Cancer Subtypes Research Articles

Genetic selection for lung adenocarcinoma: a statistical approach based on pathological genetic data

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC), with high morbidity and mortality worldwide, posing a serious threat to human health. Due to the complex molecular mechanism of lung adenocarcinoma, which involves a variety of genetic and environmental factors, this makes early diagnosis and treatment a major challenge. This research aims to identify the key pathogenic genes of lung adenocarcinoma to solve the challenges in early diagnosis and treatment. In this paper, three differential analysis methods, limma, DESeq2 and edgeR, were used to select 3315 differential genes. Then, combined with Lasso regression and XGboost algorithm, the pathological gene data were analyzed in depth, and through these advanced statistical and machine learning techniques, the genes related to lung adenocarcinoma were screened from a large number of gene expression data, and 17 characteristic genes of the highest importance were found GO enrichment analysis and KEGG enrichment analysis were performed on these characteristic genes to clarify the biological functions of the genes. Finally, 8 significant pathogenic genes were identified by Cox survival analysis. The expression levels of these genes are closely related to the prognosis of lung adenocarcinoma patients, providing a new perspective for understanding the disease mechanism. The conclusions of this study not only improve the understanding of the molecular mechanism of lung adenocarcinoma, but also provide potential biomarkers for clinical diagnosis and treatment. The discovery of these genes is expected to promote the development of early diagnosis technology and guide the formulation of personalized treatment plans, thereby improving the treatment efficacy and quality of life of lung adenocarcinoma patients. In the future, these genes may become key targets for the development of new drugs and therapeutic strategies.

Automatic lung cancer subtyping using rapid on-site evaluation slides and serum biological markers

BackgroundRapid on-site evaluation (ROSE) plays an important role during transbronchial sampling, providing an intraoperative cytopathologic evaluation. However, the shortage of cytopathologists limits its wide application. This study aims to develop a deep learning model to automatically analyze ROSE cytological images.MethodsThe hierarchical multi-label lung cancer subtyping (HMLCS) model that combines whole slide images of ROSE slides and serum biological markers was proposed to discriminate between benign and malignant lesions and recognize different subtypes of lung cancer. A dataset of 811 ROSE slides and paired serum biological markers was retrospectively collected between July 2019 and November 2020, and randomly divided to train, validate, and test the HMLCS model. The area under the curve (AUC) and accuracy were calculated to assess the performance of the model, and Cohen’s kappa (κ) was calculated to measure the agreement between the model and the annotation. The HMLCS model was also compared with professional staff.ResultsThe HMLCS model achieved AUC values of 0.9540 (95% confidence interval [CI]: 0.9257–0.9823) in malignant/benign classification, 0.9126 (95% CI: 0.8756–0.9365) in malignancy subtyping (non-small cell lung cancer [NSCLC], small cell lung cancer [SCLC], or other malignancies), and 0.9297 (95% CI: 0.9026–0.9603) in NSCLC subtyping (lung adenocarcinoma [LUAD], lung squamous cell carcinoma [LUSC], or NSCLC not otherwise specified [NSCLC-NOS]), respectively. In total, the model achieved an AUC of 0.8721 (95% CI: 0.7714–0.9258) and an accuracy of 0.7184 in the six-class classification task (benign, LUAD, LUSC, NSCLC-NOS, SCLC, or other malignancies). In addition, the model demonstrated a κ value of 0.6183 with the annotation, which was comparable to cytopathologists and superior to trained bronchoscopists and technicians.ConclusionThe HMLCS model showed promising performance in the multiclassification of lung lesions or intrathoracic lymphadenopathy, with potential application to provide real-time feedback regarding preliminary diagnoses of specimens during transbronchial sampling procedures.Clinical trial numberNot applicable.

Exploring lipidomic profiles and their correlation with hormone receptor and HER2 status in breast cancer.

