Cancer-mutation network and the number and specificity of driver mutations

CPT1A loss disrupts BCAA metabolism to confer therapeutic vulnerability in TP53-mutated liver cancer

Driver genomic mutations in tumors define specific molecular subtypes that display distinct malignancy competence, therapeutic resistance and clinical outcome. Although TP53 mutation has been identified as the most common mutation in hepatocellular carcinoma (HCC), current understanding on the biological traits and therapeutic strategies of this subtype has been largely unknown. Here, we reveal that fatty acid β oxidation (FAO) is remarkable repressed in TP53 mutant HCC and which links to poor prognosis in HCC patients. We further demonstrate that carnitine palmitoyltransferase 1 (CPT1A), the rate-limiting enzyme of FAO, is universally downregulated in liver tumor tissues, and which correlates with poor prognosis in HCC and promotes HCC progression in the de novo liver tumor and xenograft tumor models. Mechanically, hepatic Cpt1a loss disrupts lipid metabolism and acetyl-CoA production. Such reduction in acetyl-CoA reduced histone acetylation and epigenetically reprograms branched-chain amino acids (BCAA) catabolism, and leads to the accumulation of cellular BCAAs and hyperactivation of mTOR signaling. Importantly, we reveal that genetic ablation of CPT1A renders TP53 mutant liver cancer mTOR-addicted and sensitivity to mTOR inhibitor AZD-8055 treatment. Consistently, Cpt1a loss in HCC directs tumor cell therapeutic response to AZD-8055. CONCLUSION: Our results show genetic evidence for CPT1A as a metabolic tumor suppressor in HCC and provide a therapeutic approach for TP53 mutant HCC patients.

Marine algae-derived characterized bioactive compounds as therapy for cancer: A review on their classification, mechanism of action, and future perspectives

In 2022, there were around 20 million new cases and over 9.7 million cancer-related deaths worldwide. An increasing number of metabolites with anticancer activity in algae had been isolated and identified, which were promising candidates for cancer therapy. Red algae are well-known for the production of brominated metabolites, including terpenoids and phenols, which have the capacity to induce cell toxicity. Some non-toxic biological macromolecules, including polysaccharides, are distinct secondary metabolites found in many algae, particularly green algae. They possess anticancer activities by inhibiting tumor angiogenesis, stimulating the immune response, and inducing apoptosis. However, the structure-activity relationship between these components and antitumor activity, as well as certain taxa within the algae, remains relatively unstudied. This work is based on the reports published from 2003 to 2024 in PubMed and ISI Web of Science databases. A comprehensive review of the characterized algal anticancer active compounds, together with their structure and mechanism of action was performed. Also, their structure-activity relationship was preliminarily summarized to better assess their potential properties as a natural, safe bioactive product to be used as an alternative for the treatment of cancers, leading to new opportunities for drug discovery.

Implications of GCLC in prognosis and immunity of lung adenocarcinoma and multi-omics regulation mechanisms

BACKGROUND

Ferroptosis is an iron-dependent type of regulated cell death, and has been implicated in lung adenocarcinoma (LUAD). Evidence has proved the key role of glutamate-cysteine ligase catalytic subunit (GCLC) in ferroptosis, but its role in LUAD remains unclear. Herein, we explored the implications of GCLC and relevant genes in LUAD prognosis and immunity as well as underlying molecular mechanisms.

METHODS

This work gathered mRNA, miRNA, DNA methylation, somatic mutation and copy-number variation data from TCGA-LUAD. WGCNA was utilized for selecting GCLC-relevant genes, and a GCLC-relevant prognostic signature was built by uni- and multivariate-cox regression analyses. Immune compositions were estimated via CIBERSORT, and two immunotherapy cohorts of solid tumors were analyzed. Multi-omics regulatory mechanisms were finally assessed.

RESULTS

Our results showed that GCLC was overexpressed in LUAD, and potentially resulted in undesirable survival. A prognostic model was generated, which owned accurate and independent performance in prognostication. GCLC, and relevant genes were notably connected with immune compositions and immune checkpoints. High GCLC expression was linked with better responses to anti-PD-L1 and anti-CTLA-4 treatment. Their possible DNA methylation sites were inferred, e.g., hypomethylation in cg19740353 might contribute to GCLC up-regulation. Frequent genetic mutations also affected their expression. Upstream transcription factors (E2F1/3/4, etc.), post-transcriptional regulation of miRNAs (hsa-mir-30c-1, etc.), lncRNAs (C8orf34-AS1, etc.), and IGF2BP1-mediated m6A modification were identified. It was also found NOP58-mediated SUMOylation post-translational modification.

CONCLUSIONS

Together, we show that GCLC and relevant genes exert crucial roles in LUAD prognosis and immunity, and their expression can be controlled by complex multi-omics mechanisms.

In vivo evaluation of mebendazole and Ran GTPase inhibition in breast cancer model system

Aim: To investigate the therapeutic potential of mebendazole (MBZ)-loaded nanostructured lipid carriers (NLCs). Methodology: NLC-MBZ was prepared and characterized to evaluate the in vitro and in vivo anticancer effects and the inhibitory effect on RanGTP and its potential as an antimetastatic treatment in vivo. Results: NLC-MBZ exhibited a size and charge of 155 ± 20 nm and -27 ± 0.5 mV, respectively, with 90.7% encapsulation. Free MBZ and NLC-MBZ had a 50% inhibitory concentration of 610 and 305 nM, respectively, against MDA-MB-231 cell lines. NLC-MBZ decreased tumor size, suppressed tumor lung metastases, and lowered the expression of CDC25A, SKP2, RbX1 and Cullin1 while boosting the Rb proteins. Conclusion: NLC-MBZ displayed antiangiogenic potential and resulted in a reduced rate of lung metastasis in vivo.

Molecular classification reveals the sensitivity of lung adenocarcinoma to radiotherapy and immunotherapy: multi-omics clustering based on similarity network fusion

BACKGROUND

Due to individual differences in tumors and immune systems, the response rate to immunotherapy is low in lung adenocarcinoma (LUAD) patients. Combinations with other therapeutic strategies improve the efficacy of immunotherapy in LUAD patients. Although radioimmunotherapy has been demonstrated to effectively suppress tumors, the underlying mechanisms still need to be investigated.

METHODS

Total RNA from LUAD cells was sequenced before and after radiotherapy to identify differentially expressed radiation-associated genes. The similarity network fusion (SNF) algorithm was applied for molecular classification based on radiation-related genes, immune-related genes, methylation data, and somatic mutation data. The changes in gene expression, prognosis, immune cell infiltration, radiosensitivity, chemosensitivity, and sensitivity to immunotherapy were assessed for each subtype.

RESULTS

We used the SNF algorithm and multi-omics data to divide TCGA-LUAD patients into three subtypes. Patients with the CS3 subtype had the best prognosis, while those with the CS1 and CS2 subtypes had poorer prognoses. Among the strains tested, CS2 exhibited the most elevated immune cell infiltration and expression of immune checkpoint genes, while CS1 exhibited the least. Patients in the CS2 subgroup were more likely to respond to PD-1 immunotherapy. The CS2 patients were most sensitive to docetaxel and cisplatin, while the CS1 patients were most sensitive to paclitaxel. Experimental validation of signature genes in the CS2 subtype showed that inhibiting the expression of RHCG and TRPA1 could enhance the sensitivity of lung cancer cells to radiation.

CONCLUSIONS

In summary, this study identified a risk classifier based on multi-omics data that can guide treatment selection for LUAD patients.

Integrative Multi-omics Analysis Reveals Different Metabolic Phenotypes Based on Molecular Characteristics in Thyroid Cancer

PURPOSE

Thyroid cancer metabolic characteristics vary depending on the molecular subtype determined by mutational status. We aimed to investigate the molecular subtype-specific metabolic characteristics of thyroid cancers.

