruits its own nuclear envelope (NE) after mitosis. MN frequently rupture, which results in genome instability, upregulation of metastatic genes, and increased immune signaling. MN rupture is linked to NE defects, but the cause of these defects is poorly understood. Previous work from our lab found that chromosome identity correlates with rupture timing for small MN, i.e. MN containing a short chromosome, with more euchromatic chromosomes forming more stable MN with fewer nuclear lamina gaps. Here we demonstrate that histone methylation promotes rupture and nuclear lamina defects in small MN. This correlates with increased MN size, and we go on to find that all MN have a constitutive nuclear export defect that drives MN growth and nuclear lamina gap expansion, making the MN susceptible to rupture. We demonstrate that these export defects arise from decreased RCC1 levels in MN and that additional loss of RCC1 caused by low histone methylation in small euchromatic MN results in additional import defects that suppress nuclear lamina gaps and MN rupture. Through analysis of mutational signatures associated with early and late rupturing chromosomes in the Pan-Cancer Analysis of Whole Genomes (PCAWG) dataset, we identify an enrichment of APOBEC and DNA polymerase E hypermutation signatures in chromothripsis events on early and mid rupturing chromosomes, respectively, suggesting that MN rupture timing could determine the landscape of structural variation in chromothripsis. Our study defines a new model of MN rupture where increased MN growth, caused by defects in protein export, drives gaps in nuclear lamina organization that make the MN susceptible to membrane rupture with long-lasting effects on genome architecture.

A Multiplex Assay for Fast PIK3CA Hotspot Mutation Characterization in a Single Specimen by 3-Color Digital PCR Analysis

BACKGROUND

Activating mutations in the phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) gene have been detected often in solid tumors. Targeted therapy for mutant PIK3CA is now available in the clinic, making molecular diagnostics pivotal. Our aim was to design a multiplex digital PCR (dPCR) assay to evaluate the 4 most common PIK3CA hotspot mutations simultaneously to characterize and quantify these in liquid biopsies.

METHODS

A multiplex assay was developed to detect exon 9 p.E542K and p.E545K mutations, and exon 20 p.H1047L and p.H1047R mutations using the Stilla 3-color dPCR Naica system. The assay was evaluated on stock and pre-amplified DNA from cell lines with the above mutations as single and pooled samples, and on cell-free DNA (cfDNA) from healthy blood donors (HBDs) and breast cancer patients, to determine detection thresholds and diagnostic accuracy.

RESULTS

The assay distinguished all 4 PIK3CA mutations in (cf)DNA, and also when dual mutations were present. Detection thresholds of stock and pre-amplified cfDNA samples were 0.11 and 0.40 copies/uL (cp/uL) for mutant copies concentration, and 0.003% and 0.68% for variant allele frequencies (VAFs), respectively. The assay confirmed the PIK3CA (mutation) status as defined by targeted next-generation sequencing (NGS) in 82 out of 96 patients that were mutant for PIK3CA, and in 11 out of 12 patients with wild-type PIK3CA.

CONCLUSIONS

Our designed multiplex dPCR assay detected PIK3CA mutations with high accuracy in stock and pre-amplified cfDNA. Furthermore, it is affordable and demands less cfDNA input when compared to available uniplex dPCR assays and NGS analyses.

Perovskite Probe-Based Machine Learning Imaging Model for Rapid Pathologic Diagnosis of Cancers

Accurately distinguishing tumor cells from normal cells is a key issue in tumor diagnosis, evaluation, and treatment. Fluorescence-based immunohistochemistry as the standard method faces the inherent challenges of the heterogeneity of tumor cells and the lack of big data analysis of probing images. Here, we have demonstrated a machine learning-driven imaging method for rapid pathological diagnosis of five types of cancers (breast, colon, liver, lung, and stomach) using a perovskite nanocrystal probe. After conducting the bioanalysis of survivin expression in five different cancers, high-efficiency perovskite nanocrystal probes modified with the survivin antibody can recognize the cancer tissue section at the single cell level. The tumor to normal (T/N) ratio is 10.3-fold higher than that of a conventional fluorescent probe, which can successfully differentiate between tumors and adjacent normal tissues within 10 min. The features of the fluorescence intensity and pathological texture morphology have been extracted and analyzed from 1000 fluorescence images by machine learning. The final integrated decision model makes the area under the receiver operating characteristic curve (area under the curve) value of machine learning classification of breast, colon, liver, lung, and stomach above 90% while predicting the tumor organ of 92% of positive patients. This method demonstrates a high T/N ratio probe in the precise diagnosis of multiple cancers, which will be good for improving the accuracy of surgical resection and reducing cancer mortality.

