Tumor Subtypes Research Articles

Weakly supervised learning in thymoma histopathology classification: an interpretable approach

IntroductionThymoma classification is challenging due to its diverse morphology. Accurate classification is crucial for diagnosis, but current methods often struggle with complex tumor subtypes. This study presents an AI-assisted diagnostic model that combines weakly supervised learning with a divide-and-conquer multi-instance learning (MIL) approach to improve classification accuracy and interpretability.MethodsWe applied the model to 222 thymoma slides, simplifying the five-class classification into binary and ternary steps. The model features an attention-based mechanism that generates heatmaps, enabling visual interpretation of decisions. These heatmaps align with clinically validated morphological differences between thymoma subtypes. Additionally, we embedded domain-specific pathological knowledge into the interpretability framework.ResultsThe model achieved a classification AUC of 0.9172. The generated heatmaps accurately reflected the morphological distinctions among thymoma subtypes, as confirmed by pathologists. The model's transparency allows pathologists to visually verify AI decisions, enhancing diagnostic reliability.DiscussionThis model offers a significant advancement in thymoma classification, combining high accuracy with interpretability. By integrating weakly supervised learning, MIL, and attention mechanisms, it provides an interpretable AI framework that is applicable in clinical settings. The model reduces the diagnostic burden on pathologists and has the potential to improve patient outcomes by making AI tools more transparent and clinically relevant.

Invasion of the Cavernous Sinus Medial Wall by Pituitary Adenomas: Intraoperative Evaluation and the Clinical Efficacy of the Cavernous Sinus Medial Wall Resection in the Treatment of Somatotroph Adenomas.

Pituitary adenomas are common benign intracranial tumors. This study aimed to explore the relevant factors influencing the cavernous sinus medial wall invasion by Pituitary adenomas based on intraoperative evaluation. Meanwhile, the authors introduced a safe technique for the resection of the medial wall of the cavernous sinus and conducted a preliminary evaluation of the efficacy of this technique. The clinical data of 108 patients with pituitary adenomas who underwent surgery at our institution were collected. Binary logistic regression analysis was utilized to identify independent predictors of medial wall invasion. Furthermore, the authors performed medial wall resection on 12 cases of somatotroph adenomas with medial wall invasion to initially assess the surgical efficacy of this technique for somatotroph adenomas. Multivariate Logistic regression analysis showed that tumor Knosp grade and somatotroph adenomas were independent predictors of medial wall invasion (P<0.05). For somatotroph adenomas, medial wall resection resulted in a high biochemical remission rate (83%), and all patients were free of recurrence at the last follow-up. Higher knosp grade and somatotroph adenomas will increase the risk of medial wall invasion of the cavernous sinus. Relying solely on the Knosp grade system to evaluate tumor invasion of the medial wall without differentiating between tumor subtypes is inaccurate. Invasion of the medial wall may be a cause of low biochemical remission rates and recurrence after surgery for somatotroph adenomas. The technique of medial wall resection is safe and effective, and holds significant importance in the treatment of somatotroph adenomas.

The Impact of Tumor Stage and Histopathology on Survival Outcomes in Esophageal Cancer Patients over the Past Decade

Background: Esophageal cancer (EC) is the sixth leading cause of cancer-related mortality worldwide, continuing to be a significant public health concern. The purpose of this study is to assess the impact of staging and histopathology of EC on associated mortality. The study also aims to further investigate clinical characteristics, prognostic factors, and survival outcomes in patients diagnosed with EC between 2010 and 2017. Furthermore, we analyzed the interaction between tumor histology and staging and the risk of mortality. Methods: A total of 24,011 patients diagnosed with EC between 2010 and 2017 in the United States were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database. Demographic parameters, tumor stage, and histologic subtypes were analyzed and associated overall mortality (OM) and cancer-specific mortality (CSM) were measured across all subgroups. Covariates reaching the level of statistical significance, demonstrable by a p-value equal to or less than 0.01, were incorporated into a multivariate Cox proportional hazards model. A hazard ratio greater than 1 was indicative of an increased risk of mortality in the presence of the variable under discussion. Additionally, the study explores the interaction between histology and tumor stage on outcomes. Results: The majority of patients were male (80.13%) and non-Hispanic white (77.87%), with a predominant age at diagnosis of between 60 and 79 years (59.86%). Adenocarcinoma was the most common tumor subtype (68.17%), and most patients were diagnosed at a distant stage (41.29%). Multivariate analysis revealed higher mortality risks for males, older patients, unmarried individuals, and those with advanced-stage tumors. Higher income, receiving radiation or chemotherapy, and undergoing surgery were associated with lower mortality. Tumor subtype significantly influenced mortality, with squamous cell carcinoma and neuroendocrine tumors showing higher hazard ratios compared to adenocarcinoma. Adenocarcinoma is linked to a poorer prognosis at advanced stages, whereas the opposite trend is observed for SCC. Conclusions: The study identifies significant demographic and clinicopathologic factors influencing mortality in esophageal cancer patients, highlighting the importance of early diagnosis and treatment intervention. Future research should focus on tailored treatment strategies to improve survival outcomes in high-risk groups and to understand the interaction between tumor histology and tumor stage.