EXPERIMENTAL DESIGN

An integrative multi-omics analysis was conducted, incorporating transcriptomics, metabolomics, and proteomics data obtained from human tissues representing distinct molecular characteristics of thyroid cancers: BRAF-like (papillary thyroid cancer with BRAFV600E mutation; PTC-B), RAS-like (follicular thyroid cancer with RAS mutation; FTC-R), and ATC-like (anaplastic thyroid cancer with BRAFV600E or RAS mutation; ATC-B or ATC-R). To validate our findings, we employed tissue microarray of human thyroid cancer tissues and performed in vitro analyses of cancer cell phenotypes and metabolomic assays after inducing genetic knockdown.

RESULTS

Metabolic properties differed between differentiated thyroid cancers of PTC-B and FTC-R, but were similar in dedifferentiated thyroid cancers of ATC-B/R, regardless of their mutational status. Tricarboxylic acid (TCA) intermediates and branched-chain amino acids (BCAA) were enriched with the activation of TCA cycle only in FTC-R, whereas one-carbon metabolism and pyrimidine metabolism increased in both PTC-B and FTC-R and to a great extent in ATC-B/R. However, the protein expression levels of the BCAA transporter (SLC7A5) and a key enzyme in one-carbon metabolism (SHMT2) increased in all thyroid cancers and were particularly high in ATC-B/R. Knockdown of SLC7A5 or SHMT2 inhibited the migration and proliferation of thyroid cancer cell lines differently, depending on the mutational status.

CONCLUSIONS

These findings define the metabolic properties of each molecular subtype of thyroid cancers and identify metabolic vulnerabilities, providing a rationale for therapies targeting its altered metabolic pathways in advanced thyroid cancer.

Identification of a novel genomic variance of BRAF1 in papillary thyroid carcinoma: A case report

RATIONALE

Papillary thyroid carcinoma (PTC), the predominant subtypes accounting for approximately 85% of thyroid carcinomas, has a rapidly increasing global incidence rate. Statistically, approximately 74.6% PTC patients had the genomic variants of BRAF, especially BRAFV600E mutation, which has been reported to stratify patients and guide clinic-therapies. However, some PTC patients may carry other nonclassical mutation patterns of BRAF, due to the complex of genomic instability. And the spectrum of BRAF mutation was not fully characterized. We reported a novel BRAF mutation pattern of PTC.

PATIENT CONCERNS

A 59-year-old woman was admitted to our hospital because of the slight enlargement of bilateral cervical lymph nodes in July 2023.

DIAGNOSIS

Ultrasonography revealed that the bilateral thyroid nodules of the patients both presented 1 hypoechoic nodule, which was graded as 3 of the elastic score, and the small calcification in the right lobe (Chinese-Thyroid Imaging Reporting and Data System 4c). Pathological diagnosis showed the interstitial collagen change and focal follicular epithelial papillary hyperplasia with atypical hyperplasia of the bilateral thyroid. Further puncture pathology showed that the patient had a malignant thyroid lesion with the phenotypes of papillary carcinoma and diagnosed with malignancy subsequently. Additionally, the patient harbored a novel insert on BRAF exon 15, a 6-base fragment AGACAG inserting between c.1798 and c.1799.

INTERVENTIONS

The patient was undergone on microwave ablation of thyroid carcinoma on July 28, 2023. After the surgery, the patient was treated on anti-infection, cold saline external application of bilateral thyroid swelling supportive treatment.

OUTCOMES

No postoperative complications or recurrence and metastasis were found.

LESSONS

This is the first case of the novel nonclassical genomic variant of BRAF. Our study extends the spectrum of BRAF mutations. The patient had a favorable response to microwave ablation, indicating that in spite of the association between this mutation and high-grade malignant phenotype, this genomic variant of BRAF did not have a detrimental effect on the response of clinical treatment.

Sialidase NEU3 Contributes to the Invasiveness of Bladder Cancer

Bladder cancer is the 10th most commonly diagnosed cancer worldwide. The current standard treatment for advanced bladder cancer is neoadjuvant cisplatin (NAC)-based chemotherapy followed by cystectomy. However, the response rate to chemotherapy is only 50%, owing to cisplatin resistance, and there is a need for novel therapies. Because the invasiveness of bladder cancer greatly influences patient prognosis, a mechanistic analysis of the invasive function can lead to therapeutic targets. Sialidases, which remove sialic acid residues from the nonreducing ends of sugar chains and catalyze the initial reaction in the degradation of sugar chains, are predicted to be involved in cell invasion and motility. However, the involvement of sialidases in bladder cancer, especially their relationship with the invasive ability, remains unclear. Here, using patient tissues and multiple bladder cancer cell lines, we show that the sialidase NEU3 is highly expressed in bladder cancer. Analysis of NEU3's function using its siRNA-mediated knockdown revealed that NEU3 contributes to bladder cancer invasiveness. Mechanistic analysis showed that NEU3 activates ERK and PI3K signaling. Our results show that NEU3 is involved in the malignancy of bladder cancer, and its suppression may lead to novel treatments for bladder cancer.

Comprehensively prognostic and immunological analysis of VRK Serine/Threonine Kinase 1 in pan-cancer and identification in hepatocellular carcinoma

BACKGROUND

VRK1 is a member of the vaccinia-related kinase (VRK) family of serine/threonine protein kinases, which is related to the occurrence and development of malignant tumors. The expression pattern, predictive value, and biological function of VRK1 in various cancers remain largely elusive and warrant further investigation.

METHODS

Public databases, such as TCGA, GTEx, and UCEC, were utilized to comprehensively analyze the expression of VRK1 across multiple cancer types. Prognostic significance was assessed through Univariate Cox regression and Kaplan-Meier analyses. Additionally, Spearman's correlation analysis was employed to explore the potential associations between VRK1 expression and various factors, including tumor microenvironment scores, immune cell infiltration, and immune-related genes. Moreover, to validate the findings, differential expression of VRK1 in HCC tissues and cell lines was further confirmed using qPCR, Western blot, and immunohistochemistry techniques.

RESULTS

The upregulation of VRK1 was observed in most cancer types, and was associated with worse prognosis in ACC, KICH, KIRP, LGG, LIHC, LUAD, MESO, and PCPG. In various cancers, VRK1 expression exhibited positive correlations with immune infiltrating cells, immune checkpoint-related genes, TMB, and MSI. Furthermore, the promoter methylation status of VRK1 varied across different tumor types, and this variation was associated with patient prognosis in certain cancers. In our experimental analyses, we observed significantly elevated expression of VRK1 in both HCC tissues and HCC cells. Functionally, we found that the downregulation of VRK1 had a profound impact on HCC cells, leading to a significant decrease in their proliferation, migration, and invasion capabilities.

CONCLUSION

The expression of VRK1 exerts a notable influence on the prognosis of several tumors and exhibits a strong correlation with tumor immune infiltration. Moreover, in the context of HCC, VRK1 may act as an oncogene, actively promoting tumor progression.

Association of the age of onset of metabolic syndrome with the risk of all cancer types

BACKGROUND

The association between the age at onset of metabolic syndrome and cancer risk remains unknown. This study explored the association between age at metabolic syndrome onset and the risk of overall and site-specific cancer incidence.

METHODS

This study included 31,688 participants with new-onset metabolic syndrome and 31,688 participants matched according to sex, age (±1 y), and examination year among the 179,328 participants who underwent Kailuan health examinations from 2006 to 2017 in Tangshan, China. Weighted Cox regression was used to calculate the hazard ratios and 95% confidence intervals of new-onset metabolic syndrome for overall and site-specific cancer incidence across age groups. Population-attributable risk proportions were used to estimate the number of cases that could be prevented by eliminating the risk factors from the population.