Image-based deep learning model using DNA methylation data predicts the origin of cancer of unknown primary

Cancer of unknown primary (CUP) is a rare type of metastatic cancer in which the origin of the tumor is unknown. Since the treatment strategy for patients with metastatic tumors depends on knowing the primary site, accurate identification of the origin site is important. Here, we developed an image-based deep-learning model that utilizes a vision transformer algorithm for predicting the origin of CUP. Using DNA methylation dataset of 8,233 primary tumors from The Cancer Genome Atlas (TCGA), we categorized 29 cancer types into 18 organ classes and extracted 2,312 differentially methylated CpG sites (DMCs) from non-squamous cancer group and 420 DMCs from squamous cell cancer group. Using these DMCs, we created organ-specific DNA methylation images and used them for model training and testing. Model performance was evaluated using 394 metastatic cancer samples from TCGA (TCGA-meta) and 995 samples (693 primary and 302 metastatic cancers) obtained from 20 independent external studies. We identified that the DNA methylation image reveals a distinct pattern based on the origin of cancer. Our model achieved an overall accuracy of 96.95 % in the TCGA-meta dataset. In the external validation datasets, our classifier achieved overall accuracies of 96.39 % and 94.37 % in primary and metastatic tumors, respectively. Especially, the overall accuracies for both primary and metastatic samples of non-squamous cell cancer were exceptionally high, with 96.79 % and 96.85 %, respectively.

The genomic landscape of 2,023 colorectal cancers

Colorectal carcinoma (CRC) is a common cause of mortality1, but a comprehensive description of its genomic landscape is lacking2-9. Here we perform whole-genome sequencing of 2,023 CRC samples from participants in the UK 100,000 Genomes Project, thereby providing a highly detailed somatic mutational landscape of this cancer. Integrated analyses identify more than 250 putative CRC driver genes, many not previously implicated in CRC or other cancers, including several recurrent changes outside the coding genome. We extend the molecular pathways involved in CRC development, define four new common subgroups of microsatellite-stable CRC based on genomic features and show that these groups have independent prognostic associations. We also characterize several rare molecular CRC subgroups, some with potential clinical relevance, including cancers with both microsatellite and chromosomal instability. We demonstrate a spectrum of mutational profiles across the colorectum, which reflect aetiological differences. These include the role of Escherichia colipks+ colibactin in rectal cancers10 and the importance of the SBS93 signature11-13, which suggests that diet or smoking is a risk factor. Immune-escape driver mutations14 are near-ubiquitous in hypermutant tumours and occur in about half of microsatellite-stable CRCs, often in the form of HLA copy number changes. Many driver mutations are actionable, including those associated with rare subgroups (for example, BRCA1 and IDH1), highlighting the role of whole-genome sequencing in optimizing patient care.

Targeting of human cancer stem cells predicts efficacy and toxicity of FDA-approved oncology drugs

Cancer remains the leading cause of death worldwide with approved oncology drugs continuing to have heterogenous patient responses and accompanied adverse effects (AEs) that limits effectiveness. Here, we examined >100 FDA-approved oncology drugs in the context of stemness using a surrogate model of transformed human pluripotent cancer stem cells (CSCs) vs. healthy stem cells (hSCs) capable of distinguishing abnormal self-renewal and differentiation. Although a proportion of these drugs had no effects (inactive), a larger portion affected CSCs (active), and a unique subset preferentially affected CSCs over hSCs (selective). Single cell gene expression and protein profiling of each drug's FDA recognized target provided a molecular correlation of responses in CSCs vs. hSCs. Uniquely, drugs selective for CSCs demonstrated clinical efficacy, measured by overall survival, and reduced AEs. Our findings reveal that while unintentional, half of anticancer drugs are active against CSCs and associated with improved clinical outcomes. Based on these findings, we suggest ability to target CSC targeting should be included as a property of early onco-therapeutic development.

Defining the Genomic Landscape of Diffuse Sclerosing Papillary Thyroid Carcinoma: Prognostic Implications of RET Fusions

BACKGROUND

Diffuse sclerosing papillary thyroid carcinoma (DSPTC) is an aggressive histopathologic subtype of papillary thyroid carcinoma. Correlation between genotype and phenotype has not been comprehensively described. This study aimed to describe the genomic landscape of DSPTC comprehensively using next-generation sequencing (NGS), analyze the prognostic implications of different mutations, and identify potential molecular treatment targets.

METHODS

Tumor tissue was available for 41 DSPTC patients treated at Memorial Sloan Kettering Cancer Center between 2004 and 2021. After DNA extraction, NGS was performed using the Memorial Sloan Kettering Integrated Mutation Profiling of Actionable Cancer Targets platform, which sequences 505 critical cancer genes. Clinicopathologic characteristics were compared using the chi-square test. The Kaplan-Meier method and log-rank statistics were used to compare outcomes.

RESULTS

The most common mutation was RET fusion, occurring in 32% (13/41) of the patients. Other oncologic drivers occurred in 68% (28/41) of the patients, including 8 BRAFV600E mutations (20%) and 4 USP8 mutations (10%), which have not been described in thyroid malignancy previously. Patients experienced RET fusion-positive tumors at a younger age than other drivers, with more aggressive histopathologic features and more advanced T stage (p = 0.019). Patients who were RET fusion-positive had a significantly poorer 5-year recurrence-free survival probability than those with other drivers (46% vs 84%; p = 0.003; median follow-up period, 45 months). In multivariable analysis, RET fusion was the only independent risk factor for recurrence (hazard ratio [HR], 7.69; p = 0.017).

CONCLUSION

Gene-sequencing should be strongly considered for recurrent DSPTC due to significant prognostic and treatment implications of RET fusion identification. The novel finding of USP8 mutation in DSPTC requires further investigation into its potential as a driver mutation.

