Distinct Cancer Research Articles

A triple hormone receptor ER, AR, and VDR signature is a robust prognosis predictor in breast cancer

BackgroundDespite evidence indicating the dominance of cell-of-origin signatures in molecular tumor patterns, translating these genome-wide patterns into actionable insights has been challenging. This study introduces breast cancer cell-of-origin signatures that offer significant prognostic value across all breast cancer subtypes and various clinical cohorts, compared to previously developed genomic signatures.MethodsWe previously reported that triple hormone receptor (THR) co-expression patterns of androgen (AR), estrogen (ER), and vitamin D (VDR) receptors are maintained at the protein level in human breast cancers. Here, we developed corresponding mRNA signatures (THR-50 and THR-70) based on these patterns to categorize breast tumors by their THR expression levels. The THR mRNA signatures were evaluated across 56 breast cancer datasets (5040 patients) using Kaplan–Meier survival analysis, Cox proportional hazard regression, and unsupervised clustering.ResultsThe THR signatures effectively predict both overall and progression-free survival across all evaluated datasets, independent of subtype, grade, or treatment status, suggesting improvement over existing prognostic signatures. Furthermore, they delineate three distinct ER-positive breast cancer subtypes with significant survival in differences—expanding on the conventional two subtypes. Additionally, coupling THR-70 with an immune signature identifies a predominantly ER-negative breast cancer subgroup with a highly favorable prognosis, comparable to ER-positive cases, as well as an ER-negative subgroup with notably poor outcome, characterized by a 15-fold shorter survival.ConclusionsThe THR cell-of-origin signature introduces a novel dimension to breast cancer biology, potentially serving as a robust foundation for integrating additional prognostic biomarkers. These signatures offer utility as a prognostic index for stratifying existing breast cancer subtypes and for de novo classification of breast cancer cases. Moreover, THR signatures may also hold promise in predicting hormone treatment responses targeting AR and/or VDR.

Design and optimization of refractive index biosensor for MDA-MB-231 and MCF-7 breast cancer biomarker detection

Breast cancer is the leading malignancy in women and the 2nd widespread cancer globally. Earlier identification of breast cancer can improve treatment outcomes and prevent metastasis beyond the breast. Traditional screening tests are not sensitive enough for early diagnosis and have extended detection periods. Recent studies have explored diversified Breast cancer biosensor techniques, including optical, electrical, electrochemical, and mechanical biosensors. This paper nominates a circlet with a large plus sign refraction indices biosensor for the evaluation of two distinct breast cancer cells namely, MDA-MB-231 and MCF-7. The supreme sensitivity has been viewed as 1714.28 nm/RIU for the MDA-MB-231 and 1714.28 nm/RIU for the MCF-7. The supreme quality factor (QF) value for UC1(usual cell of MDA-MB-231) is 10.52, for CC1(cancer cell of MDA-MB-231) is 11.94, for UC2 (usual cell of MCF-7) is 10.64, and for CC2 (cancer cell of MCF-7) is 12.09. The minimal detection value (DL) for UC1 is 0.1573, CC1 is 0.1360, UC2 is 0.1554, and CC2 is 0.1341. This nominated sensor has the potential to sense breast tumor biomarkers.

Pharmacomicrobiomics in precision cancer therapy: bench to bedside.

The burgeoning field of pharmacomicrobiomics offers promising insights into the intricate interplay between the microbiome and cancer, shaping responses to diverse treatment modalities. This review aims to analyze the molecular mechanisms underlying interactions between distinct microbiota types and cancer, as well as their influence on treatment outcomes. We explore how the microbiome impacts antitumor immunity, and response to chemotherapy, immunotherapy, and radiation therapy, unveiling its multifaceted roles in cancer progression and therapy resistance. Moreover, we discuss the challenges hindering the development of microbiome-based interventions in cancer therapy, including standardization, validation, and clinical translation. By synthesizing clinical evidence, we underscore the transformative potential of harnessing pharmacomicrobiomics in guiding cancer treatment decisions, paving the way for improved patient outcomes in clinical practice.