RESULTS

During an average follow-up period of 10.22 y, we identified 2,710 cases of cancer and 4,218 deaths that occurred before the diagnosis of cancer. With an increase in metabolic syndrome onset age, the hazards of overall cancer incidence were gradually attenuated. The average hazard ratios (95% confidence intervals) of overall cancer were 1.94 (1.25-2.99) for metabolic syndrome onset age <45 year old, 1.41 (1.15-1.71) for age 45-54 years old, 1.38 (1.11-1.73) for age 55-64 years old, and 1.07 (0.89-1.28) for age ≥65 years old, respectively (p for interaction = 0.005). Similar results were obtained for colorectal, liver, and breast cancers in the site-specific analysis.

CONCLUSIONS

New-onset metabolic syndrome was associated with a higher risk of overall cancer and incidence of several types of cancer, and the associations were stronger with a younger age of onset.

TRIAL REGISTRATION

Kailuan Study, ChiCTR2000029767 (Registered February 12, 2020, https://www.chictr.org.cn/showprojEN.html?proj=48316).

Identification of CHMP7 as a promising immunobiomarker for immunotherapy and chemotherapy and impact on prognosis of colorectal cancer patients

Introduction: ESCRT is a molecular machine involved in various important physiological processes, such as the formation of multivesicular bodies, cellular autophagy, and cellular membrane repair. CHMP7 is a regulatory subunit of ESCRT-III and is necessary for the proper functioning of ESCRT. In this study, public databases were exploited to explore the role of CHMP7 in tumors. Methods: The research on CHMP7 in oncology is rather limited. In this study, the differential expression of CHMP7 in multiple tumor tissues was analyzed with information from public databases and clinically collected colorectal cancer tissue samples. Subsequently, the mutational landscape of CHMP7, methylation levels, and the relationship between its expression levels and genomic instability were resolved. The immune microenvironment is a compelling emerging star in tumor research. The correlation of CHMP7 with various infiltrating immune cell types in TME was analyzed by online datasets and single-cell sequencing. In terms of clinical treatment, the impact of CHMP7 expression levels on chemotherapy and immunotherapy and the evaluation of small molecule drugs related to CHMP7 were assessed. Results: CHMP7 has a predictive value for the prognosis of patients with tumors and is highly involved in tumor immunity. The downregulation of CHMP7 may lead to genomic instability. A strong correlation between CHMP7 and TME immune cell infiltration has been observed, participating in the formation of suppressive TME and promoting tumor progression. The expression level of CHMP7 is significantly lower in the non-responder group of multiple chemotherapeutic agents. CHMP7 can potentially serve as a new biomarker for predicting the efficacy of tumor chemotherapy and immunotherapy. Conclusion: As a gene of interest, CHMP7 is expected to provide novel and promising targets for further treatment of patients with tumor.

Distinct mutational processes shape selection of MHC class I and class II mutations across primary and metastatic tumors

Disruption of antigen presentation via loss of major histocompatibility complex (MHC) expression is a strategy whereby cancer cells escape immune surveillance and develop resistance to immunotherapy. Here, we develop the personalized genomics algorithm Hapster and accurately call somatic mutations within the MHC genes of 10,001 primary and 2,199 metastatic tumors, creating a catalog of 1,663 non-synonymous mutations that provide key insights into MHC mutagenesis. We find that MHC class I genes are among the most frequently mutated genes in both primary and metastatic tumors, while MHC class II mutations are more restricted. Recurrent deleterious mutations are found within haplotype- and cancer-type-specific hotspots associated with distinct mutational processes. Functional classification of MHC residues reveals significant positive selection for mutations disruptive to the B2M, peptide, and T cell binding interfaces, as well as to MHC chaperones.

Translating Molecular Biology Discoveries to Develop Targeted Cancer Interception in Barrett's Esophagus

Esophageal adenocarcinoma (EAC) is a rapidly increasing lethal tumor. It commonly arises from a metaplastic segment known as Barrett's esophagus (BE), which delineates the at-risk population. Ample research has elucidated the pathogenesis of BE and its progression from metaplasia to invasive carcinoma; and multiple molecular pathways have been implicated in this process, presenting several points of cancer interception. Here, we explore the mechanisms of action of various agents, including proton pump inhibitors, non-steroidal anti-inflammatory drugs, metformin, and statins, and explain their roles in cancer interception. Data from the recent AspECT trial are discussed to determine how viable a multipronged approach to cancer chemoprevention would be. Further, novel concepts, such as the repurposing of chemotherapeutic drugs like dasatinib and the prevention of post-ablation BE recurrence using itraconazole, are discussed.

Development of an Immune Prognostic Model for Clear Cell Renal Cell Carcinoma Based on Tumor Microenvironment

Immune infiltration remains at a high level in clear cell renal cell carcinoma (ccRCC). It has been confirmed that immune cell infiltration in tumor microenvironment (TME) is intimately bound up with the progression and the clinical outcome of ccRCC. The prognostic model, developed based on different immune subtypes of ccRCC, has a predictive value in patients' prognosis. RNA sequencing data, somatic mutation data of ccRCC and clinical information were acquired from the cancer genome atlas (TCGA) database. The key immune-related genes (IRGs) were selected and by univariate Cox, LASSO, and multivariate Cox regression analyses. Then the ccRCC prognostic model was developed. The applicability of this model was verified in the independent dataset GSE29609. Thirteen IRGs including CCL7, ATP6V1C2, ATP2B3, ELAVL2, SLC22A8, DPP6, EREG, SERPINA7, PAGE2B, ADCYAP1, ZNF560, MUC20, and ANKRD30A were finally selected and a 13-IRGs prognostic model was developed. Survival analysis demonstrated that when compared with the low-risk group, patients in the high-risk group had a lower overall survival (p<0.05). AUC values based on the 13-IRGs prognostic model used to predict 3- and 5-year survival of ccRCC patients were greater than 0.70. And risk score was an independent prognostic factor (p<0.001). In addition, nomogram could accurately predict ccRCC patient's prognosis. This 13-IRGs model can effectively evaluate the prognosis of ccRCC patients, and also provide guidance for the treatment and prognosis of ccRCC patients.

Sirtuin1-p53: a potential axis for cancer therapy

Sirtuin1 (SIRT1) is a conserved nicotinamide adenine dinucleotide (NAD⁺)-dependent histone deacetylase that plays key roles in a range of cellular events, including the maintenance of genome stability, gene regulation, cell proliferation, and apoptosis. P53 is one of the most studied tumor suppressors and the first identified non-histone target of SIRT1. SIRT1 deacetylates p53 in a NAD⁺-dependent manner and inhibits its transcriptional activity, thus exerting action on a series of pathways related to tissue homeostasis and various pathological states. The SIRT1-p53 axis is thought to play a central role in tumorigenesis. Although SIRT1 was initially identified as a tumor promoter, evidence now indicates that SIRT1 may also act as a tumor suppressor. This seemingly contradictory evidence indicates that the functionality of SIRT1 may be dictated by different cell types and intracellular localization patterns. In this review, we summarize recent evidence relating to the interactions between SIRT1 and p53 and discuss the relative roles of these two molecules with regards to cancer-associated cellular events. We also provide an overview of current knowledge of SIRT1-p53 signaling in tumorigenesis. Given the vital role of the SIRT1-p53 pathway, targeting this axis may provide promising strategies for the treatment of cancer.

Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles

Introduction

Local and regional recurrence after surgical intervention is a significant problem in cancer management. The multistage theory of carcinogenesis precisely places the presence of histologically normal but mutated premalignant lesions surrounding the tumor - field cancerization, as a significant cause of cancer recurrence. The relationship between tissue dynamics, cancer initiation and cancer recurrence in multistage carcinogenesis is not well known.