Identifying cooperating cancer driver genes in individual patients through hypergraph random walk

OBJECTIVE

Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies.

METHODS

Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients.

RESULTS

The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments.

CONCLUSION

We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.

Analysis of 10,478 cancer genomes identifies candidate driver genes and opportunities for precision oncology

Tumor genomic profiling is increasingly seen as a prerequisite to guide the treatment of patients with cancer. To explore the value of whole-genome sequencing (WGS) in broadening the scope of cancers potentially amenable to a precision therapy, we analysed whole-genome sequencing data on 10,478 patients spanning 35 cancer types recruited to the UK 100,000 Genomes Project. We identified 330 candidate driver genes, including 74 that are new to any cancer. We estimate that approximately 55% of patients studied harbor at least one clinically relevant mutation, predicting either sensitivity or resistance to certain treatments or clinical trial eligibility. By performing computational chemogenomic analysis of cancer mutations we identify additional targets for compounds that represent attractive candidates for future clinical trials. This study represents one of the most comprehensive efforts thus far to identify cancer driver genes in the real world setting and assess their impact on informing precision oncology.

Comprehensive cross cancer analyses reveal mutational signature cancer specificity

Mutational signatures refer to distinct patterns of DNA mutations that occur in a specific context or under certain conditions. It is a powerful tool to describe cancer etiology. We conducted a study to show cancer heterogeneity and cancer specificity from the aspect of mutational signatures through collinearity analysis and machine learning techniques. Through thorough training and independent validation, our results show that while the majority of the mutational signatures are distinct, similarities between certain mutational signature pairs can be observed through both mutation patterns and mutational signature abundance. The observation can potentially assist to determine the etiology of yet elusive mutational signatures. Further analysis using machine learning approaches demonstrated moderate mutational signature cancer specificity. Skin cancer among all cancer types demonstrated the strongest mutational signature specificity.

Identification of RNMT as an immunotherapeutic and prognostic biomarker: From pan-cancer analysis to lung squamous cell carcinoma validation

BACKGROUND

Dysregulation of RNA guanine-7 methyltransferase (RNMT) plays a crucial role in the tumor progression and immune responses. However, the detailed role of RNMT in pan-cancer is still unknown.

METHODS

Bulk transcriptomic data of pan-cancer were obtained from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases. Single-cell transcriptomic and proteomics data of lung squamous cell carcinoma (LUSC) were analyzed in the Tumor Immune Single-cell Hub 2 (TISCH2) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, respectively. The correlation between RNMT expression and cancer prognosis was analyzed by Cox proportional hazards regression and Kaplan-Meier analyses. The correlation of RNMT expression with common immunoregulators, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), and DNA methyltransferase (DNMT) was analyzed. Additionally, the correlation between RNMT expression and immune infiltration level was evaluated. A total of 1287 machine learning combinations were used to construct prognostic models for LUSC. qRT-PCR and Western blot were used to validate the bioinformatics findings of RNMT upregulation in LUSC.

RESULTS

RNMT was widely expressed across different cancers, with significant correlation to prognosis in cancers such as kidney chromophobe (KICH) (p = 0.0033, HR = 7.12), liver hepatocellular carcinoma (LIHC) (p = 0.01, HR = 1.41), and others. Notably, RNMT participates in the regulation of the tumor microenvironment. RNMT expression positively correlated with immune cell expression (Spearman's rank correlation, p < 0.05). Moreover, RNMT expression was strongly associated with immunoregulators, TMB, MSI, MMR, and DNMT in most cancer types. Notably, RNMT expression displayed excellent prognostic and immunological performance in LUSC. The expression of RNMT was mainly enriched in B cells of LUSC tissues. qRT-PCR and Western blot verified the high expression of RNMT in LUSC.

CONCLUSION

RNMT expression widely correlated with prognosis and immune infiltration in various tumors, especially LUSC. The RNMT detection may provide a new idea for future tumor immune studies and treatment strategies.

ctDNA whole exome sequencing in pancreatic ductal adenocarcinoma unveils organ-dependent metastatic mechanisms and identifies actionable alterations in fast progressing patients

Understanding progression mechanisms and developing new targeted therapies is imperative in pancreatic ductal adenocarcinoma (PDAC). In this study, 80 metastatic PDAC patients were prospectively recruited and divided into discovery (n=37) and validation (n=43) cohorts. Tumor and plasma samples taken at diagnosis were pair analyzed using whole exome sequencing (WES) in patients belonging to the discovery cohort alone. The variant allele frequency (VAF) of KRAS mutations was measured by ddPCR in plasma at baseline and response assessment in all patients. Plasma WES identified at least one pathogenic variant across the cohort, uncovering oncogenic mechanisms, DNA repair, microsatellite instability, and alterations in the TGFb pathway. Interestingly, actionable mutations were mostly found in plasma rather than tissue. Patients with shorter survival showed enrichment in cellular organization regulatory pathways. Through WES we could identify a specific molecular profile of patients with liver metastasis, which exhibited exclusive mutations in genes related to the adaptive immune response pathway, highlighting the importance of the immune system in liver metastasis development. Moreover, KRAS mutations in plasma (both at diagnosis and persistent at follow-up) correlated with shorter progression free survival (PFS). Patients presenting a reduction of over 84.75 % in KRAS VAF at response assessment had similar PFS to KRAS-negative patients. Overall, plasma WES reveals molecular profiles indicative of rapid progression, potentially actionable targets, and associations between adaptive immune response pathway alterations and liver tropism.