Shikonin mitigates cyclophosphamide-induced cardiotoxicity in mice: the role of sirtuin-1, NLRP3 inflammasome, autophagy, and apoptosis.

The aim of this study was to elucidate the protective potential of shikonin (SHK) on cyclophosphamide (CP)-induced cardiotoxicity in Swiss albino mice. Mice received SHK in three different doses by oral gavage daily for 14 days and CP at 100mg/kg, intraperitoneally once on the seventh day. On the 15th day, mice were euthanized, blood collected, and hearts were removed to estimate various biochemical and histopathological parameters. CP significantly increased serum lactate dehydrogenase, creatine kinase-MB, troponin I and NT pro-BNP, and cardiac malondialdehyde and decreased cardiac total antioxidant capacity and Nrf2, whereas increased inflammatory markers in the cardiac tissues. CP also caused hypertrophy and fibrosis in the cardiac tissues via activation of IL6/JAK2/STAT3 while depressed SIRT1 and PI3K/p-Akt pathway with consequent increased apoptosis and dysregulation of autophagy. SHK treatment reversed these changes in a dose-dependent manner and showed a significant protective effect against CP-induced cardiotoxicity via suppressing oxidative stress, inflammation, and apoptosis with modulation of autophagy via induction of SIRT1/PI3K/p-Akt signaling. Shikonin may be used as an adjuvant to cyclophosphamide in cancer treatment, but further research is needed to investigate its effects on cardiotoxicity in distinct animal cancer models.

Immunogenomic features of radiologically distinctive nodules in multiple primary lung cancer

ObjectivesTo provide molecular and immunological attributes mechanistic insights for the management of radiologically distinctive multiple primary lung cancer (MPLC).MethodsThe Bulk RNA-seq data of MPLC were obtained from our center. The Bulk RNA-seq data and CT images of patients with single primary lung cancer (SPLC) were obtained from GSE103584. Immune infiltration algorithms were performed to investigate the disparities in the immunological microenvironment between the two groups. Single-cell gene analysis was used to explore immune cells composition and communication relationships between cells in MPLC.ResultsIn MPLC, 11 pure ground-glass opacity nodules (pGGN) and 10 mixed GGN (mGGN) were identified, while in SPLC, the numbers were 18 pGGN and 22 mGGN, respectively. In MPLC, compared to pGGN, mGGN demonstrated a significantly elevated infiltration of CD8+ T cells. Single-cell gene analysis demonstrated that CD8+ T cells play a central role in the signaling among immune cells in MPLC. The transcription factors including MAFG, RUNX3, and TBX21 may play pivotal roles in regulation of CD8+ T cells. Notably, compared to SPLC nodules for both mGGN and pGGN, MPLC nodules demonstrated a significantly elevated degree of tumor-infiltrating immune cells, with this difference being particularly pronounced in mGGN. There was a positive correlation between the proportion of immune cells and consolidation/tumor ratio (CTR).ConclusionsOur findings provided a comprehensive description about the difference in the immune microenvironment between pGGN and mGGN in early-stage MPLC, as well as between MPLC and SPLC for both mGGN and pGGN. The findings may provide evidence for the design of immunotherapeutic strategies for MPLC.

Design and evaluation of sulfadiazine derivatives as potent dual inhibitors of EGFRWT and EGFRT790M: integrating biological, molecular docking, and ADMET analysis.