Methods

This study constructs a computational model for cancer initiation and recurrence by combining the Moran and branching processes in which cells requires 3 or more mutations to become malignant. In addition, a spatial structure-setting is included in the model to account for positional relativity in cell turnover towards malignant transformation. The model consists of a population of normal cells with no mutation; several populations of premalignant cells with varying number of mutations and a population of malignant cells. The model computes a stage of cancer detection and surgery to eliminate malignant cells but spares premalignant cells and then estimates the time for malignant cells to re-emerge.

Results

We report the cellular conditions that give rise to different patterns of cancer initiation and the conditions favoring a shorter cancer recurrence by analyzing premalignant cell types at the time of surgery. In addition, the model is fitted to disease-free clinical data of 8,957 patients in 27 different cancer types; From this fitting, we estimate the turnover rate per month, relative fitness of premalignant cells, growth rate and death rate of cancer cells in each cancer type.

Discussion

Our study provides insights into how to identify patients who are likely to have a shorter recurrence and where to target the therapeutic intervention.

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.

The role of physics in multiomics and cancer evolution

Complex interactions between the physical environment and phenotype of a tumour, and genomics, transcriptomics, proteomics and epigenomics, are increasingly known to have a significant influence on cancer development, progression and evolution. For example, mechanical stress can alter both genome maintenance and histone modifications, which consequently affect transcription and the epigenome. Increased stiffness has been linked to genetic heterogeneity and is responsible for heterochromatin accumulations. Stiffness thereby leads to deregulation in gene expression, disrupts the proteome and can impact angiogenesis. Several studies have shown how the physics of cancer can influence diverse cancer hallmarks such as resistance to cell death, angiogenesis and evasion from immune destruction. In this review, we will explain the role that physics of cancer plays in cancer evolution and explore how multiomics are being used to elucidate the mechanisms underpinning them.

Experimental mouse models for translational human cancer research

The development and growth of tumors remains an important and ongoing threat to human life around the world. While advanced therapeutic strategies such as immune checkpoint therapy and CAR-T have achieved astonishing progress in the treatment of both solid and hematological malignancies, the malignant initiation and progression of cancer remains a controversial issue, and further research is urgently required. The experimental animal model not only has great advantages in simulating the occurrence, development, and malignant transformation mechanisms of tumors, but also can be used to evaluate the therapeutic effects of a diverse array of clinical interventions, gradually becoming an indispensable method for cancer research. In this paper, we have reviewed recent research progress in relation to mouse and rat models, focusing on spontaneous, induced, transgenic, and transplantable tumor models, to help guide the future study of malignant mechanisms and tumor prevention.

Tumour Genetic Heterogeneity in Relation to Oral Squamous Cell Carcinoma and Anti-Cancer Treatment

Oral squamous cell carcinoma (SCC) represents more than 90% of all oral cancers and is the most frequent SCC of the head and neck region. It may affect any oral mucosal subsite but most frequently the tongue, followed by the floor of the mouth. The use of tobacco and betel nut, either smoked or chewed, and abuse of alcohol are the main risk factors for oral SCC. Oral SCC is characterized by considerable genetic heterogeneity and diversity, which together have a significant impact on the biological behaviour, clinical course, and response to treatment and on the generally poor prognosis of this carcinoma. Characterization of spatial and temporal tumour-specific molecular profiles and of person-specific resource availability and environmental and biological selective pressures could assist in personalizing anti-cancer treatment for individual patients, with the aim of improving treatment outcomes. In this narrative review, we discuss some of the events in cancer evolution and the functional significance of driver-mutations in carcinoma-related genes in general and elaborate on mechanisms mediating resistance to anti-cancer treatment.

Rethinking glutamine metabolism and the regulation of glutamine addiction by oncogenes in cancer

Glutamine, the most abundant non-essential amino acid in human blood, is crucial for cancer cell growth and cancer progression. Glutamine mainly functions as a carbon and nitrogen source for biosynthesis, energy metabolism, and redox homeostasis maintenance in cancer cells. Dysregulated glutamine metabolism is a notable metabolic characteristic of cancer cells. Some carcinogen-driven cancers exhibit a marked dependence on glutamine, also known as glutamine addiction, which has rendered the glutamine metabolic pathway a breakpoint in cancer therapeutics. However, some cancer cells can adapt to the glutamine unavailability by reprogramming metabolism, thus limiting the success of this therapeutic approach. Given the complexity of metabolic networks and the limited impact of inhibiting glutamine metabolism alone, the combination of glutamine metabolism inhibition and other therapeutic methods may outperform corresponding monotherapies in the treatment of cancers. This review summarizes the uptake, transport, and metabolic characteristics of glutamine, as well as the regulation of glutamine dependence by some important oncogenes in various cancers to emphasize the therapeutic potential of targeting glutamine metabolism. Furthermore, we discuss a glutamine metabolic pathway, the glutaminase II pathway, that has been substantially overlooked. Finally, we discuss the applicability of polytherapeutic strategies targeting glutamine metabolism to provide a new perspective on cancer therapeutics.

Subtypes analysis and prognostic model construction based on lysosome-related genes in colon adenocarcinoma

Background: Lysosomes are essential for the development and recurrence of cancer. The relationship between a single lysosome-related gene and cancer has previously been studied, but the relationship between the lysosome-related genes (LRGs) and colon adenocarcinoma (COAD) remains unknown. This research examined the role of lysosome-related genes in colon adenocarcinoma. Methods: 28 lysosome-related genes associated with prognosis (PLRGs) were found by fusing the gene set that is differently expressed between tumor and non-tumor in colon adenocarcinoma with the gene set that is related to lysosomes. Using consensus unsupervised clustering of PLRGs, the colon adenocarcinoma cohort was divided into two subtypes. Prognostic and tumor microenvironment (TME) comparisons between the two subtypes were then made. The PLRGs_score was constructed using the least absolute shrinkage and selection operator regression (LASSO) method to quantify each patient's prognosis and provide advice for treatment. Lastly, Western Blot and immunohistochemistry (IHC) were used to identify MOGS expression at the protein level in colon adenocarcinoma tissues. Results: PLRGs had more somatic mutations and changes in genetic level, and the outcomes of the two subtypes differed significantly in terms of prognosis, tumor microenvironment, and enrichment pathways. Then, PLRGs_score was established based on two clusters of differential genes in the cancer genome atlas (TCGA) database, and external verification was performed using the gene expression omnibus (GEO) database. Then, we developed a highly accurate nomogram to enhance the clinical applicability of the PLRGs_score. Finally, a higher PLRGs_score was associated with a poorer overall survival (OS), a lower tumor mutation burden (TMB), a lower cancer stem cell (CSC) index, more microsatellite stability (MSS), and a higher clinical stage. MOGS was substantially elevated at the protein level in colon adenocarcinoma as additional confirmation. Conclusion: Overall, based on PLRGs, we identified two subtypes that varied significantly in terms of prognosis and tumor microenvironment. Then, in order to forecast patient prognosis and make treatment suggestions, we developed a diagnostic model with major significance for prognosis, clinical relevance, and immunotherapy. Moreover, we were the first to demonstrate that MOGS is highly expressed in colon adenocarcinoma.