Innovative strategies to optimise colorectal cancer immunotherapy through molecular mechanism insights

BackgroundColorectal cancer (CRC) is a leading cause of cancer-related deaths globally. The heterogeneity of the tumor microenvironment significantly influences patient prognosis, while the diversity of tumor cells shapes its unique characteristics. A comprehensive analysis of the molecular profile of tumor cells is crucial for identifying novel molecular targets for drug sensitivity analysis and for uncovering the pathophysiological mechanisms underlying CRC.MethodsWe utilized single-cell RNA sequencing technology to analyze 13 tissue samples from 4 CRC patients, identifying key cell types within the tumor microenvironment. Intercellular communication was assessed using CellChat, and a risk score model was developed based on eight prognostic genes to enhance patient stratification for immunotherapeutic approaches. Additionally, in vitro experiments were performed on DLX2, a gene strongly associated with poor prognosis, to validate its potential role as a therapeutic target in CRC progression.ResultsEight major cell types were identified across the tissue samples. Within the tumor cell population, seven distinct subtypes were recognized, with the C0 FXYD5+ tumor cells subtype being significantly linked to cancer progression and poor prognosis. CellChat analysis indicated extensive communication among tumor cells, fibroblasts, and immune cells, underscoring the complexity of the tumor microenvironment. The risk score model demonstrated high accuracy in predicting 1-, 3-, and 5-year survival rates in CRC patients. Enrichment analysis revealed that the C0 FXYD5+ tumor cell subtype exhibited increased energy metabolism, protein synthesis, and oxidative phosphorylation, contributing to its aggressive behavior. In vitro experiments confirmed DLX2 as a critical gene associated with poor prognosis, suggesting its viability as a target for improving drug sensitivity.ConclusionIn summary, this study advances our understanding of CRC progression by identifying critical tumor subtypes, molecular pathways, and prognostic markers that can inform innovative strategies for predicting and enhancing drug sensitivity. These findings hold promise for optimizing immunotherapeutic approaches and developing new targeted therapies, ultimately aiming to improve patient outcomes in CRC.

Lymphoid enhancer-binding factor 1 (LEF1) immunostaining as a surrogate for β-catenin (CTNNB1) mutations

AimsMutations affecting exon 3 of the β-catenin (CTNNB1) gene result in constitutive activation of WNT signalling and are a diagnostic hallmark of several tumour entities including desmoid-type fibromatosis. They also...

Clinicopathological significance of SOX2 and FOXG1 expression patterns in ovarian immature teratomas

Objective: To investigate the relationship between the expression patterns of SOX2 and FOXG1 and the differentiation/development level of neural components in immature teratoma and to determine the clinical significance and potential application of this correlation in a clinical setting. Methods: We conducted a comprehensive whole transcriptome sequencing analysis to identify differentially expressed genes (DEGs) across various subtypes of ovarian germ cell tumors. Additionally, immunohistochemical staining of paraffin-embedded tissue sections was employed to assess the nuclear staining pattern of SOX2 and FOXG1 proteins within the tumor tissues. Results: The transcriptome sequencing data showed that transcription factors SOX2 and FOXG1 exhibited high levels of expression typically in immature teratoma and occupied a pivotal position within the protein-protein interaction network. Immunohistochemical staining revealed the absence of both SOX2 and FOXG1 protein expression in dysgerminoma and yolk sac tumor samples. In immature teratoma, immunohistochemical staining demonstrated diffuse expression of SOX2 and FOXG1 proteins within the inner layer of densely-arranged primitive neuroepithelial tubules. This pattern of expression suggested the presence of stem cell-like properties within these tumor cells. In the sparsely peripheral neurogliocytes, FOXG1 maintained a diffuse nuclear staining pattern resembling that of neuroepithelial cells, while SOX2 exhibited a scattered pattern of positive staining, hinting at a neural lineage differentiation potential. This spatial differential expression pattern of SOX2 and FOXG1 proteins in immature teratoma suggested that primitive neural components within these tumors often recapitulated the trajectory of neural formation and cortical development that was typically observed during embryogenesis. The primitive neural tube acted as the center that constantly moved from inside to outside, with a dynamic shift from the interior to the exterior, paralleled by the sequential differentiation of cell lineages from primitive neuroepithelial stem cells to radial glia, intermediate progenitor cells, and ultimately to precursor glia. Conclusions: This spatial expression pattern of SOX2 and FOXG1 proteins observed in immature teratoma mirrors the lineage differentiation and migration trajectories of primitive neuroepithelial components typically seen in embryonic neurogenesis and cortical development. In daily practice, the combined application of SOX2 and FOXG1 SOX2 and FOXG1 helps identify the primitive neuroepithelial components in immature teratoma, avoid misjudgment of similar morphologies, and thereby assist in the histological grading and clinical decision-making.