Regulation, functional impact, and therapeutic targeting of APOBEC3A in cancer

Enzymes of the apolipoprotein B mRNA editing catalytic polypeptide like (APOBEC) family are cytosine deaminases that convert cytosine to uracil in DNA and RNA. Among these proteins, APOBEC3 sub-family members, APOBEC3A (A3A) and APOBEC3B (A3B), are prominent sources of mutagenesis in cancer cells. The aberrant expression of A3A and A3B in cancer cells leads to accumulation of mutations with specific single-base substitution (SBS) signatures, characterized by C→T and C→G changes, in a number of tumor types. In addition to fueling mutagenesis, A3A and A3B, particularly A3A, induce DNA replication stress, DNA damage, and chromosomal instability through their catalytic activities, triggering a range of cellular responses. Thus, A3A/B have emerged as key drivers of genome evolution during cancer development, contributing to tumorigenesis, tumor heterogeneity, and therapeutic resistance. Yet, the expression of A3A/B in cancer cells presents a cancer vulnerability that can be exploited therapeutically. In this review, we discuss the recent studies that shed light on the mechanisms regulating A3A expression and the impact of A3A in cancer. We also review recent advances in the development of A3A inhibitors and provide perspectives on the future directions of A3A research.

Somatic CpG hypermutation is associated with mismatch repair deficiency in cancer

Somatic hypermutation in cancer has gained momentum with the increased use of tumour mutation burden as a biomarker for immune checkpoint inhibitors. Spontaneous deamination of 5-methylcytosine to thymine at CpG dinucleotides is one of the most ubiquitous endogenous mutational processes in normal and cancer cells. Here, we performed a systematic investigation of somatic CpG hypermutation at a pan-cancer level. We studied 30,191 cancer patients and 103 cancer types and developed an algorithm to identify somatic CpG hypermutation. Across cancer types, we observed the highest prevalence in paediatric leukaemia (3.5%), paediatric high-grade glioma (1.7%), and colorectal cancer (1%). We discovered germline variants and somatic mutations in the mismatch repair complex MutSα (MSH2-MSH6) as genetic drivers of somatic CpG hypermutation in cancer, which frequently converged on CpG sites and TP53 driver mutations. We further observe an association between somatic CpG hypermutation and response to immune checkpoint inhibitors. Overall, our study identified novel cancer types that display somatic CpG hypermutation, strong association with MutSα-deficiency, and potential utility in cancer immunotherapy.

Prognostic genome and transcriptome signatures in colorectal cancers

Colorectal cancer is caused by a sequence of somatic genomic alterations affecting driver genes in core cancer pathways1. Here, to understand the functional and prognostic impact of cancer-causing somatic mutations, we analysed the whole genomes and transcriptomes of 1,063 primary colorectal cancers in a population-based cohort with long-term follow-up. From the 96 mutated driver genes, 9 were not previously implicated in colorectal cancer and 24 had not been linked to any cancer. Two distinct patterns of pathway co-mutations were observed, timing analyses identified nine early and three late driver gene mutations, and several signatures of colorectal-cancer-specific mutational processes were identified. Mutations in WNT, EGFR and TGFβ pathway genes, the mitochondrial CYB gene and 3 regulatory elements along with 21 copy-number variations and the COSMIC SBS44 signature correlated with survival. Gene expression classification yielded five prognostic subtypes with distinct molecular features, in part explained by underlying genomic alterations. Microsatellite-instable tumours divided into two classes with different levels of hypoxia and infiltration of immune and stromal cells. To our knowledge, this study constitutes the largest integrated genome and transcriptome analysis of colorectal cancer, and interlinks mutations, gene expression and patient outcomes. The identification of prognostic mutations and expression subtypes can guide future efforts to individualize colorectal cancer therapy.

Role of Radiology in the Diagnosis and Treatment of Breast Cancer in Women: A Comprehensive Review

Breast cancer remains a leading cause of morbidity and mortality among women worldwide. Early detection and precise diagnosis are critical for effective treatment and improved patient outcomes. This review explores the evolving role of radiology in the diagnosis and treatment of breast cancer, highlighting advancements in imaging technologies and the integration of artificial intelligence (AI). Traditional imaging modalities such as mammography, ultrasound, and magnetic resonance imaging have been the cornerstone of breast cancer diagnostics, with each modality offering unique advantages. The advent of radiomics, which involves extracting quantitative data from medical images, has further augmented the diagnostic capabilities of these modalities. AI, particularly deep learning algorithms, has shown potential in improving diagnostic accuracy and reducing observer variability across imaging modalities. AI-driven tools are increasingly being integrated into clinical workflows to assist in image interpretation, lesion classification, and treatment planning. Additionally, radiology plays a crucial role in guiding treatment decisions, particularly in the context of image-guided radiotherapy and monitoring response to neoadjuvant chemotherapy. The review also discusses the emerging field of theranostics, where diagnostic imaging is combined with therapeutic interventions to provide personalized cancer care. Despite these advancements, challenges such as the need for large annotated datasets and the integration of AI into clinical practice remain. The review concludes that while the role of radiology in breast cancer management is rapidly evolving, further research is required to fully realize the potential of these technologies in improving patient outcomes.