A series of derivatives (5-14) were synthesized through the diazotization of sulfadiazine with active methylene compounds. The chemical structures of these newly designed compounds were validated through spectral and elemental analysis techniques. The antiproliferative potential of derivatives 5-14 was assessed against three distinct cancer cell lines (A431, A549, and H1975) using the MTT assay. The results revealed that compounds 8, 12, and 14 exhibited the most potent antiproliferative activity, with IC50 values ranging from 2.31 to 7.56 μM. Notably, these values were significantly lower than those of known EGFR inhibitors, including erlotinib, gefitinib, and osimertinib, suggesting the potential of these derivatives as novel antiproliferative agents. Furthermore, compound 12 was identified as the most potent inhibitor of both EGFRWT and EGFRT790M protein kinases, with IC50 values of 14.5 and 35.4 nM, respectively. These results outperformed those of gefitinib and osimertinib, which exhibited IC50 values of 18.2 and 368.2 nM, and 57.8 and 8.5 nM, respectively. Molecular docking studies of compounds 8, 12, and 14 within the ATP-binding sites of both EGFRWT and EGFRT790M corroborated the in vitro results when compared to erlotinib, gefitinib, and osimertinib. The docking results indicated that compound 8 exhibited a favorable binding affinity for both EGFRWT and EGFRT790M, with binding scores of -6.40 kcal mol-1 and -7.53 kcal mol-1, respectively, which were comparable to those of gefitinib and osimertinib, with binding scores of -8.01 and -8.72 kcal mol-1, respectively. Furthermore, an assessment of the most promising EGFR inhibitors (8, 12, and 14) using the egg-boiled method for their in silico ADME properties revealed significant lipophilicity, blood-brain barrier (BBB) penetration, and gastrointestinal (GIT) absorption. Collectively, our designed analogs, particularly compounds 8, 12, and 14, exhibit promising dual antiproliferative and EGFRWT and EGFRT790M kinase inhibitory properties, positioning them as efficient candidates for further therapeutic development.

Shenqi Sanjie Granules induce Hmox1-mediated ferroptosis to inhibit colorectal cancer

BackgroundBecause adverse reactions or drug resistance are often found after current chemotherapies for metastatic colorectal cancer (mCRC), new treatments are still in demand. Shenqi Sanjie Granules (SSG), an antitumor compound preparation of traditional Chinese medicine, has been recognized for its ability in clinical practice of oncotherapy. Nevertheless, the precise effects of SSG in colorectal cancer (CRC) and underlying mechanisms through which SSG inhibits CRC remain uncertain. The current study aimed to evaluate the anti-CRC activity of the Chinese herbal compound preparation SSG and investigate the underlying mechanisms of action. Materials and methodsInitially, nine distinct cancer cell lines, including five CRC cell lines, one breast cancer cell line, two lung adenocarcinoma cell lines and one cervical cancer cell line, were used to evaluate the antitumor activity of SSG, and the mouse CRC cell line CT26 were used for further research. In vitro experiments utilizing diverse assays were conducted to assess the inhibitory effects of the SSG on CT26. Furthermore, subcutaneous syngeneic mouse model and AOM (azoxymethane)/DSS (dextran sodium sulfate) induced in-situ colitis-related mouse CRC model were used to evaluate the antitumor potential and biotoxicity of SSG in vivo. To elucidate the underlying molecular mechanisms, transcriptome sequencing and network pharmacology analysis were performed. Meanwhile, verification is carried out with quantitative real-time PCR (qRT-PCR) and flow cytometry (FCM) analysis. ResultsOur in vitro inhibition study showed that SSG could effectively inhibit CRC cell line CT26 growth and metastasis, and induce cell death. Neither of apoptosis inhibitor, necroptosis inhibitor, ferroptosis inhibitor, but the combination of the three diminished SSG-induced cell death, suggesting that multiple cell death pathways were involved. Both the syngeneic CRC model and the in-situ CRC model indicated SSG inhibited CRC in vivo with few toxic side effects. Further mechanistic study suggested SSG treatment activated the ferroptosis pathway, particularly mediated by Hmox1, which was upregulated scores of times. Network pharmacology analysis indicated that the active ingredients of SSG, including Quercetin, Luteolin and Kaempferol were potential components directly upregulated Hmox1 expression. ConclusionsCollectively, our findings indicate that the administration of SSG has the potential to inhibit CRC both in vitro and in vivo. The mechanism by which this compound preparation exerts its action is, at least partly, the induction of ferroptosis through upregulating Hmox-1 by its three active ingredients Quercetin, Luteolin and Kaempferol.