Dysregulated lipid metabolism promotes the progression of various cancer types, including breast cancer. The present study aimed to explore the lipidomic profiles of patients with breast cancer, providing insights into the correlation between lipid compositions and tumor subtypes characterized by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status. Briefly, 30 patients with breast cancer were categorized into four groups based on their HR and HER2 status: HR+ no HER2 expression (HER2-0), HR+ HER2-low; HR+ HER2-positive (pos) and HR- HER2-pos. The lipidomic profiles of these patients were analyzed using high-throughput liquid chromatography-mass spectrometry. The data were processed through principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and random forest (RF) classification to assess the lipidomic variations and significant lipid features among these groups. The profiles of the lipids, particularly triglycerides (TG) such as TG(16:0-18:1-18:1)+NH4, were significantly different across the groups. PCA and PLS-DA identified unique lipid profiles in the HR+ HER2-pos and HR+ HER2-0 groups, while RF highlighted phosphatidylinositol-3,4,5-trisphosphate(21:2)+NH4 as a crucial lipid feature for accurate patient grouping. Advanced statistical analysis showed significant correlations between lipid carbon chain length and the number of double bonds within the classifications, providing insights into the role of structural lipid properties in tumor biology. Additionally, a clustering heatmap and network analysis indicated significant lipid-lipid interactions. Pathway enrichment analysis showed critical biological pathways, such as the 'Assembly of active LPL and LIPC lipase complexes', which has high enrichment ratio and statistical significance. In conclusion, the present study underscored that lipidomic profiling is crucial in understanding the metabolic alterations associated with different breast cancer subtypes. These findings highlighted specific lipid features and interactions that may serve as potential biomarkers for breast cancer classification and target pathways for therapeutic intervention. Furthermore, advanced lipidomic analyses can be integrated to decipher complex biological data, offering a foundation for further research into the role of lipid metabolism in cancer progression.

Myocardial infarction risk across cancer subtypes - nationwide registry study

Abstract Background Progress in cancer treatments have significantly improved patient outcomes, leading to increased survival rates. However, patients with cancer continue to have higher rates of cardiovascular events an area that has traditionally not received much attention. Purpose The aim of this study was to examine the risk of myocardial infarction in patients with different types of cancer compared with age- and sex-matched population controls with benign skin cancer. Methods Patients with cancer between 2000-2016 were identified within the Danish National Patient Registry. Cox regression was used accounting for the competing risk of death reporting hazard ratios (HR) for both myocardial infarction and mortality. Models were adjusted for age, sex, hypercholesterolemia, atrial fibrillation, hypertension and anticoagulant therapies of all included subjects. Patients with a previous myocardial infarction were excluded from the analysis. Results A total of 503.616 patients were included in the final analysis of whom 355.920 had cancer with different cancer types and 147.696 were controls with benign skin cancer. The one-year outcome of myocardial infarction (Figure 1) was overall significant higher for all subtypes of cancer especially lymphoma HR 1.66 (interquartile range [IQR]: 1.45-1.89), bladder cancer HR 1.64 (IQR: 1.39-1.94), and gastrointestinal cancer HR 1.53 (IQR: 1.40-1.69). Hepatobiliary and pancreatic cancer, breast cancer and malignant melanoma had reduced but not significantly different HRs of 0.96 (IQR: 0.80-1.15), 0.92 (IQR: 0.79-1.07), and 0.76 (IQR: 0.60-0.96), respectively. The one-year competing risk of death (Figure 2) was overall higher for all subtybes of cancer especially hepatobiliary and pancreatic cancer and lung cancer with HRs of 52.11 (IQR: 50.18-54.11) and 41.62 (IQR: 40.17-43.12). Conclusion The one-year risk of myocardial infarction varied across cancer subtypes. Notably, lymphoma, bladder, and gastrointestinal cancers were associated with higher risks, whereas hepatobiliary and pancreatic, breast, and malignant melanoma cancer showed no statistically significant associations. The clinical significance of these differences must be viewed within the context of the significant competing risk of death in this cancer population.Risk Of Myocardial InfarctionCompeting Risk Of Death

Obesity-dependent selection of driver mutations in cancer

Obesity is a risk factor for cancer, but whether obesity is linked to specific genomic subtypes of cancer is unknown. We examined the relationship between obesity and tumor genotype in two clinicogenomic corpora. Obesity was associated with specific driver mutations in lung adenocarcinoma, endometrial carcinoma and cancers of unknown primaries, independent of clinical covariates, demographic factors and genetic ancestry. Obesity is therefore a driver of etiological heterogeneity in some cancers.