MYC Causes Multiple Myeloma Progression via Attenuating TP53-Induced MicroRNA-34 Expression

MicroRNAs (miRNAs and miRs) are small (19-25 base pairs) non-coding RNAs with the ability to modulate gene expression. Previously, we showed that the miR-34 family is downregulated in multiple myeloma (MM) as the cancer progressed. In this study, we aimed to clarify the mechanism of miRNA dysregulation in MM. We focused particularly on the interaction between MYC and the TP53-miR34 axis because there is a discrepancy between increased TP53 and decreased miR-34 expressions in MM. Using the nutlin-3 or Tet-on systems, we caused wild-type (WT) p53 protein accumulation in human MM cell lines (HMCLs) and observed upregulated miR-34 expression. Next, we found that treatment with an Myc inhibitor alone did not affect miR-34 expression levels, but when it was coupled with p53 accumulation, miR-34 expression increased. In contrast, forced MYC activation by the MYC-ER system reduced nutlin-3-induced miR-34 expression. We also observed that TP53 and MYC were negatively correlated with mature miR-34 expressions in the plasma cells of patients with MM. Our results suggest that MYC participates in the suppression of p53-dependent miRNA expressions. Because miRNA expression suppresses tumors, its inhibition leads to MM development and malignant transformation.

Knockdown of TMEM132A restrains the malignant phenotype of gastric cancer cells via inhibiting Wnt signaling

Transmembrane protein 132 A (TMEM132A) has been recently reported to be a novel regulator of the Wnt signaling pathway, which is a cancer-associated cascade. However, the role of TMEM132A in cancer is not well characterized. Here, we used bioinformatics analysis to analyze the differential expression of TMEM132A in gastric cancer (GC) tissues and determine its diagnostic and prognostic value. Results showed that TMEM132A expression was upregulated in GC tissues. TMEM132A was also found to have diagnostic and prognostic roles in patient with GC. Furthermore, as evaluated by in vitro assays, knockdown of TMEM132A restrained cell proliferation, migration, and invasion of GC cells, while overexpression of TMEM132A exerted opposite effects. However, the effects of TMEM132A silencing and overexpression on GC cells were reversed by treatment with LiCl and ICG-001 (the Wnt signaling activator and inhibitor), respectively. In addition, in vivo assays showed that knockdown of TMEM132A suppressed GC tumorigenesis. Hence, our results provide new insights into the oncogenic role of TMEM132A in regulating GC cell proliferation, migration, and invasion, as well as its prognostic and therapeutic roles in patients with GC. These data highlight the diagnostic, prognostic, and therapeutic potential of TMEM132A in GC.

Evaluating cancer etiology and risk with a mathematical model of tumor evolution

Recent evidence arising from DNA sequencing of healthy human tissues has clearly indicated that our organs accumulate a relevant number of somatic mutations due to normal endogenous mutational processes, in addition to those caused by environmental factors. A deeper understanding of the evolution of this endogenous mutational load is critical for understanding what causes cancer. Here we present a mathematical model of tumor evolution that is able to predict the expected number of endogenous somatic mutations present in various tissue types of a patient at a given age. These predictions are then compared to those observed in patients. We also obtain an improved fitting of the variation in cancer incidence across cancer types, showing that the endogenous mutational processes can explain 4/5 of the variation in cancer risk. Overall, these results offer key insights into cancer etiology, by providing further evidence for the major role these endogenous processes play in cancer.

A prognostic signature based on cuprotosis-related long non-coding RNAs predicts the prognosis and sensitivity to chemotherapy in patients with colorectal cancer

Cuprotosis, a newly proposed mechanism of cell death, can trigger acute oxidative stress that leads to cell death by mediating protein lipidation in the tricarboxylic acid cycle. However, cuprotosis-related long non-coding RNAs (CRLNCs) and their relationship with prognosis and the immunological landscape of colorectal cancer (CRC) are unclear. We have developed a lncRNA signature to predict survival time, immune infiltration, and sensitivity to chemotherapy. CRLNCs were screened using the Cor function of the R software and the differentially expressed lncRNAs were collected with the limma package. Differentially expressed long non-coding RNAs (lncRNAs) associated with prognosis were selected using univariate regression analysis. A prognostic signature was developed using the least absolute shrinkage and selection operator (LASSO) and multivariate regression analysis. Patients with CRC were divided into two groups based on the risk score. The low-risk group had a more favorable prognosis, higher expression of immune checkpoints, and a higher level of immune cell infiltration compared with the high-risk group. Furthermore, there was a close association between the risk score and the clinical stage, tumor mutational burden, cancer stem cell index, and microsatellite instability. We also assessed chemotherapy response in the two risk groups. Our study analyzed the role of CRLNCs in CRC and provided novel targets and strategies for CRC chemotherapy and immunotherapy.

Rewired Metabolism of Amino Acids and Its Roles in Glioma Pathology

Amino acids (AAs) are indispensable building blocks of diverse bio-macromolecules as well as functional regulators for various metabolic processes. The fact that cancer cells live with a voracious appetite for specific AAs has been widely recognized. Glioma is one of the most lethal malignancies occurring in the central nervous system. The reprogrammed metabolism of AAs benefits glioma proliferation, signal transduction, epigenetic modification, and stress tolerance. Metabolic alteration of specific AAs also contributes to glioma immune escape and chemoresistance. For clinical consideration, fluctuations in the concentrations of AAs observed in specific body fluids provides opportunities to develop new diagnosis and prognosis markers. This review aimed at providing an extra dimension to understanding glioma pathology with respect to the rewired AA metabolism. A deep insight into the relevant fields will help to pave a new way for new therapeutic target identification and valuable biomarker development.

Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms

Background

Developing prediction tools for immunotherapy approaches is a clinically important and rapidly emerging field. The routinely used prediction biomarker is inaccurate and may not adequately utilize large amounts of medical data. Machine learning is a promising way to predict the benefit of immunotherapy from individual data by individuating the most important features from genomic data and clinical characteristics.

Methods

Machine learning was applied to identify a list of candidate genes that may predict immunotherapy benefits using data from the published cohort of 853 patients with NSCLC. We used XGBoost to capture nonlinear relations among many mutation genes and ICI benefits. The value of the derived machine learning-based mutation signature (ML-signature) on immunotherapy efficacy was evaluated and compared with the tumor mutational burden (TMB) and other clinical characteristics. The predictive power of ML-signature was also evaluated in independent cohorts of patients with NSCLC treated with ICI.

Results

We constructed the ML-signature based on 429 (training/validation = 8/2) patients who received immunotherapy and extracted 88 eligible predictive genes. Additionally, we conducted internal and external validation with the utility of the OAK+POPLAR dataset and independent cohorts, respectively. This ML-signature showed the enrichment in immune-related signaling pathways and compared to TMB, ML-signature was equipped with favorable predictive value and stratification.

Conclusion

Previous studies proposed no predictive difference between original TMB and modified TMB, and original TMB contains some genes with no predictive value. To demonstrate that fewer genetic tests are sufficient to predict immunotherapy efficacy, we used machine learning to screen out gene panels, which are used to calculate TMB. Therefore, we obtained the 88-gene panel, which showed the favorable prediction performance and stratification effect compared to the original TMB.

Establishment of an endoplasmic reticulum stress-related signature predicting outcomes of gastric adenocarcinoma patients

Background: Gastric adenocarcinoma (GAC) is a common clinical malignancy with a poor prognosis. Endoplasmic reticulum (ER) stress plays important roles in the progression, immune filtration, and chemoresistance of cancers. However, whether ER stress-related gene signatures can predict the prognosis of GAC patients remains unknown.

Methods: GAC patient RNA-seq data downloaded from The Cancer Genome Atlas and gastric cancer patient microarray data from Gene Expression Omnibus datasets were analyzed using LASSO regression to construct an ER stress-related signature. Survival analysis, time-dependent receiver operating characteristic (ROC) curves, and Cox regression analysis were used to verify the efficacy of the signature. Immune infiltration, somatic mutation, immune checkpoint, and copy number variation analyses were utilized to explore the potential biological significance of the signature.