Comprehensive Analysis of Microsatellite Instability in Canine Cancers: Implications for Comparative Oncology and Personalized Veterinary Medicine.

Microsatellite instability (MSI) is a crucial feature in cancer biology, yet its prevalence and significance in canine cancers remain largely unexplored. This study conducted a comprehensive analysis of MSI across 10 distinct canine cancer histotypes using whole-exome sequencing data from 692 tumor-normal sample pairs. MSI was detected in 64% of tumors, with prevalence varying significantly among cancer types. B-cell lymphomas exhibited the highest MSI burden, contrasting with human studies. A novel "MSI-burden" score was developed, correlating significantly with tumor mutational burden. MSI-high (MSI-H) tumors showed elevated somatic mutation counts compared to MSI-low and microsatellite stable tumors. The study identified 3632 recurrent MSI-affected genomic regions across cancer types. Notably, seven of the ten cancer types exhibited MSI-H tumors, with prevalence ranging from 1.5% in melanomas to 37% in B-cell lymphomas. These findings highlight the potential importance of MSI in canine cancer biology and suggest opportunities for targeted therapies, particularly immunotherapies. The high prevalence of MSI in canine cancers, especially in B-cell lymphomas, warrants further investigation into its mechanistic role and potential as a biomarker for prognosis and treatment response.

The SWI/SNF PBAF complex facilitates REST occupancy at repressive chromatin.

Multimeric SWI/SNF chromatin remodelers assemble into discrete conformations with unique complex functionalities difficult to dissect. Distinct cancers harbor mutations in specific subunits, altering the chromatin landscape, such as the PBAF-specific component ARID2 in melanoma. Here, we performed comprehensive epigenomic profiling of SWI/SNF complexes and their associated chromatin states in melanoma and melanocytes and uncovered a subset of PBAF-exclusive regions that coexist with PRC2 and repressive chromatin. Time-resolved approaches revealed that PBAF regions are generally less sensitive to ATPase-mediated remodeling than BAF sites. Moreover, PBAF/PRC2-bound loci are enriched for REST, a transcription factor that represses neuronal genes. In turn, absence of ARID2 and consequent PBAF complex disruption hinders the ability of REST to bind and inactivate its targets, leading to upregulation of synaptic transcripts. Remarkably, this gene signature is conserved in melanoma patients with ARID2 mutations. In sum, we demonstrate a unique role for PBAF in generating accessibility for a silencing transcription factor at repressed chromatin, with important implications for disease.

Spatial transcriptomics profiling of gallbladder adenocarcinoma: a detailed two-case study of progression from precursor lesions to cancer

BackgroundMost studies on tumour progression from precursor lesion toward gallbladder adenocarcinoma investigate lesions sampled from distinct patients, providing an overarching view of pathogenic cascades. Whether this reflects the tumourigenic process in individual patients remains insufficiently explored. Genomic and epigenomic studies suggest that a subset of gallbladder cancers originate from biliary intraepithelial neoplasia (BilIN) precursor lesions, whereas others form independently from BilINs. Spatial transcriptomic data supporting these conclusions are missing. Moreover, multiple areas with precursor or adenocarcinoma lesions can be detected within the same pathological sample. Yet, knowledge about intra-patient variability of such lesions is lacking.MethodsTo characterise the spatial transcriptomics of gallbladder cancer tumourigenesis in individual patients, we selected two patients with distinct cancer aetiology and whose samples simultaneously displayed multiple areas of normal epithelium, BilINs and adenocarcinoma. Using GeoMx digital spatial profiling, we characterised the whole transcriptome of a high number of regions of interest (ROIs) per sample in the two patients (24 and 32 ROIs respectively), with each ROI covering approximately 200 cells of normal epithelium, low-grade BilIN, high-grade BilIN or adenocarcinoma. Human gallbladder organoids and cell line-derived tumours were used to investigate the tumour-promoting role of genes.ResultsSpatial transcriptomics revealed that each type of lesion displayed limited intra-patient transcriptomic variability. Our data further suggest that adenocarcinoma derived from high-grade BilIN in one patient and from low-grade BilIN in the other patient, with co-existing high-grade BilIN evolving via a distinct process in the latter case. The two patients displayed distinct sequences of signalling pathway activation during tumour progression, but Semaphorin 4 A (SEMA4A) expression was repressed in both patients. Using human gallbladder-derived organoids and cell line-derived tumours, we provide evidence that repression of SEMA4A promotes pseudostratification of the epithelium and enhances cell migration and survival.ConclusionGallbladder adenocarcinoma can develop according to patient-specific processes, and limited intra-patient variability of precursor and cancer lesions was noticed. Our data suggest that repression of SEMA4A can promote tumour progression. They also highlight the need to gain gene expression data in addition to histological information to avoid understimating the risk of low-grade preneoplastic lesions.