GOG-MBSHO: multi-strategy fusion binary sea-horse optimizer with Gaussian transfer function for feature selection of cancer gene expression data

Cancer gene expression data has the characteristics of high-dimensional, multi-text and multi-classification. The problem of cancer subtype diagnosis can be solved by selecting the most representative and predictive genes from a large number of gene expression data. Feature selection technology can effectively reduce the dimension of data, which helps analyze the information on cancer gene expression data. A multi-strategy fusion binary sea-horse optimizer based on Gaussian transfer function (GOG-MBSHO) is proposed to solve the feature selection problem of cancer gene expression data. Firstly, the multi-strategy includes golden sine strategy, hippo escape strategy and multiple inertia weight strategies. The sea-horse optimizer with the golden sine strategy does not disrupt the structure of the original algorithm. Embedding the golden sine strategy within the spiral motion of the sea-horse optimizer enhances the movement of the algorithm and improves its global exploration and local exploitation capabilities. The hippo escape strategy is introduced for random selection, which avoids the algorithm from falling into local optima, increases the search diversity, and improves the optimization accuracy of the algorithm. The advantage of multiple inertial weight strategies is that dynamic exploitation and exploration can be carried out to accelerate the convergence speed and improve the performance of the algorithm. Then, the effectiveness of multi-strategy fusion was demonstrated by 15 UCI datasets. The simulation results show that the proposed Gaussian transfer function is better than the commonly used S-type and V-type transfer functions, which can improve the classification accuracy, effectively reduce the number of features, and obtain better fitness value. Finally, comparing with other binary swarm intelligent optimization algorithms on 15 cancer gene expression datasets, it is proved that the proposed GOG1-MBSHO has great advantages in the feature selection of cancer gene expression data.

Leveraging ML for profiling lipidomic alterations in breast cancer tissues: a methodological perspective

In this study, a comprehensive methodology combining machine learning and statistical analysis was employed to investigate alterations in the metabolite profiles, including lipids, of breast cancer tissues and their subtypes. By integrating biological and machine learning feature selection techniques, along with univariate and multivariate analyses, a notable lipid signature was identified in breast cancer tissues. The results revealed elevated levels of saturated and monounsaturated phospholipids in breast cancer tissues, consistent with external validation findings. Additionally, lipidomics analysis in both the original and validation datasets indicated lower levels of most triacylglycerols compared to non-cancerous tissues, suggesting potential alterations in lipid storage and metabolism within cancer cells. Analysis of cancer subtypes revealed that levels of PC 30:0 were relatively reduced in HER2(−) samples that were ER(+) and PR(+) compared to those that were ER(−) and PR(−). Conversely, HER2(+) tumors, which were ER(−) and PR(−), exhibited increased concentrations of PC 30:0. This increase could potentially be linked to the role of Stearoyl-CoA-Desaturase 1 in breast cancer. Comprehensive metabolomic analyses of breast cancer can offer crucial insights into cancer development, aiding in early detection and treatment evaluation of this devastating disease.

A proteome-wide association study identifies putative causal proteins for breast cancer risk.