CRISPR-Cas9 screening identified novel subtypes of cutaneous melanoma based on essential cancer genes.

Our primary objective was to identify genes critical for cutaneous melanoma (CM) and related typing, based on essential genes, to generate novel insights for clinical management and immunotherapy of patients with CM. We analyzed RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx), and sequencing data of 29 CM cell line from Cancer Cell Line Encyclopedia (CCLE) databases. Combined with DepMap database, 406 CM essential cancer genes were finally obtained. Based on the expression of essential genes in cancer, the patients included in TCGA and Gene Expression Omnibus (GEO) databases were divided into three different molecular subtypes (C1, C2, and C3) by the NMF algorithm. Data analysis from TCGA and GEO datasets revealed that subtype C3 had the poorest prognosis, while subtype C1 exhibited the best prognosis. Combined with the CIBERSORT, ESTIMATE and ssGSEA algorithm, patients with different molecular subtypes can be divided into two immune subtypes (hot and cold). We found that subtype C1 was characterized by hot tumors, in contrast to subtypes C2 and C3, which were characterized by cold tumors. Then, we used univariate Cox regression, LASSO, and multifactor Cox regression analysis to select risk genes and constructed a prognostic model based on eight genes: RABIF, CDCA8, FOXM1, SPRR2E, AIP, CAP1, CTSW, and IFITM3. All patients were divided into two risk subtypes (high and low ) according to the median of risk scores. We found that most hot tumor subtypes were found in the low-risk subtypes and most patients with this subtype survived for longer. Ultimately, we selected RABIF, which exhibits the highest risk coefficient, for histological and cytological verification. The results showed that RABIF was overexpressed in melanoma. Inhibition of RABIF expression could suppress the proliferation and invasion of melanoma cells and promote the apoptosis of melanoma cells. In conclusion, we used CRISPR-Cas9 screening to verify the association between molecular subtypes (C1, C2, and C3), immune subtypes (hot and cold), and risk subtypes (high and low) in patients with CM, particularly in distinguishing survival and prognosis. These findings can be used to guide clinical management and immunotherapy of patients with CM.

Genomic differences of patients with hematologic malignancies in different age groups

Hematologic malignancies cause significant morbidity/mortality in both children and young adults (CYAs) as well as older adults (OAs). Yet their biological underpinnings remain inadequately understood. Here, we analyzed clinical and genomic disparities between CYAs and OAs in various hematologic malignancies. We found substantial differences in clinical features such as patient sex, ethnicity, metastasis rates, and tumor subtypes. Genomically, most CYA hematologic malignancies indicated lower mutational burden. Subsequently, we identified differentially mutated genes (DMGs) with varying mutation rates between CYAs and OAs, noting fewer mutations in CYAs for most genes such as TP53, TET2, and DNMT3A. In contrast, several DMGs (i.e., NRAS, KRAS, SMARCA4, ID3, PTPN11, WT1, and KIT) were overrepresented in CYAs. We further investigated human protein interacting partners of these identified DMGs that were highly mutated in CYAs/OAs, respectively, and found significant differences in network topological and functional roles. Notably, CYA malignancies demonstrated extensive copy number alterations (CNAs) and more driver gene fusions. In particular, four CNA differential genes (i.e., ARID1B, MYB, TP53, and ESR1) were overrepresented as amplifications and deletions in CYAs and OAs, respectively. Ultimately, we demonstrated a landscape comparative view of clinically actionable genetic events in CYAs and OAs, providing clues for age-related personalized treatment.