DB-FCN: An end-to-end dual-branch fully convolutional nucleus detection model

Detecting cells or nuclei in histopathological image analysis is a crucial prerequisite for subsequent cancer diagnosis. By precisely locating cell nuclei, pathologists can quantitatively analyze the morphology of each cell nucleus. This enables accurate cancer grading, allowing for the implementation of tailored treatment plans based on distinct cancer stages. Manual nucleus detection methods are labor-intensive, making the development of automatic cell nucleus detection algorithms highly necessary. However, due to the small size of the nucleus and increased adhesions between cells, tissue staining may be uneven, leading to a higher occurrence of false positives in the results of the current automatic nucleus detection algorithm. This paper introduces an end-to-end dual-branch-based fully convolutional neural network (DB-FCN) that effectively addresses the aforementioned challenges, thereby enhancing the accuracy of automatic nucleus detection. This algorithm introduces for the first time the use of two detection branches, namely the coarse detection branch and the fine detection branch, to accomplish the detection task. The role of the coarse detection branch is to identify all cell regions in the pathological image as comprehensively as possible and then transmit the detection results as prior information to the fine detection branch. The fine detection branch is necessary solely to conduct more precise detection based on the coarse detection results. Given that the coarse detection branch has already eliminated interference from many background regions, the fine detection branch can focus on detecting the nucleus region, thereby significantly enhancing the efficiency and accuracy of model detection. The detection model proposed in this paper was evaluated on three classic datasets and compared with many existing detection algorithms. Compared with other algorithms, the detection algorithm proposed in this paper has made significant progress in detecting cell nuclei in histopathological images.

Extracellular Vesicles heterogeneity through the lens of multiomics.

Extracellular vesicles (EVs) are heterogenous in size, biogenesis, cargo and function. Beside small EVs, aggressive tumor cells release a population of particularly large EVs, namely large oncosomes (LO). This study provides the first resource of label-free quantitative proteomics of LO and small EVs obtained from distinct cancer cell types (prostate, breast, and glioma). This dataset was integrated with a SWATH Proteomic assay on LO, rigorously isolated from the plasma of patients with metastatic prostate cancer (PC). Proteins enriched in LO, which were identified also at the RNA level, and found in plasma LO significantly correlated with PC progression. Single EV RNA-Seq of the PC cell-derived LO confirmed some of the main findings from the bulk RNA-Seq, providing first evidence that single cell technologies can be successfully applied to EVs. This multiomics resource of cancer EVs can be leveraged for developing a multi-analyte approach for liquid biopsy.