Genome-wide association studies (GWAS) have identified more than 200 breast cancer risk-associated genetic loci, yet the causal genes and biological mechanisms for most loci remain elusive. Proteins, as final gene products, are pivotal in cellular function. In this study, we conducted a proteome-wide association study (PWAS) to identify proteins in breast tissue related to breast cancer risk. We profiled the proteome in fresh frozen breast tissue samples from 120 cancer-free European-ancestry women from the Susan G. Komen Tissue Bank (KTB). Protein expression levels were log2-transformed then normalized via quantile and inverse-rank transformations. GWAS data were also generated for these 120 samples. These data were used to build statistical models to predict protein expression levels via cis-genetic variants using the elastic net method. The prediction models were then applied to the GWAS summary statistics data of 133,384 breast cancer cases and 113,789 controls to assess the associations of genetically predicted protein expression levels with breast cancer risk overall and its subtypes using the S-PrediXcan method. A total of 6388 proteins were detected in the normal breast tissue samples from 120 women with a high detection false discovery rate (FDR) p value < 0.01. Among the 5820 proteins detected in more than 80% of participants, prediction models were successfully built for 2060 proteins with R > 0.1 and P < 0.05. Among these 2060 proteins, five proteins were significantly associated with overall breast cancer risk at an FDR p value < 0.1. Among these five proteins, the corresponding genes for proteins COPG1, DCTN3, and DDX6 were located at least 1 Megabase away from the GWAS-identified breast cancer risk variants. COPG1 was associated with an increased risk of breast cancer with a p value of 8.54 × 10-4. Both DCTN3 and DDX6 were associated with a decreased risk of breast cancer with p values of 1.01 × 10-3 and 3.25 × 10-4, respectively. The corresponding genes for the remaining two proteins, LSP1 and DNAJA3, were located in previously GWAS-identified breast cancer risk loci. After adjusting for GWAS-identified risk variants, the association for DNAJA3 was still significant (p value of 9.15 × 10-5 and adjusted p value of 1.94 × 10-4). However, the significance for LSP1 became weaker with a p value of 0.62. Stratification analyses by breast cancer subtypes identified three proteins, SMARCC1, LSP1, and NCKAP1L, associated with luminal A, luminal B, and ER-positive breast cancer. NCKAP1L was located at least 1Mb away from the GWAS-identified breast cancer risk variants. After adjusting for GWAS-identified breast cancer risk variants, the association for protein LSP1 was still significant (adjusted p value of 6.43 × 10-3 for luminal B subtype). We conducted the first breast-tissue-based PWAS and identified seven proteins associated with breast cancer, including five proteins not previously implicated. These findings help improve our understanding of the underlying genetic mechanism of breast cancer development.

Understanding the chemistry & pharmacology of antibody-drug conjugates in triple-negative breast cancer with special reference to exatecan derivatives.

In the spectrum of breast malignancies, triple-negative breast cancer is the most widely spreading subtype of breast cancer due to a low availability of therapeutic remedies. Recently, antibody-drug conjugates dramatically resolved the landscape for the treatment of triple-negative breast cancer. This review mainly focuses on the chemistry, structure, mechanism of action, and role of antibody-drug conjugates in triple-negative breast cancer. Datopotecan Deruxtecan (Dato-DXd) is a new-generation ADC showing encouraging results for TNBC. In this review, we have also emphasized TROP-2-directed Datopotamab deruxtecan ADCs to treat triple-negative breast cancer, its synthesis, mechanism of action, pharmacokinetics, pharmacodynamics, adverse events, and their ongoing clinical trials.

PGRMC1 and PAQR4 are promising molecular targets for a rare subtype of ovarian cancer.

The heterogeneity of ovarian cancer (OC) has made developing effective treatments difficult. Nowadays, hormone therapy plays a growing role in the treatment of OC; however, hormone modulators have had only limited success so far. To provide a more rigorous foundation for hormonal therapy for different OC subtypes, the current study used a series of bioinformatics approaches to analyse the expression profiles of genes encoding membrane progesterone (PGRMC1, progestins and the adipoQ receptor [PAQR] family), and androgen (zinc transporter member 9 [ZIP9], OXER1) receptors. Our work investigated also their prognostic value in the context of OC. We found differences in expression of ZIP9 and OXER1 between different OC subtypes, as well as between patient tumour and normal tissues. Expression of mRNA encoding PAQR7 and PAQR8 in a panel of OC cell lines was below the qPCR detection limit and was downregulated in tumour tissue samples, whereas high expression of PGRMC1 and PAQR4 mRNA was observed in rare subtypes of OC cell lines. In addition, chemical inhibition of PGRMC1 reduced the viability of rare OCs represented by COV434 cells. In conclusion, PGRMC1 and PAQR4 are promising targets for anticancer therapy, particularly for rare subtypes of OC. These findings may reflect differences in the observed responses of various OC subtypes to hormone therapy.