MoNETA: MultiOmics Network Embedding for SubType Analysis

Cells are complex systems whose behavior emerges from a huge number of reactions taking place within and among different molecular districts. The availability of bulk and single-cell omics data fueled the creation of multi-omics systems biology models capturing the dynamics within and between omics layers. Powerful modeling strategies are needed to cope with the increased amount of data to be interrogated and the relative research questions. Here, we present MultiOmics Network Embedding for SubType Analysis (MoNETA) for fast and scalable identification of relevant multi-omics relationships between biological entities at the bulk and single-cells level. We apply MoNETA to show how glioma subtypes previously described naturally emerge with our approach. We also show how MoNETA can be used to identify cell types in five multi-omic single-cell datasets.

Unraveling the impact of lysosomal dysfunction on myeloproliferative neoplasm

BACKGROUND

Lysosomal dysfunction (LD) impacts cytokine regulation, inflammation, and immune responses, influencing the development and progression of cancer. Inflammation is implicated in the pathogenesis of myeloproliferative neoplasm (MPN). With a hypothesis that LD significantly contributes to MPN carcinogenesis by inducing abnormal inflammation, our objective was to elucidate the pathophysiological mechanisms of MPN arising from an LD background.

METHODS

Genotyping of the LD background was performed in a cohort of MPN patients (n = 190) and healthy controls (n = 461). Logistic regression modeling, utilizing genotype data, was employed to estimate the correlation between LD and MPN. Whole transcriptome sequencing (WTS) (LD carriers = 8, non-carriers = 6) and single-cell RNA sequencing data (LD carriers = 2, non-carriers = 2, healthy controls = 2) were generated and analyzed.

RESULTS

A higher variant frequency of LD was observed in MPN compared to healthy controls (healthy, 4.9%; MPN, 7.8%), with the highest frequency seen in polycythemia vera (PV) (odds ratio = 2.33, p = 0.03). WTS revealed that LD carriers exhibited upregulated inflammatory cytokine ligand-receptor genes, pathways, and network modules in MPNs compared to non-carriers. At the single-cell level, there was monocyte expansion and elevation of cytokine ligand-receptor interactions, inflammatory transcription factors, and network modules centered on monocytes. Notably, Oncostatin-M (OSM) consistently emerged as a candidate molecule involved in the pathogenesis of LD-related PV.

CONCLUSIONS

In summary, an LD background is prevalent in MPN patients and leads to increased cytokine dysregulation and inflammation. OSM, as one of the potential molecules, plays a crucial role in PV pathogenesis by impairing lysosomal function.

(Single-stranded DNA) gaps in understanding BRCAness

The tumour-suppressive roles of BRCA1 and 2 have been attributed to three seemingly distinct functions - homologous recombination, replication fork protection, and single-stranded (ss)DNA gap suppression - and their relative importance is under debate. In this review, we examine the origin and resolution of ssDNA gaps and discuss the recent advances in understanding the role of BRCA1/2 in gap suppression. There are ample data showing that gap accumulation in BRCA1/2-deficient cells is linked to genomic instability and chemosensitivity. However, it remains unclear whether there is a causative role and the function of BRCA1/2 in gap suppression cannot unambiguously be dissected from their other functions. We therefore conclude that the three functions of BRCA1 and 2 are closely intertwined and not mutually exclusive.

A common druggable signature of oncogenic c-Myc, mutant KRAS and mutant p53 reveals functional redundancy and competition among oncogenes in cancer

The major driver oncogenes MYC, mutant KRAS, and mutant TP53 often coexist and cooperate to promote human neoplasia, which results in anticancer therapeutic opportunities within their downstream molecular programs. However, little research has been conducted on whether redundancy and competition among oncogenes affect their programs and ability to drive neoplasia. By CRISPR‒Cas9-mediated downregulation we evaluated the downstream proteomics and transcriptomics programs of MYC, mutant KRAS, and mutant TP53 in a panel of cell lines with either one or three of these oncogenes activated, in cancers of the lung, colon and pancreas. Using RNAi screening of the commonly activated molecular programs, we found a signature of three proteins - RUVBL1, HSPA9, and XPO1, which could be efficiently targeted by novel drug combinations in the studied cancer types. Interestingly, the signature was controlled by the oncoproteins in a redundant or competitive manner rather than by cooperation. Each oncoprotein individually upregulated the target genes, while upon oncogene co-expression each target was controlled preferably by a dominant oncoprotein which reduced the influence of the others. This interplay was mediated by redundant routes of target gene activation - as in the case of mutant KRAS signaling to c-Jun/GLI2 transcription factors bypassing c-Myc activation, and by competition - as in the case of mutant p53 and c-Myc competing for binding to target promoters. The global transcriptomics data from the cell lines and patient samples indicate that the redundancy and competition of oncogenic programs are broad phenomena, that may constitute even a majority of the genes dependent on oncoproteins, as shown for mutant p53 in colon and lung cancer cell lines. Nevertheless, we demonstrated that redundant oncogene programs harbor targets for efficient anticancer drug combinations, bypassing the limitations for direct oncoprotein inhibition.