Results: In the present study, eight ER stress-related gene signatures were constructed. Survival analysis showed that patients in the high-risk group had a significantly worse prognosis. The area under the time-dependent ROC curves was 0.65, 0.70, and 0.63 at 1, 3, and 5 years, respectively, in the training cohort. Cox regression analysis showed that the signature is an independent prognostic factor. To predict GAC patients’ prognosis meeting individual needs, a nomogram was constructed with good accuracy. In addition, gene set enrichment and immune infiltration analyses showed that the ER stress-related signature is associated with cancer-related pathway activation and an immunosuppressive tumor microenvironment in GAC.

Conclusion: In the current study, we established an ER stress-related signature. This prognostic signature has good predictive power and could facilitate the development of novel strategies for the clinical treatment of GAC.

Pervasive conditional selection of driver mutations and modular epistasis networks in cancer

Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistasis and quantifying its effect on tumor evolution remains a challenge. We develop a method (Coselens) to quantify conditional selection on the excess of nonsynonymous substitutions in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identify 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection affects 25%-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario where gene-specific across-pathway epistasis shapes differentiated cancer subtypes.

Differential roles and regulation of the protein kinases PAK4, PAK5 and PAK6 in melanoma cells

The protein kinases PAK4, PAK5 and PAK6 comprise a family of ohnologues. In multiple cancers including melanomas PAK5 most frequently carries non-synonymous mutations; PAK6 and PAK4 have fewer; and PAK4 is often amplified. To help interpret these genomic data, initially we compared the cellular regulation of the sister kinases and their roles in melanoma cells. In common with many ohnologue protein kinases, PAK4, PAK5 and PAK6 each have two 14-3-3-binding phosphosites of which phosphoSer99 is conserved. PAK4 localises to the leading edge of cells in response to phorbol ester-stimulated binding of 14-3-3 to phosphoSer99 and phosphoSer181, which are phosphorylated by two different PKCs or PKDs. These phosphorylations of PAK4 are essential for its phorbol ester-stimulated phosphorylation of downstream substrates. In contrast, 14-3-3 interacts with PAK5 in response to phorbol ester-stimulated phosphorylation of Ser99 and epidermal growth factor-stimulated phosphorylation of Ser288; whereas PAK6 docks onto 14-3-3 and is prevented from localising to cell–cell junctions when Ser133 is phosphorylated in response to cAMP-elevating agents via PKA and insulin-like growth factor 1 via PKB/Akt. Silencing of PAK4 impairs viability, migration and invasive behaviour of melanoma cells carrying BRAF^V600E or NRAS^Q61K mutations. These defects are rescued by ectopic expression of PAK4, more so by a 14-3-3-binding deficient PAK4, and barely by PAK5 or PAK6. Together these genomic, biochemical and cellular data suggest that the oncogenic properties of PAK4 are regulated by PKC–PKD signalling in melanoma, while PAK5 and PAK6 are dispensable in this cancer.

Occam's razor gets a new edge: the use of symmetries in model selection

We demonstrate the power of using symmetries for model selection in the context of mechanistic modelling. We analyse two different models called the power law model (PLM) and the immunological model (IM) describing the increase in cancer risk with age, due to mutation accumulation or immunosenescence, respectively. The IM fits several cancer types better than the PLM implying that it would be selected based on minimizing residuals. However, recently a symmetry-based method for model selection has been developed, which has been successfully used in an in silico setting to find the correct model when traditional model fitting has failed. Here, we apply this method in a real-world setting to investigate the mechanisms of carcinogenesis. First, we derive distinct symmetry transformations of the two models and then we select the model which not only fits the original data but is also invariant under transformations by its symmetry. Contrary to the initial conclusion, we conclude that the PLM realistically describes the mechanism underlying the colon cancer dataset. These conclusions agree with experimental knowledge, and this work demonstrates how a model selection criterion based on biological properties can be implemented using symmetries.

Aging: Lifespan and the evolution of somatic mutation rates

A new study finds an inverse correlation between lifespan and somatic mutation rate in mammals. This suggests an evolutionary relationship between aging and somatic mutation rates, perhaps mediated by selection against noncancerous selfish lineages.

Cancer Relevance of Human Genes

BACKGROUND

We hypothesize that genes that directly or indirectly interact with core cancer genes (CCGs) in a comprehensive gene-gene interaction network may have functional importance in cancer.

METHODS

We categorized 12 767 human genes into CCGs (n = 468), 1 (n = 5467), 2 (n = 5573), 3 (n = 915), and more than 3 steps (n = 416) removed from the nearest CCG in the Search Tool for the Retrieval of Interacting Genes/Proteins network. We estimated cancer-relevant functional importance in these neighborhood categories using 1) gene dependency score, which reflects the effect of a gene on cell viability after knockdown; 2) somatic mutation frequency in The Cancer Genome Atlas; 3) effect size that estimates to what extent a mutation in a gene enhances cell survival; and 4) negative selection pressure of germline protein-truncating variants in healthy populations.

RESULTS

Cancer biology-related functional importance of genes decreases as their distance from the CCGs increases. Genes closer to cancer genes show greater connectedness in the network, have greater importance in maintaining cancer cell viability, are under greater negative germline selection pressure, and have higher somatic mutation frequency in cancer. Based on these 4 metrics, we provide cancer relevance annotation to known human genes.

CONCLUSIONS

A large number of human genes are connected to CCGs and could influence cancer biology to various extent when dysregulated; any given mutation may be functionally important in one but not in another individual depending on genomic context.

Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review

With a 5-year survival rate of less than 50%, ovarian high-grade serous carcinoma (HGSC) is one of the most highly aggressive gynecological malignancies affecting women today. The high mortality rate of HGSC is largely attributable to delays in diagnosis, as most patients remain undiagnosed until the late stages of -disease. There are currently no recommended screening tests for ovarian cancer and there thus remains an urgent need for new diagnostic methods, particularly those that can detect the disease at early stages when clinical intervention remains effective. While diagnostics for ovarian cancer share many of the same technical hurdles as for other cancer types, the low prevalence of the disease in the general population, coupled with a notable lack of sensitive and specific biomarkers, have made the development of a clinically useful screening strategy particularly challenging. Here, we present a detailed review of the overall landscape of ovarian cancer diagnostics, with emphasis on emerging methods that employ novel protein, genetic, epigenetic and imaging-based biomarkers and/or advanced diagnostic technologies for the noninvasive detection of HGSC, particularly in women at high risk due to germline mutations such as BRCA1/2. Lastly, we discuss the translational potential of these approaches for achieving a clinically implementable solution for screening and diagnostics of early-stage ovarian cancer as a means of ultimately improving patient outcomes in both the general and high-risk populations.

Arginyl-tRNA-protein transferase 1 (ATE1) promotes melanoma cell growth and migration

Arginyl-tRNA-protein transferase 1 (ATE1) catalyses N-terminal protein arginylation, a post-translational modification implicated in cell migration, invasion and the cellular stress response. Herein, we report that ATE1 is overexpressed in NRAS-mutant melanomas, while it is downregulated in BRAF-mutant melanomas. ATE1 expression was higher in metastatic tumours, compared with primary tumours. Consistent with these findings, ATE1 depletion reduced melanoma cell viability, migration and colony formation. Reduced ATE1 expression also affected cell responses to mTOR and MEK inhibitors and to serum deprivation. Among putative ATE1 substrates is the tumour suppressor AXIN1, pointing to the possibility that ATE1 may fine-tune AXIN1 function in melanoma. Our findings highlight an unexpected role for ATE1 in melanoma cell aggressiveness and suggest that ATE1 constitutes a potential new therapeutic target.