MicroRNA-205-5p inhibits the growth and migration of breast cancer through targeting Wnt/β-catenin co-receptor LRP6 and interacting with lncRNAs.

Breast cancer is the most prevalent type of cancer among women worldwide. Non-coding RNAs play a fundamental role in regulating the expression of different genes. MicroRNAs (miRNAs) are known to bind to mRNA and either induce its degradation or repress its translation. Also, miRNA can modulate the expression of long non-coding RNAs (lncRNA) through different mechanisms. This study aims to determine the role of miRNA-205-5p in breast cancer cell lines. miR-205-5p was bioinformatically predicted to interact with LRP6 mRNA and lncRNAs MALAT1, NEAT1, SNHG5, and SNHG16. Then, the levels of miR-205-5p and its target genes and lncRNAs in breast cancer cell lines MCF-7 and MDA-MB-231 were determined. In addition, MCF-7 and MDA-MB-231 breast cancer cells were transfected with miR-205-5p mimic or miRNA mimic negative control using lipofectamine 3000, and the effect of miR-205-5p overexpression on cellular proliferation and migration was assessed. Moreover, we probed the impact of miR-205-5poverexpression on the expression levels of LRP6, Wnt/β-catenin pathway genes, lncRNAs, and apoptotic markers. miR-205-5p upregulation resulted in decreasing the growth and migration and induced apoptosis markers in the two tested breast cancer subtypes. Additionally, miR-205-5p overexpression resulted in decreasing the expression of LRP6 in MCF-7 and MDA-MB-231 cells leading to downregulation of Wnt/β-catenin target genes, c-Myc, cyclin D1, and PPARδ and had various regulatory effects on the expression of lncRNAs MALAT1, NEAT1, SNHG5, and SNHG16. miR-205-5p inhibits the proliferation and migration of breast cancer through diverse mechanisms including targeting LRP6, Wnt/β-catenin pathway, and its regulatory effects on lncRNAs.

Profiling Breast Tumor Heterogeneity and Identifying Breast Cancer Subtypes Through Tumor-Associated Immune Cell Signatures and Immuno Nano Sensors.

Breast cancer is a complex and heterogeneous disease with varying cellular, genetic, epigenetic, and molecular expressions.The detection of intratumor heterogeneity in breast cancer poses significant challenges due to its complex multifaceted characteristics, yet its identification is crucial for guiding effective treatment decisions and understanding the diseaseprogression. Currently, there exists no method capable of capturing the full extent of breast tumor heterogeneity. In this study, the aim is to identify and characterize metabolic heterogeneity in breast tumors using immune cells and an ultrafast laser-fabricated Immuno Nano Sensor.Combining spectral markers from both Natural Killer (NK)and T cells, a machine-learning approach is implemented to distinguish cancer from healthy samples, identify primary versus metastatic tumors, and determine estrogen receptor (ER)/progesterone receptor (PR) status at the single-cell level. The platform successfully distinguished heterogeneous breast cancer samples fromhealthy individuals, achieving97.8%sensitivity and92.2%specificity, andaccurately identifiedprimary tumors from metastatic tumors. Characteristic spectral signatures allow for discrimination between ER/PR-positive andnegative tumors with 97.5% sensitivity.This study demonstrates the potential of immune cell-based metabolic profiling in providing a comprehensive assessment of breast tumor heterogeneity and paving the way for minimally invasive liquid biopsy approaches in breast cancer diagnosis and management.

Impact of Malnutrition on the Length of Stay for Hospitalized Chimeric Antigen Receptor T-cell (CAR-T) Therapy Patients in the United States (2020).