Large-scale copy number alterations are enriched for synthetic viability in BRCA1/BRCA2 tumors

BACKGROUND

Pathogenic BRCA1 or BRCA2 germline mutations contribute to hereditary breast, ovarian, prostate, and pancreatic cancer. Paradoxically, bi-allelic inactivation of BRCA1 or BRCA2 (bBRCA1/2) is embryonically lethal and decreases cellular proliferation. The compensatory mechanisms that facilitate oncogenesis in bBRCA1/2 tumors remain unclear.

METHODS

We identified recurrent genetic alterations enriched in human bBRCA1/2 tumors and experimentally validated if these improved proliferation in cellular models. We analyzed mutations and copy number alterations (CNAs) in bBRCA1/2 breast and ovarian cancer from the TCGA and ICGC. We used Fisher's exact test to identify CNAs enriched in bBRCA1/2 tumors compared to control tumors that lacked evidence of homologous recombination deficiency. Genes located in CNA regions enriched in bBRCA1/2 tumors were further screened by gene expression and their effects on proliferation in genome-wide CRISPR/Cas9 screens. A set of candidate genes was functionally validated with in vitro clonogenic survival and functional assays to validate their influence on proliferation in the setting of bBRCA1/2 mutations.

RESULTS

We found that bBRCA1/2 tumors harbor recurrent large-scale genomic deletions significantly more frequently than histologically matched controls (n = 238 cytobands in breast and ovarian cancers). Within the deleted regions, we identified 277 BRCA1-related genes and 218 BRCA2-related genes that had reduced expression and increased proliferation in bBRCA1/2 but not in wild-type cells in genome-wide CRISPR screens. In vitro validation of 20 candidate genes with clonogenic proliferation assays validated 9 genes, including RIC8A and ATMIN (ATM-Interacting protein). We identified loss of RIC8A, which occurs frequently in both bBRCA1/2 tumors and is synthetically viable with loss of both BRCA1 and BRCA2. Furthermore, we found that metastatic homologous recombination deficient cancers acquire loss-of-function mutations in RIC8A. Lastly, we identified that RIC8A does not rescue homologous recombination deficiency but may influence mitosis in bBRCA1/2 tumors, potentially leading to increased micronuclei formation.

CONCLUSIONS

This study provides a means to solve the tumor suppressor paradox by identifying synthetic viability interactions and causal driver genes affected by large-scale CNAs in human cancers.

Somatic mutations in tumor-infiltrating lymphocytes impact on antitumor immunity

Immune checkpoint inhibitors (ICIs) exert clinical efficacy against various types of cancers by reinvigorating exhausted CD8+ T cells that can expand and directly attack cancer cells (cancer-specific T cells) among tumor-infiltrating lymphocytes (TILs). Although some reports have identified somatic mutations in TILs, their effect on antitumor immunity remains unclear. In this study, we successfully established 18 cancer-specific T cell clones, which have an exhaustion phenotype, from the TILs of four patients with melanoma. We conducted whole-genome sequencing for these T cell clones and identified various somatic mutations in them with high clonality. Among the somatic mutations, an SH2D2A loss-of-function frameshift mutation and TNFAIP3 deletion could activate T cell effector functions in vitro. Furthermore, we generated CD8+ T cell-specific Tnfaip3 knockout mice and showed that Tnfaip3 function loss in CD8+ T cell increased antitumor immunity, leading to remarkable response to PD-1 blockade in vivo. In addition, we analyzed bulk CD3+ T cells from TILs in additional 12 patients and identified an SH2D2A mutation in one patient through amplicon sequencing. These findings suggest that somatic mutations in TILs can affect antitumor immunity and suggest unique biomarkers and therapeutic targets.

Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines

Although aberrant activation of the KRAS and PI3K pathway alongside TP53 mutations account for frequent aberrations in human gastric cancers, neither the sequence nor the individual contributions of these mutations have been clarified. Here, we establish an allelic series of mice to afford conditional expression in the glandular epithelium of KrasG12D;Pik3caH1047R or Trp53R172H and/or ablation of Pten or Trp53. We find that KrasG12D;Pik3caH1047R is sufficient to induce adenomas and that lesions progress to carcinoma when also harboring Pten deletions. An additional challenge with either Trp53 loss- or gain-of-function alleles further accelerated tumor progression and triggered metastatic disease. While tumor-intrinsic STAT3 signaling in response to gp130 family cytokines remained as a gatekeeper for all stages of tumor development, metastatic progression required a mutant Trp53-induced interleukin (IL)-11 to IL-6 dependency switch. Consistent with the poorer survival of patients with high IL-6 expression, we identify IL-6/STAT3 signaling as a therapeutic vulnerability for TP53-mutant gastric cancer.

Unveiling the unique role of TSPAN7 across tumors: a pan-cancer study incorporating retrospective clinical research and bioinformatic analysis

BACKGROUND

TSPAN7 is an important factor in tumor progression. However, the precise function of TSPAN7 and its role in pan-cancer are not clear.