Melanin and Melanin-Functionalized Nanoparticles as Promising Tools in Cancer Research-A Review

Cancer poses an ongoing global challenge, despite the substantial progress made in the prevention, diagnosis, and treatment of the disease. The existing therapeutic methods remain limited by undesirable outcomes such as systemic toxicity and lack of specificity or long-term efficacy, although innovative alternatives are being continuously investigated. By offering a means for the targeted delivery of therapeutics, nanotechnology (NT) has emerged as a state-of-the-art solution for augmenting the efficiency of currently available cancer therapies while combating their drawbacks. Melanin, a polymeric pigment of natural origin that is widely spread among many living organisms, became a promising candidate for NT-based cancer treatment owing to its unique physicochemical properties (e.g., high biocompatibility, redox behavior, light absorption, chelating ability) and innate antioxidant, photoprotective, anti-inflammatory, and antitumor effects. The latest research on melanin and melanin-like nanoparticles has extended considerably on many fronts, allowing not only efficient cancer treatments via both traditional and modern methods, but also early disease detection and diagnosis. The current paper provides an updated insight into the applicability of melanin in cancer therapy as antitumor agent, molecular target, and delivery nanoplatform.

Rapid Multiplex Strip Test for the Detection of Circulating Tumor DNA Mutations for Liquid Biopsy Applications

In the era of personalized medicine, molecular profiling of patient tumors has become the standard practice, especially for patients with advanced disease. Activating point mutations of the KRAS proto-oncogene are clinically relevant for many types of cancer, including colorectal cancer (CRC). While several approaches have been developed for tumor genotyping, liquid biopsy has been gaining much attention in the clinical setting. Analysis of circulating tumor DNA for genetic alterations has been challenging, and many methodologies with both advantages and disadvantages have been developed. We here developed a gold nanoparticle-based rapid strip test that has been applied for the first time for the multiplex detection of KRAS mutations in circulating tumor DNA (ctDNA) of CRC patients. The method involved ctDNA isolation, PCR-amplification of the KRAS gene, multiplex primer extension (PEXT) reaction, and detection with a multiplex strip test. We have optimized the efficiency and specificity of the multiplex strip test in synthetic DNA targets, in colorectal cancer cell lines, in tissue samples, and in blood-derived ctDNA from patients with advanced colorectal cancer. The proposed strip test achieved rapid and easy multiplex detection (normal allele and three major single-point mutations) of the clinically relevant KRAS mutations in ctDNA in blood samples of CRC patients with high specificity and repeatability. This multiplex strip test represents a minimally invasive, rapid, low-cost, and promising diagnostic tool for the detection of clinically relevant mutations in cancer patients.

Find the path of least resistance: Adaptive therapy to delay treatment failure and improve outcomes

Cytotoxic chemotherapy and targeted therapies help people with advanced cancers, but for most, treatment fails. Cancer heterogeneity is one cause of treatment failure, but also suggests an opportunity to improve outcomes; reconceptualising cancer therapy as an ecological problem offers the strategy of adaptive therapy. If an agent is active against a patient's cancer, instead of traditional continuous dosing at the maximum tolerated dose until treatment failure, the patient and their oncologist may instead choose to pause treatment as soon as the cancer responds. When tumour burden increases, the cancer is rechallenged with the same agent in hope of delivering another response, ideally before symptoms occur or quality-of-life is impacted. These 'loops' of 'pause/restart' allows an active treatment to be used strategically, to delay the development of evolutionary selection within the cancer, delaying the onset of treatment resistance, controlling the cancer for longer. Modelling predicts patients can navigate several 'loops', potentially increasing the utility of an active treatment by multiples, and early trials suggest at least doubling of progression-free survival. In this narrative review we confront how cancer heterogeneity limits treatment effectiveness, re-examine cancer as an ecological problem, review the data supporting adaptive therapy and outline the challenges and opportunities faced in clinical practice to implement this evolutionary concept. In an era where multiple novel active anti-neoplastic agents are being used with ancient inflexibile maximum tolerated dose for maximum duration approaches, adaptive dosing offers a personalised, n = 1 approach to cancer therapy selection.

Dynamic Cancer Cell Heterogeneity: Diagnostic and Therapeutic Implications

Though heterogeneity of cancers is recognized and has been much discussed in recent years, the concept often remains overlooked in different routine examinations. Indeed, in clinical or biological articles, reviews, and textbooks, cancers and cancer cells are generally presented as evolving distinct entities rather than as an independent heterogeneous cooperative cell population with its self-oriented biology. There are, therefore, conceptual gaps which can mislead the interpretations/diagnostic and therapeutic approaches. In this short review, we wish to summarize and discuss various aspects of this dynamic evolving heterogeneity and its biological, pathological, clinical, diagnostic, and therapeutic implications, using thyroid carcinoma as an illustrative example.

A proposed Information-Based modality for the treatment of cancer

Treatment modalities for cancer involve physical manipulations such as surgery, immunology, radiation, chemotherapy or gene editing. This is a proposal for an information-based modality. This modality does not change the internal state of the cancer cell directly - instead, the cancer cell is manipulated by giving it information to instruct the cell to perform an action. This modality is based on a theory of Structure Encoding in DNA, where information about body part structure controls the epigenetic state of cells in the process of development from pluripotent cells to fully differentiated cells. It has been noted that cancer is often due to errors in morphogenetic differentiation accompanied by associated epigenetic processes. This implies a model of cancer called the Epigenetic Differentiation Model. A major feature of the Structure Encoding Theory is that the characteristics of the differentiated cell are affected by inter-cellular information passed in the tissue microenvironment, which specifies the exact location of a cell in a body part structure. This is done by exosomes that carry fragments of long non-coding RNA and transposons, which convey structure information. In the normal process of epigenetic differentiation, the information passed may lead to apoptosis due to the constraints of a particular body part structure. The proposed treatment involves determining what structure information is being passed in a particular tumor, then adding artificial exosomes that overwhelm the current information with commands for the cells to go into apoptosis.

Organ Specific Copy Number Variations in Visceral Metastases of Human Melanoma

Malignant melanoma is one of the most aggressive skin cancers with high potential of visceral dissemination. Since the information about melanoma genomics is mainly based on primary tumors and lymphatic or skin metastases, an autopsy-based visceral metastasis biobank was established. We used copy number variation arrays (N = 38 samples) to reveal organ specific alterations. Results were partly completed by proteomic analysis. A significant increase of high-copy number gains was found in an organ-specific manner, whereas copy number losses were predominant in brain metastases, including the loss of numerous DNA damage response genes. Amplification of many immune genes was also observed, several of them are novel in melanoma, suggesting that their ectopic expression is possibly underestimated. This "immunogenic mimicry" was exclusive for lung metastasis. We also provided evidence for the possible autocrine activation of c-MET, especially in brain and lung metastases. Furthermore, frequent loss of 9p21 locus in brain metastases may predict higher metastatic potential to this organ. Finally, a significant correlation was observed between BRAF gene copy number and mutant allele frequency, mainly in lung metastases. All of these events may influence therapy efficacy in an organ specific manner, which knowledge may help in alleviating difficulties caused by resistance.