Background Chimeric antigen receptor T-cell (CAR-T) therapy offers a promising treatment for certain malignancies but can be associated with complications. Malnutrition and cachexia are common in cancer patients and may worsen outcomes. This study investigated the impact of malnutrition on the length of hospital stay (LOS) in patients with hematologic malignancies undergoing CAR-T therapy. The analysis focused on different subpopulations, including those with acute lymphoblastic leukemia (ALL), multiple myeloma (MM), diffuse large B-cell lymphoma (DLBCL), and non-Hodgkin lymphoma (NHL) excluding DLBCL. Methods Utilizing the 2020 National Inpatient Sample (NIS) data, we performed survey-based mean estimation analyses for LOS across various subpopulations of CAR-T therapy patients. These subpopulations were defined by specific diagnoses: ALL, myeloma, DLBCL, and NHL excluding DLBCL. We compared the LOS between patients with and without malnutrition using STATA accounting for the complex survey design. Cachexia was included as disease-induced malnutrition. Results The total CAR-T population used for analyses included 439 patients, and malnutrition was present in 50 (11.39%). The overall CAR-T population demonstrated a significantly longer LOS for patients with malnutrition (30.92 days, 95% CI: 24.30 to 37.54) compared to those without malnutrition (17.97 days, 95% CI: 15.48 to 20.46, p = 0.0002). This trend held true across subgroups. Specifically, the ALL population had a significantly longer LOS with malnutrition (45.25 days, 95% CI: 35.46 to 55.04) compared to non-malnourished patients (27.58 days, 95% CI: 16.74 to 38.42, p = 0.0279). For the DLBCL population, the mean LOS was 24.47 days (95% CI: 19.22 to 29.71) with malnutrition and 17.17 days (95% CI: 13.29 to 21.04, p = 0.0161) without malnutrition. The NHL population excluding DLBCL exhibited a mean LOS of 33.86 days (95% CI: 22.66 to 45.07) for malnourished patients and 17.44 days (95% CI: 14.76 to 20.11, p = 0.0055) for non-malnourished patients. The myeloma population showed a similar trend although not statistically significant, with a mean LOS of 39.00 days (95% CI: -3.54 to 81.54) for malnourished patients and 18.03 days (95% CI: 15.02 to 21.03, p = 0.3337) for non-malnourished patients. These findings highlight significant variations in LOS across different CAR-T-treated cancer subtypes, emphasizing the impact of malnutrition on healthcare resource utilization in oncology. Conclusion Malnutrition is associated with a significantly longer hospital stay among patients undergoing CAR-T therapy. This trend is consistent across various subpopulations, including those with ALL, DLBCL, and NHL (excluding DLBCL). While the impact of malnutrition on LOS was not statistically significant in the myeloma population, this could potentially be attributed to the smaller sample size in this group. Overall, these findings underscore the critical role of nutritional status in managing patients undergoing CAR-T therapy. Future studies should investigate the most effective methods for identifying and treating malnutrition in this patient population to reduce hospital stays and optimize overall patient care.

A framework for target discovery in rare cancers.

While large-scale functional genetic screens have uncovered numerous cancer dependencies, rare cancers are poorly represented in such efforts and the landscape of dependencies in many rare cancers remains obscure. We performed genome-scale CRISPR knockout screens in an exemplar rare cancer, TFE3- translocation renal cell carcinoma (tRCC), revealing previously unknown tRCC-selective dependencies in pathways related to mitochondrial biogenesis, oxidative metabolism, and kidney lineage specification. To generalize to other rare cancers in which experimental models may not be readily available, we employed machine learning to infer gene dependencies in a tumor or cell line based on its transcriptional profile. By applying dependency prediction to alveolar soft part sarcoma (ASPS), a distinct rare cancer also driven by TFE3 translocations, we discovered and validated that MCL1 represents a dependency in ASPS but not tRCC. Finally, we applied our model to predict gene dependencies in tumors from the TCGA (11,373 tumors; 28 lineages) and multiple additional rare cancers (958 tumors across 16 types, including 13 distinct subtypes of kidney cancer), nominating potentially actionable vulnerabilities in several poorly-characterized cancer types. Our results couple unbiased functional genetic screening with a predictive model to establish a landscape of candidate vulnerabilities across cancers, including several rare cancers currently lacking in potential targets.