Tumor-associated macrophage clusters linked to immunotherapy in a pan-cancer census

Transcriptional heterogeneity of tumor-associated macrophages (TAMs) has been investigated in individual cancers, but the extent to which these states transcend tumor types and represent a general feature of cancer remains unclear. We performed pan-cancer single-cell RNA sequencing analysis across nine cancer types and identified distinct monocyte/TAM composition patterns. Using spatial analysis from clinical study tissues, we assessed TAM functions in shaping the tumor microenvironment (TME) and influencing immunotherapy. Two specific TAM clusters (pro-inflammatory and pro-tumor) and four TME subtypes showed distinct immunological features, genomic profiles, immunotherapy responses, and cancer prognosis. Pro-inflammatory TAMs resided in immune-enriched niches with exhausted CD8+ T cells, while pro-tumor TAMs were restricted to niches associated with a T-cell-excluded phenotype and hypoxia. We developed a machine learning model to predict immune checkpoint blockade response by integrating TAMs and clinical data. Our study comprehensively characterizes the common features of TAMs and highlights their interaction with the TME.

Exploit Spatially Resolved Transcriptomic Data to Infer Cellular Features from Pathology Imaging Data.

Digital pathology is a rapidly advancing field where deep learning methods can be employed to extract meaningful imaging features. However, the efficacy of training deep learning models is often hindered by the scarcity of annotated pathology images, particularly images with detailed annotations for small image patches or tiles. To overcome this challenge, we propose an innovative approach that leverages paired spatially resolved transcriptomic data to annotate pathology images. We demonstrate the feasibility of this approach and introduce a novel transfer-learning neural network model, STpath (Spatial Transcriptomics and pathology images), designed to predict cell type proportions or classify tumor microenvironments. Our findings reveal that the features from pre-trained deep learning models are associated with cell type identities in pathology image patches. Evaluating STpath using three distinct breast cancer datasets, we observe its promising performance despite the limited training data. STpath excels in samples with variable cell type proportions and high-resolution pathology images. As the influx of spatially resolved transcriptomic data continues, we anticipate ongoing updates to STpath, evolving it into an invaluable AI tool for assisting pathologists in various diagnostic tasks.

Selective Estrogen Receptor Modulators' (SERMs) Influence on TET3 Expression in Breast Cancer Cell Lines with Distinct Biological Subtypes.

Tamoxifen, a selective estrogen receptor modulator (SERM), exhibits dual agonist or antagonist effects contingent upon its binding to either G-protein-coupled estrogen receptor (GPER) or estrogen nuclear receptor (ESR). Estrogen signaling plays a pivotal role in initiating epigenetic alterations and regulating estrogen-responsive genes in breast cancer. Employing three distinct breast cancer cell lines-MCF-7 (ESR+; GPER+), MDA-MB-231 (ESR-; GPER-), and SkBr3 (ESR-; GPER+)-this study subjected them to treatment with two tamoxifen derivatives: 4-hydroxytamoxifen (4-HT) and endoxifen (Endox). Through 2D high-performance liquid chromatography with tandem mass spectrometry detection (HPLC-MS/MS), varying levels of 5-methylcytosine (5-mC) were found, with MCF-7 displaying the highest levels. Furthermore, TET3 mRNA expression levels varied among the cell lines, with MCF-7 exhibiting the lowest expression. Notably, treatment with 4-HT induced significant changes in TET3 expression across all cell lines, with the most pronounced increase seen in MCF-7 and the least in MDA-MB-231. These findings underscore the influence of tamoxifen derivatives on DNA methylation patterns, particularly through modulating TET3 expression, which appears to be contingent on the presence of estrogen receptors. This study highlights the potential of targeting epigenetic modifications for personalized anti-cancer therapy, offering a novel avenue to improve treatment outcomes.