Profound synchrony of age-specific incidence rates and tumor suppression for different cancer types as revealed by the multistage-senescence model of carcinogenesis

The age-specific trend of cancer incidence rates, but not its magnitude, is well described employing the multistage theory of carcinogenesis by Armitage and Doll in combination with the senescence model of Pompei and Wilson. We derived empirical parameters of the multistage-senescence model from U.S. Surveillance, Epidemiology, and End Results (SEER) incidence data from 2000–2003 and 2010–2013 for The Cancer Genome Atlas (TCGA) cancer types. Under the assumption of a constant tumor-specific transition rate between stages, there is an extremely strong linear relationship (P < 0.0001) between the number of stages and the stage transition rate. The senescence tumor suppression factor for 20 non-reproductive cancers is remarkably consistent (0.0099±0.0005); however, five female reproductive cancers have significantly higher tumor suppression. The peak incidence rate for non-reproductive cancers occurs at a younger age for cancers with fewer stages and their carcinogenic stages are of longer duration. Driver gene mutations are shown to contribute on average only about a third of the carcinogenic stages of different tumor types. A tumor’s accumulated incidence, calculated using a two-variable (age, stage) model, is strongly associated with intrinsic cancer risk. During both early adulthood and senescence, the pace of tumor suppression appears to be synchronized across most cancer types, suggesting the presence of overlapping evolutionary processes.

A molecular taxonomy of tumors independent of tissue-of-origin

Cancer is an organism-level disease, impacting processes from cellular metabolism and the microenvironment to systemic immune response. Nevertheless, efforts to distinguish overarching mutational processes from interactions with the cell of origin for a tumor have seen limited success, presenting a barrier to individualized medicine. Here we present a pathway-centric approach, extracting somatic mutational profiles within and between tissues, largely orthogonal to cell of origin, mutational burden, or stage. Known predisposition variants are equally distributed among clusters, and largely independent of molecular subtype. Prognosis and risk of death vary jointly by cancer type and cluster. Analysis of metastatic tumors reveals that differences are largely cluster-specific and complementary, implicating convergent mechanisms that combine familiar driver genes with diverse low-frequency lesions in tumor-promoting pathways, ultimately producing distinct molecular phenotypes. The results shed new light on the interplay between organism-level dysfunction and tissue-specific lesions.

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Pathway-based clustering is applied to reclassify tumors using somatic mutations

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Clusters represent distinct molecular features that cut across tissue-of-origin

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Risk of death is modelled using tissue origin and cluster membership

Targetable Pathways in Advanced Bladder Cancer: FGFR Signaling

Bladder cancer is the 10th most commonly diagnosed cancer in the world, accounting for around 573,000 new cases and 213,000 deaths in 2020. The current standard treatment for locally advanced bladder cancer is neoadjuvant cisplatin (NAC)-based chemotherapy followed by cystectomy. The significant progress being made in the genomic and molecular understandings of bladder cancer has uncovered the genetic alterations and signaling pathways that drive bladder cancer progression. These developments have led to a dramatic increase in the evaluation of molecular agents targeting at these alterations. One example is Erdafitinib, a first-in-class FGFR inhibitor being approved as second-line treatment for locally advanced or metastatic urothelial carcinoma with FGFR mutations. Immunotherapy has also been approved as second-line treatment for advanced and metastatic bladder cancer. Preclinical studies suggest targeted therapy combined with immunotherapy has the potential to markedly improve patient outcome. Given the prevalence of FGFR alternations in bladder cancer, here we review recent preclinical and clinical studies on FGFR inhibitors and analyze possible drug resistance mechanisms to these agents. We also discuss FGFR inhibitors in combination with other therapies and its potential to improve outcome.

Activation of 4-1BBL+ B cells with CD40 agonism and IFNγ elicits potent immunity against glioblastoma

Immunotherapy has revolutionized the treatment of many tumors. However, most glioblastoma (GBM) patients have not, so far, benefited from such successes. With the goal of exploring ways to boost anti-GBM immunity, we developed a B cell-based vaccine (BVax) that consists of 4-1BBL+ B cells activated with CD40 agonism and IFNγ stimulation. BVax migrates to key secondary lymphoid organs and is proficient at antigen cross-presentation, which promotes both the survival and the functionality of CD8+ T cells. A combination of radiation, BVax, and PD-L1 blockade conferred tumor eradication in 80% of treated tumor-bearing animals. This treatment elicited immunological memory that prevented the growth of new tumors upon subsequent reinjection in cured mice. GBM patient-derived BVax was successful in activating autologous CD8+ T cells; these T cells showed a strong ability to kill autologous glioma cells. Our study provides an efficient alternative to current immunotherapeutic approaches that can be readily translated to the clinic.

The reversed intra- and extracellular pH in tumors as a unified strategy to chemotherapeutic delivery using targeted nanocarriers

Solid tumors are complex entities, comprising a wide variety of malignancies with very different molecular alterations. Despite this, they share a set of characteristics known as "hallmarks of cancer" that can be used as common therapeutic targets. Thus, every tumor needs to change its metabolism in order to obtain the energy levels required for its high proliferative rates, and these adaptations lead to alterations in extra- and intracellular pH. These changes in pH are common to all solid tumors, and can be used either as therapeutic targets, blocking the cell proton transporters and reversing the pH changes, or as means to specifically deliver anticancer drugs. In this review we will describe how proton transport inhibitors in association with nanocarriers have been designed to block the pH changes that are needed for cancer cells to survive after their metabolic adaptations. We will also describe studies aiming to decrease intracellular pH in cancer using nanoparticles as molecular cages for protons which will be released upon UV or IR light exposure. Finally, we will comment on several studies that have used the extracellular pH in cancer for an enhanced cell internalization and tumor penetration of nanocarriers and a controlled drug delivery, describing how nanocarriers are being used to increase drug stability and specificity.

Multiomics analysis reveals CT83 is the most specific gene for triple negative breast cancer and its hypomethylation is oncogenic in breast cancer

Triple-negative breast cancer (TNBC) is a highly aggressive breast cancer (BrC) subtype lacking effective therapeutic targets currently. The development of multi-omics databases facilities the identification of core genes for TNBC. Using TCGA-BRCA and METABRIC datasets, we identified CT83 as the most TNBC-specific gene. By further integrating FUSCC-TNBC, CCLE, TCGA pan-cancer, Expression Atlas, and Human Protein Atlas datasets, we found CT83 is frequently activated in TNBC and many other cancers, while it is always silenced in non-TNBC, 120 types of normal non-testis tissues, and 18 types of blood cells. Notably, according to the TCGA-BRCA methylation data, hypomethylation on chromosome X 116,463,019 to 116,463,039 is significantly correlated with the abnormal activation of CT83 in BrC. Using Kaplan-Meier Plotter, we demonstrated that activated CT83 is significantly associated with unfavorably overall survival in BrC and worse outcomes in some other cancers. Furthermore, GSEA suggested that the abnormal activation of CT83 in BrC is probably oncogenic by triggering the activation of cell cycle signaling. Meanwhile, we also noticed copy number variations and mutations of CT83 are quite rare in any cancer type, and its role in immune infiltration is not significant. In summary, we highlighted the significance of CT83 for TNBC and presented a comprehensive bioinformatics strategy for single-gene analysis in cancer.

Sex disparities matter in cancer development and therapy

Curing cancer through precision medicine is the paramount aim of the new wave of molecular and genomic therapies. Currently, whether patients with non-reproductive cancers are male or female according to their sex chromosomes is not adequately considered in patient standard of care. This is a matter of consequence because there is growing evidence that these cancer types generally initiate earlier and are associated with higher overall incidence and rates of death in males compared with females. Gender, in contrast to sex, refers to a chosen sexual identity. Hazardous lifestyle choices (notably tobacco smoking) differ in prevalence between genders, aligned with disproportionate cancer risk. These add to underlying genetic predisposition and influences of sex steroid hormones. Together, these factors affect metabolism, immunity and inflammation, and ultimately the fidelity of the genetic code. To accurately understand how human defences against cancer erode, it is crucial to establish the influence of sex. Our Perspective highlights evidence from basic and translational research indicating that including genetic sex considerations in treatments for patients with cancer will improve outcomes. It is now time to adopt the challenge of overhauling cancer medicine based on optimized treatment strategies for females and males.