Obesity and lack of breastfeeding: a perfect storm to augment risk of breast cancer?

Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer with higher rates of recurrence and distant metastasis, as well as decreased 5-year survival rates. Racial disparities are evident in the incidence and mortality rates of triple negative breast cancer particularly increased in young African American women. Concurrently, young African American women have multiple risk factors for TNBC including higher rates of premenopausal abdominal obesity (higher waist-hip ratio) and lower rates of breastfeeding with higher parity, implicating these factors as potentially contributors to poor outcomes. By understanding the mechanisms of how premenopausal obesity and lack of breastfeeding may be associated with increased risk of triple negative breast cancer, we can determine the best strategies for intervention and awareness to improve outcomes in TNBC.

Serous Ovarian Carcinoma: Detailed Analysis of Clinico-Pathological Characteristics as Prognostic Factors.

Serous ovarian carcinoma (SOC) is the most common subtype of epithelial ovarian cancer, with high-grade (HGSOC) and low-grade (LGSOC) subtypes presenting distinct clinical behaviours. This study aimed to evaluate histopathologic features in SOC, correlating these with prognostic outcomes, and explore the potential clinical implications. We analysed 51 SOC cases for lymphovascular space invasion (LVSI), tumour border configuration (TBC), microvessel density (MVD), tumour budding (TB), the tumour-stroma ratio (TSR), the stromal type, tumour-infiltrating lymphocytes (TILs), and tertiary lymphoid structures (TLSs). A validation cohort of 54 SOC cases from The Cancer Genome Atlas (TCGA) was used for comparison. In the discovery set, significant predictors of aggressive behaviour included LVSI, high MVD, high TB, and low TILs. These findings were validated in the validation set where the absence of TLSs, lower peritumoural TILs, immature stromal type, and low TSR were associated with worse survival outcomes. The stromal type was identified as an independent prognostic predictor in SOC across both datasets. Inter-observer variability analysis demonstrated substantial to almost perfect agreement for these features, ensuring the reproducibility of the findings. The histopathological evaluation of immune and stromal features, such as TILs, TLSs, TB, TSR, and stromal type, provides critical prognostic information for SOC. Incorporating these markers into routine pathological assessments could enhance risk stratification and guide treatment, offering practical utility, particularly in low-resource settings when molecular testing is not feasible.

A Rare Case of Triple-Negative Breast Cancer with RAD51D Gene Mutation

Background: The heterogeneity of breast cancer (BC) subtypes poses a significant challenge, with carcinogenesis involving multiple stages and genes, including proto-oncogenes, tumor suppressor genes, and DNA repair genes. Next-generation sequencing has expanded access to multigene panels, such as RAD51 paralogs, which increase the risk of ovarian cancer and possibly triple-negative (TN) BC. Case presentation: We present a rare case of a 45-year-old woman with TNBC and a RAD51D gene mutation. Mammography and breast ultrasonography revealed an irregular, 30 mm hypoechoic area and dystrophic calcifications in the right breast. Immunohistochemistry showed a lack of expression of ER, RP, HER-2, and P53, with 50% of neoplastic cell nuclei positive for Ki-67. Next-generation sequencing revealed a mutation in RAD51D and MUYTH genes. The patient underwent partial mastectomy, chemotherapy, and prophylactic mastectomy. Conclusion: Genetic analysis is crucial for identifying specific mutations contributing to TNBC development. Current preventive interventions primarily address BRCA1 and BRCA2 mutations, following established guidelines.

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks.

The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing deep neural networks and incorporating the SHapley Additive exPlanations algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high area under the curve (AUC) scores-0.98±0.02 for lung cancer subtype differentiation and 0.83±0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient and interpretable approach that contributes to a deeper understanding of disease mechanisms.