Abstract Mo019: Angiotensin II Links Cardiovascular Disease With Enhanced Cancer Growth

Introduction: It has been demonstrated that CVD promote tumor growth in mouse models of breast, lung, and colon cancer, suggesting a causal relationship between both diseases. Cancer and CVD share several risk factors and pathophysiological mechanisms, e.g. inflammation and oxidative stress, that could explain why patients with CVD are prone to develop cancer. Recent reports also suggest a link between hypertension, hypertrophy and lung cancer. Angiotensin II (ANGII) is a hormone involved in blood pressure regulation, vascular hemostasis and hypertrophy development. Moreover, ANGII is cancerogenic by increasing proliferation of several cancer cell types. Hence, ANGII could be a link in CVD-induced enhancement of cancer growth. Hypothesis: We hypothesized that ANGII treatment, and subsequent hypertension and cardiac hypertrophy, could promote cancer growth. Methods: ANGII (2000 ng.kg -1 .min -1 ; 4w) was administered to 10w old C57B6/J mice using osmotic minipumps. After 4w, pumps were removed and 5*10 5 Lewis lung carcinoma (LLC) cells injected onto the right flank. To study the direct impact of the ANGII peptide on the cancer growth mice were treated with ANGII as mentioned above and LLC cells were injected after 3w to allow concomitant ANGII treatment and cancer growth. A similar experiment was performed by injecting 2*10 5 MC38 colon cancer cells. Tumor growth was monitored for 21d after which the mice were sacrificed. Results are expressed as mean ± SEM. Results: ANGII treatment concomitantly with cancer growth resulted in significantly larger tumors as shown by post-mortem tumor volume (881 ± 111 control vs. 1467 ± 154 mm^3 ANGII, P=0.013). In contrast, no increased tumor growth was observed when ANGII was administered non-concomitant with cancer growth. Interestingly, MC38 tumor growth significantly decreased in the ANGII treated group as portrayed by tumor volume (462 ± 65 control vs. 227 ± 81 mm^3 ANGII). Conclusion: Our data suggest a role for ANGII in CVD-enhanced cancer growth, but also that this effect is not universal, illustrated by a differential effect of ANGII on distinct cancer types. Although further investigation is required to unravel the role of ANGII, screening cancer patients with CVD comorbidities for ANGII involvement might be warranted.

Controlling Pericellular Oxygen Tension in Cell Culture Reveals Distinct Breast Cancer Responses to Low Oxygen Tensions.

In oxygen (O2)-controlled cell culture, an indispensable tool in biological research, it is presumed that the incubator setpoint equals the O2 tension experienced by cells (i.e., pericellular O2). However, it is discovered that physioxic (5% O2) and hypoxic (1% O2) setpoints regularly induce anoxic (0% O2) pericellular tensions in both adherent and suspension cell cultures. Electron transport chain inhibition ablates this effect, indicating that cellular O2 consumption is the driving factor. RNA-seq analysis revealed that primary human hepatocytes cultured in physioxia experience ischemia-reperfusion injury due to cellular O2 consumption. A reaction-diffusion model is developed to predict pericellular O2 tension a priori, demonstrating that the effect of cellular O2 consumption has the greatest impact in smaller volume culture vessels. By controlling pericellular O2 tension in cell culture, it is found that hypoxia vs. anoxia induce distinct breast cancer transcriptomic and translational responses, including modulation of the hypoxia-inducible factor (HIF) pathway and metabolic reprogramming. Collectively, these findings indicate that breast cancer cells respond non-monotonically to low O2, suggesting that anoxic cell culture is not suitable for modeling hypoxia. Furthermore, it is shown that controlling atmospheric O2 tension in cell culture incubators is insufficient to regulate O2 in cell culture, thus introducing the concept of pericellular O2-controlled cell culture.