METHODS

Based on Xinhua cohort incorporating 370 patients with kidney neoplasm, we conducted differential expression analysis by immunohistochemistry between tumor and normal tissues, and explored correlations of TSPAN7 with patients' survival. Subsequently, we conducted a pan-cancer study, and successively employed differential expression analysis, competing endogenous RNA (ceRNA) analysis, protein-protein interaction (PPI) analysis, correlation analysis of TSPAN7 with clinical characteristics, tumor purity, tumor genomics, tumor immunity, and drug sensitivity. Last but not least, gene set enrichment analysis was applied to identify enriched pathways of TSPAN7.

RESULTS

In Xinhua cohort, TSPAN7 expression was significantly up-regulated (P-value = 0.0019) in tumor tissues of kidney neoplasm patients. High TSPAN7 expression was associated with decreases in overall survival (OS) (P-value = 0.009) and progression-free survival (P-value = 0.009), and it was further revealed as an independent risk factor for OS (P-value = 0.0326, HR = 5.66, 95%CI = 1.155-27.8). In pan-cancer analysis, TSPAN7 expression was down-regulated in most tumors, and it was associated with patients' survival, tumor purity, tumor genomics, tumor immunity, and drug sensitivity. The ceRNA network and PPI network of TSPAN7 were also constructed. Last but not least, the top five enriched pathways of TSPAN7 in various tumors were identified.

CONCLUSION

TSPAN7 served as a promising biomarker of various tumors, especially kidney neoplasms, and it was closely associated with tumor purity, tumor genomics, tumor immunology, and drug sensitivity in pan-cancer level.

Towards Digital Quantification of Ploidy from Pan-Cancer Digital Pathology Slides using Deep Learning

Abnormal DNA ploidy, found in numerous cancers, is increasingly being recognized as a contributor in driving chromosomal instability, genome evolution, and the heterogeneity that fuels cancer cell progression. Furthermore, it has been linked with poor prognosis of cancer patients. While next-generation sequencing can be used to approximate tumor ploidy, it has a high error rate for near-euploid states, a high cost and is time consuming, motivating alternative rapid quantification methods. We introduce PloiViT, a transformer-based model for tumor ploidy quantification that outperforms traditional machine learning models, enabling rapid and cost-effective quantification directly from pathology slides. We trained PloiViT on a dataset of fifteen cancer types from The Cancer Genome Atlas and validated its performance in multiple independent cohorts. Additionally, we explored the impact of self-supervised feature extraction on performance. PloiViT, using self-supervised features, achieved the lowest prediction error in multiple independent cohorts, exhibiting better generalization capabilities. Our findings demonstrate that PloiViT predicts higher ploidy values in aggressive cancer groups and patients with specific mutations, validating PloiViT potential as complementary for ploidy assessment to next-generation sequencing data. To further promote its use, we release our models as a user-friendly inference application and a Python package for easy adoption and use.

Evolutionary dependency of cancer mutations in gene pairs inferred by nonsynonymous-synonymous mutation ratios

BACKGROUND

Determining the impact of somatic mutations requires understanding the functional relationship of genes acquiring mutations; however, it is largely unknown how mutations in functionally related genes influence each other.

METHODS

We employed non-synonymous-to-synonymous or dNdS ratios to evaluate the evolutionary dependency (ED) of gene pairs, assuming a mutation in one gene of a gene pair can affect the evolutionary fitness of mutations in its partner genes as mutation context. We employed PanCancer- and tumor type-specific mutational profiles to infer the ED of gene pairs and evaluated their biological relevance with respect to gene dependency and drug sensitivity.

RESULTS

We propose that dNdS ratios of gene pairs and their derived cdNS (context-dependent dNdS) scores as measure of ED distinguishing gene pairs either as synergistic (SYN) or antagonistic (ANT). Mutation contexts can induce substantial changes in the evolutionary fitness of mutations in the paired genes, e.g., IDH1 and IDH2 mutation contexts lead to substantial increase and decrease of dNdS ratios of ATRX indels and IDH1 missense mutations corresponding to SYN and ANT relationship with positive and negative cdNS scores, respectively. The impact of gene silencing or knock-outs on cell viability (genetic dependencies) often depends on ED, suggesting that ED can guide the selection of candidates for synthetic lethality such as TCF7L2-KRAS mutations. Using cell line-based drug sensitivity data, the effects of targeted agents on cell lines are often associated with mutations of genes exhibiting ED with the target genes, informing drug sensitizing or resistant mutations for targeted inhibitors, e.g., PRSS1 and CTCF mutations as resistant mutations to EGFR and BRAF inhibitors for lung adenocarcinomas and melanomas, respectively.

CONCLUSIONS

We propose that the ED of gene pairs evaluated by dNdS ratios can advance our understanding of the functional relationship of genes with potential biological and clinical implications.