DAB2IP loss in luminal a breast cancer leads to NF-κB-associated aggressive oncogenic phenotypes.

Despite proven therapy options for estrogen receptor-positive (ER+) breast tumors, a substantial number of patients with ER+ breast cancer exhibit relapse with associated metastasis. Loss of expression of RasGAPs leads to poor outcomes in several cancers, including breast cancer. Mining the The Cancer Genome Atlas (TCGA) breast cancer RNA-Seq dataset revealed that low expression of the RasGAP DAB2IP was associated with a significant decrease in relapse-free survival in patients with Luminal A breast cancer. Immunostaining demonstrated that DAB2IP loss occurred in grade 2 tumors and higher. Consistent with this, genes upregulated in DAB2IP-low Luminal A tumors were shared with more aggressive tumor subtypes and were associated with proliferation, metastasis, and altered ER signaling. Low DAB2IP expression in ER+ breast cancer cells was associated with increased proliferation, enhanced stemness phenotypes, and activation of IKK, the upstream regulator of the transcription factor NF-κB. Integrating cell-based ChIP-Seq with motif analysis and TCGA RNA-Seq data, we identified a set of candidate NF-κB target genes upregulated with loss of DAB2IP linked with several oncogenic phenotypes, including altered RNA processing. This study provides insight into mechanisms associated with aggressiveness and recurrence within a subset of the typically less aggressive Luminal A breast cancer intrinsic subtype.

Rare giant epithelioid inflammatory myofibroblastic sarcoma of the abdominal cavity in a child: a case report and review of the literature

Epithelioid inflammatory myofibroblastic sarcoma (EIMS) is a distinct subtype of inflammatory myofibroblastoma tumor (IMT) that is recognized as a rare malignant tumor characterized by anaplastic lymphoma kinase (ALK) positivity, significant aggressiveness, treatment challenges, and a poor prognosis. We report on the case of an 8-year-old boy presenting with abdominal pain and vomiting. Computed tomography (CT) of the abdomen revealed a large tumor, and the pathology results following a biopsy confirmed the diagnosis of EIMS. The patient underwent radical tumor resection, and genetic testing identified the presence of the RANBP2–ALK fusion. To our knowledge, this represents the largest pediatric case of abdominal EIMS documented in the literature. Currently, there is no standard therapy for EIMS; however, existing studies advocate for the use of ALK tyrosine kinase inhibitors (TKIs) in its treatment. This case was reported to be in remission following treatment with crizotinib, thereby contributing to the understanding of the specific pathology of EIMS and facilitating accurate diagnosis and targeted therapy.

The T cell-mediated tumor killing patterns revealed tumor heterogeneous and proposed treatment recommendation in ovary cancer

BackgroundImmune checkpoint inhibitors (ICIs) have greatly improved cancer treatment, but the role of genes related to T cell-mediated tumor killing (TTK) sensitivity in ovarian cancer (OV) is unclear.MethodsThis study analyzed 1367 OV patients and 11 independent cohorts. The unsupervised clustering was conducted to identify three tumor subtypes based on genes that regulate tumor cell sensitivity to TTK (GSTTKs). The biological characteristics, genetic variations, immunological landscape, and therapeutic evaluation for each subtype were further explored.ResultsPatients were divided into three reproducible subtypes based on 61 GSTKKs associated with prognosis. C1 was likely to be an invasive subtype with the worst prognosis and highly unstable genome. C2 might be an immune-active subtype with the best prognosis, high immune infiltration and preferable response to immunotherapy. C3 might be a metabolic subtype, resistant to immunotherapy, but sensitive to drug therapy. Following an extensive exploration into a variety of distinct molecular features between the three subtypes, the results suggested that C2 patients were considered to derive significant efficacy from immunotherapy. For C1 and C3 patients, chemotherapy might be an ideal treatment strategy.ConclusionsIn this study, three GSTKKs-based subtypes were identified by assessing TTK patterns in OV. These new insights further improved our understanding of GSTTKs and might refine clinical treatment strategies for OV patients.

Human pancreatic cancer single cell atlas reveals association of CXCL10+ fibroblasts and basal subtype tumor cells.