Inference of chromosome selection parameters and missegregation rate in cancer from DNA-sequencing data

Aneuploidy is frequently observed in cancers and has been linked to poor patient outcome. Analysis of aneuploidy in DNA-sequencing (DNA-seq) data necessitates untangling the effects of the Copy Number Aberration (CNA) occurrence rates and the selection coefficients that act upon the resulting karyotypes. We introduce a parameter inference algorithm that takes advantage of both bulk and single-cell DNA-seq cohorts. The method is based on Approximate Bayesian Computation (ABC) and utilizes CINner, our recently introduced simulation algorithm of chromosomal instability in cancer. We examine three groups of statistics to summarize the data in the ABC routine: (A) Copy Number-based measures, (B) phylogeny tip statistics, and (C) phylogeny balance indices. Using these statistics, our method can recover both the CNA probabilities and selection parameters from ground truth data, and performs well even for data cohorts of relatively small sizes. We find that only statistics in groups A and C are well-suited for identifying CNA probabilities, and only group A carries the signals for estimating selection parameters. Moreover, the low number of CNA events at large scale compared to cell counts in single-cell samples means that statistics in group B cannot be estimated accurately using phylogeny reconstruction algorithms at the chromosome level. As data from both bulk and single-cell DNA-sequencing techniques becomes increasingly available, our inference framework promises to facilitate the analysis of distinct cancer types, differentiation between selection and neutral drift, and prediction of cancer clonal dynamics.

Deep fine-KNN classification of ovarian cancer subtypes using efficientNet-B0 extracted features: a comprehensive analysis

This study presents a robust approach for the classification of ovarian cancer subtypes through the integration of deep learning and k-nearest neighbor (KNN) methods. The proposed model leverages the powerful feature extraction capabilities of EfficientNet-B0, utilizing its deep features for subsequent fine-grained classification using the fine-KNN approach. The UBC-OCEAN dataset, encompassing histopathological images of five distinct ovarian cancer subtypes, namely, high-grade serous carcinoma (HGSC), clear-cell ovarian carcinoma (CC), endometrioid carcinoma (EC), low-grade serous carcinoma (LGSC), and mucinous carcinoma (MC), served as the foundation for our investigation. With a dataset comprising 725 images, divided into 80% for training and 20% for testing, our model exhibits exceptional performance. Both the validation and testing phases achieved 100% accuracy, underscoring the efficacy of the proposed methodology. In addition, the area under the curve (AUC), a key metric for evaluating the model’s discriminative ability, demonstrated high performance across various subtypes, with AUC values of 0.94, 0.78, 0.69, 0.92, and 0.94 for MC. Furthermore, the positive likelihood ratios (LR+) were indicative of the model’s diagnostic utility, with notable values for each subtype: CC (27.294), EC (9.441), HGSC (12.588), LGSC (17.942), and MC (17.942). These findings demonstrate the effectiveness of the model in distinguishing between ovarian cancer subtypes, positioning it as a promising tool for diagnostic applications. The demonstrated accuracy, AUC values, and LR+ values underscore the potential of the model as a valuable diagnostic tool, contributing to the advancement of precision medicine in the field of ovarian cancer research.

Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms.

Tumorigenesis arises from the dysfunction of cancer genes, leading to uncontrolled cell proliferation through various mechanisms. Establishing a complete cancer gene catalogue will make precision oncology possible. Although existing methods based on graph neural networks (GNN) are effective in identifying cancer genes, they fall short in effectively integrating data from multiple views and interpreting predictive outcomes. To address these shortcomings, an interpretable representation learning framework IMVRL-GCN is proposed to capture both shared and specific representations from multiview data, offering significant insights into the identification of cancer genes. Experimental results demonstrate that IMVRL-GCN outperforms state-of-the-art cancer gene identification methods and several baselines. Furthermore, IMVRL-GCN is employed to identify a total of 74 high-confidence novel cancer genes, and multiview data analysis highlights the pivotal roles of shared, mutation-specific, and structure-specific representations in discriminating distinctive cancer genes. Exploration of the mechanisms behind their discriminative capabilities suggests that shared representations are strongly associated with gene functions, while mutation-specific and structure-specific representations are linked to mutagenic propensity and functional synergy, respectively. Finally, our in-depth analyses of these candidates suggest potential insights for individualized treatments: afatinib could counteract many mutation-driven risks, and targeting interactions with cancer gene SRC is a reasonable strategy to mitigate interaction-induced risks for NR3C1, RXRA, HNF4A, and SP1.