EFFICACY OF NEOADJUVANT TARGETED THERAPY IN TREATMENT OF PATIENTS WITH LOCALISED CLEAR-CELL RENAL CELL CARCINOMA

Clear-cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, accounting for approximately 75-80% of all renal malignancies. Objectives: The main objective of the study is to find the efficacy of neoadjuvant targeted therapy in the treatment of patients with localised clear-cell renal cell carcinoma. Methods: This prospective study was conducted at Mardan Medical Complex from June 2023 to January 2024. Data were collected from 85 patients. Patients aged&gt;18 years and have a histologically confirmed diagnosis of localized ccRCC were included in the study. Tumor size was required to be 4 cm or larger, but confined to the kidney, corresponding to Stage I to III disease. Results: Data were collected from 85 patients with an average baseline tumour size for the entire cohort was 7.5 cm, and after 12 weeks of treatment, tumours shrank to an average of 5.6 cm, resulting in a mean tumour reduction of 25%. In the sunitinib group, the tumour size decreased from 7.5 cm to 5.55 cm, reflecting a 26% reduction. Similarly, in the pazopanib group, the tumour size was reduced from 7.5 cm to 5.65 cm, with an average reduction of 24%. Conclusion: It is concluded that neoadjuvant targeted therapy, using agents such as sunitinib and pazopanib, effectively reduces tumour size and improves surgical outcomes in patients with localized clear-cell renal cell carcinoma.

Dual Immune Checkpoint Inhibition in Patients With Aggressive Thyroid Carcinoma

Aggressive thyroid carcinoma, including radioiodine refractory (RAIR) differentiated thyroid carcinoma (DTC), medullary thyroid carcinoma (MTC), and anaplastic thyroid carcinoma (ATC), are associated with significant morbidity and mortality and have limited therapeutic options. Distinct immune profiles have been identified in thyroid cancer subtypes suggesting they may be susceptible to immune checkpoint inhibition. To evaluate the efficacy of anti-programmed cell death 1 nivolumab and anti-cytotoxic lymphocyte-associated protein 4 ipilimumab in patients with aggressive thyroid carcinoma. This phase 2 nonrandomized clinical trial enrolled patients with RAIR DTC in a single center from October 2017 to May 2019, with exploratory cohorts in MTC and ATC. The data were analyzed between June 2021 and September 2023. Intravenous nivolumab, 3 mg/kg, every 2 weeks and ipilimumab, 1 mg/kg, every 6 weeks until disease progression, intolerable adverse events, or a maximum duration of 2 years. The primary end point of the study was objective response rate (ORR) in RAIR DTC, which was scored according to RECIST (Response Evaluation Criteria in Solid Tumours), version 1.1. Key secondary end points included safety, progression-free survival, overall survival, and biomarker analyses. A total of 51 patients were registered, and 49 patients were evaluable for analysis. The median (range) age was 65 years (30-88 years), and 25 participants (51%) were female. ORR in the DTC cohort was 9.4% (3/32 [95% CI, 2.8%-28.5%]), with all partial responses in either oncocytic carcinoma (2/6 [33.0%]) or poorly differentiated thyroid carcinoma (1/5 [20.0%]). Clinical benefit rates were 62.5% (20/32) in the overall DTC cohort, including 83.3% (5/6) in oncocytic carcinoma and 40% (2/5) in poorly differentiated thyroid carcinoma. ORR in the exploratory ATC cohort was 30.0% (3/10 [95% CI, 6.7%-65.2%]), with a clinical benefit rates of 50.0% (5/10). No responses were observed in the exploratory MTC cohort. The safety profile was similar to prior reports with dual immune checkpoint inhibition (pruritus, rash, diarrhea, fatigue, and elevation of lipase and liver enzymes). The presence of NRAS tumor genetic sequence variations, but not BRAF V600E, was associated with worse outcomes. This phase 2 nonrandomized clinical trial reported clinical activity of dual immune checkpoint inhibition in aggressive thyroid cancer. The study did not meet its end point in the primary population of RAIR DTC and does not support further investigation in non-biomarker-selected DTC. However, the signal observed in ATC may merit further evaluation. ClinicalTrials.gov Identifier: NCT03246958.