Identification of tumor rejection antigens and the immunologic landscape of medulloblastoma

BACKGROUND

The current standard of care treatments for medulloblastoma are insufficient as these do not take tumor heterogeneity into account. Newer, safer, patient-specific treatment approaches are required to treat high-risk medulloblastoma patients who are not cured by the standard therapies. Immunotherapy is a promising treatment modality that could be key to improving survival and avoiding morbidity. For an effective immune response, appropriate tumor antigens must be targeted. While medulloblastoma patients with subgroup-specific genetic substitutions have been previously reported, the immunogenicity of these genetic alterations remains unknown. The aim of this study is to identify potential tumor rejection antigens for the development of antigen-directed cellular therapies for medulloblastoma.

METHODS

We developed a cancer immunogenomics pipeline and performed a comprehensive analysis of medulloblastoma subgroup-specific transcription profiles (n = 170, 18 WNT, 46 SHH, 41 Group 3, and 65 Group 4 patient tumors) available through International Cancer Genome Consortium (ICGC) and European Genome-Phenome Archive (EGA). We performed in silico antigen prediction across a broad array of antigen classes including neoantigens, tumor-associated antigens (TAAs), and fusion proteins. Furthermore, we evaluated the antigen processing and presentation pathway in tumor cells and the immune infiltrating cell landscape using the latest computational deconvolution methods.

RESULTS

Medulloblastoma patients were found to express multiple private and shared immunogenic antigens. The proportion of predicted TAAs was higher than neoantigens and gene fusions for all molecular subgroups, except for sonic hedgehog (SHH), which had a higher neoantigen burden. Importantly, cancer-testis antigens, as well as previously unappreciated neurodevelopmental antigens, were found to be expressed by most patients across all medulloblastoma subgroups. Despite being immunologically cold, medulloblastoma subgroups were found to have distinct immune cell gene signatures.

CONCLUSIONS

Using a custom antigen prediction pipeline, we identified potential tumor rejection antigens with important implications for the development of immunotherapy for medulloblastoma.

Germline mutation rate predicts cancer mortality across 37 vertebrate species

Background and objectives

Cancer develops across nearly every species. However, cancer occurs at unexpected and widely different rates throughout the animal kingdom. The reason for this variation in cancer susceptibility remains an area of intense investigation. Cancer evolves in part through the accumulation of mutations, and therefore, we hypothesized that germline mutation rates would be associated with cancer prevalence and mortality across species.

Methodology

We collected previously published data on germline mutation rate and cancer mortality data for 37 vertebrate species.

Results

Germline mutation rate was positively correlated with cancer mortality (P-value = 0.0008; R2 = 0.13). Controlling for species' average parental age, maximum longevity, adult body mass or domestication did not improve the model fit (the change (Δ) in Akaike Information Criterion (AIC) was less than 2). However, this model fit was better than a model controlling for species trophic level (ΔAIC > 2).

Conclusions and implications

The increased death rate from cancer in animals with increased germline mutation rates may suggest underlying hereditary cancer predisposition syndromes similar to those diagnosed in human patients. Species with higher germline mutation rates may benefit from close monitoring for tumors due to increased genetic risk for cancer development. Early diagnoses of cancer in these species may increase their chances of overall survival, especially for threatened and endangered species.

Anticancer Effects of Secoiridoids-A Scoping Review of the Molecular Mechanisms behind the Chemopreventive Effects of the Olive Tree Components Oleocanthal, Oleacein, and Oleuropein

The olive tree (Olea europaea) and olive oil hold significant cultural and historical importance in Europe. The health benefits associated with olive oil consumption have been well documented. This paper explores the mechanisms of the anti-cancer effects of olive oil and olive leaf, focusing on their key bioactive compounds, namely oleocanthal, oleacein, and oleuropein. The chemopreventive potential of oleocanthal, oleacein, and oleuropein is comprehensively examined through this systematic review. We conducted a systematic literature search to identify eligible articles from Scopus, PubMed, and Web of Science databases published up to 10 October 2023. Among 4037 identified articles, there were 88 eligible articles describing mechanisms of chemopreventive effects of oleocanthal, oleacein, and oleuropein. These compounds have the ability to inhibit cell proliferation, induce cell death (apoptosis, autophagy, and necrosis), inhibit angiogenesis, suppress tumor metastasis, and modulate cancer-associated signalling pathways. Additionally, oleocanthal and oleuropein were also reported to disrupt redox hemostasis. This review provides insights into the chemopreventive mechanisms of O. europaea-derived secoiridoids, shedding light on their role in chemoprevention. The bioactivities summarized in the paper support the epidemiological evidence demonstrating a negative correlation between olive oil consumption and cancer risk. Furthermore, the mapped and summarized secondary signalling pathways may provide information to elucidate new synergies with other chemopreventive agents to complement chemotherapies and develop novel nutrition-based anti-cancer approaches.

Patterns of Aneuploidy and Signaling Consequences in Cancer

Aneuploidy, or a change in the number of whole chromosomes or chromosome arms, is a near-universal feature of cancer. Chromosomes affected by aneuploidy are not random, with observed cancer-specific and tissue-specific patterns. Recent advances in genome engineering methods have allowed the creation of models with targeted aneuploidy events. These models can be used to uncover the downstream effects of individual aneuploidies on cancer phenotypes including proliferation, apoptosis, metabolism, and immune signaling. Here, we review the current state of research into the patterns of aneuploidy in cancer and their impact on signaling pathways and biological processes.