Pancreatic ductal adenocarcinoma (PDAC) patients with tumors enriched for the basal-like molecular subtype exhibit enhanced resistance to standard of care treatments and have significantly worse overall survival (OS) compared to patients with classical subtype enriched tumors. It is important to develop genomic resources, enabling identification of novel putative targets in a statistically rigorous manner. We compiled a single cell RNA sequencing (scRNAseq) atlas of the human pancreas with 229 patient samples, aggregated from publicly available raw data. We mapped cell-type specific scRNAseq gene signatures in bulk RNAseq (n=744) and spatial transcriptomics (ST) (n=22) and performed validation using multiplex immunostaining. Analysis of tumor cells from our scRNAseq atlas revealed nine distinct populations, two of which aligned with the basal subtype, correlating with worse OS in bulk RNAseq. Deconvolution identified one of the basal populations to be the predominant tumor subtype in non-dissociated ST tissues and in vitro tumor cell and patient-derived organoid lines. We discovered a novel enrichment and spatial association of CXCL10+ cancer associated fibroblasts (CAFs) with basal tumor cells. We identified that besides immune cells, ductal cells also express CXCR3, the receptor for CXCL10, suggesting a relationship between these cell types in PDAC tumor microenvironment. We show that our scRNAseq atlas (700,000 cells), integrated with ST data, has increased statistical power and is a powerful resource, allowing for expansion of current subtyping paradigms in PDAC. We uncovered a novel signaling niche marked by CXCL10+ CAFs and basal tumor cells that could be explored for future targeted therapies.

Comprehensive Analysis of GJB1 in Breast Cancer: Its Implications in Survival and Molecular Mechanisms.

Breast cancer is the leading cause of cancer-related mortality among women worldwide. The connexin (Cx) family, including GJB1 (Cx32), plays complex roles in tumor progression depending on cellular context and cancer subtype. While Cx32 overexpression has been linked to lymph node metastasis, its effects on survival and molecular processes remain unclear. Herein, we aimed to investigate the role of GJB1 in breast cancer by examining its impact on survival and cellular processes in addition to its expression pattern in tumor subtypes, using public datasets. We conducted a comprehensive analysis of GJB1 in breast cancer using METABRIC patient dataset, Cancer Cell Line Encylopedia, and other publicly available databases. We examined the association between GJB1 expression and patient survival, performed differential gene expression analysis, and explored gene set enrichment to identify biological processes associated with high GJB1 expression. GJB1 was significantly down-regulated in breast cancer tissues compared to normal tissues. However, patients with high GJB1 expression had significantly poorer survival compared to those with low expression, with the median survival reduced by over 25 months. Gene ontology (GO) analysis revealed that down-regulated genes in the GJB1-high group were enriched in extracellular matrix components and membrane junctions, while up-regulated genes were associated with mitochondrial function and cellular respiration. Our findings suggest a dual role for GJB1 in breast cancer. Although it is generally down-regulated, high GJB1 expression is associated with poorer survival, implying a potential oncogenic role. Further studies are needed to clarify the role of GJB1 in breast cancer and explore its therapeutic implications.

MMP-2, MMP-9 and TIMP-2 expression in pituitary neuroendocrine tumors and their relationship with cavernous sinus invasion

Objective: To evaluate the expression of matrix metalloproteinases-2 (MMP-2) and -9 (MMP-9) and tissue inhibitor of metalloproteinase-2 (TIMP-2) in pituitary tumors, and investigate their correlation with circulating plasma proteins and cavernous sinus invasion. Additionally, the Ki-67 index was also assessed. Methods: Seventy-four patients (37 females) with pituitary adenomas were included, with preoperative peripheral blood collected in 29 cases. Tumor samples were evaluated for MMP-2, MMP-9, TIMP-2, and Ki-67 expression by immunohistochemistry. Protein plasma was semi-quantitatively detected using a commercial membrane antibody array. Results: Sixteen patients presented tumors invading the cavernous sinus. MMP-2 and TIMP-2 were slightly increased in these tumors compared to the non-invasive group, but the difference was not statistically significant. MMP-9 and TIMP-2 plasma concentrations did not correlate with tumor protein expression and also did not differ between the two groups. MMP-2 was not detected in plasma in any case. No statistically significant difference was observed when different tumors subtypes were considered. A significant difference was observed in tumor size [3.4 cm (2.8-4.9) vs. 1.9 cm (1.3-2.6); p &lt; 0.001] and in the Ki-67 index [1.8% (0.3-2.5) vs. 0.5% (0.2-1.0); p = 0.01] between the invasive and non-invasive groups, respectively. Conclusions: In this cohort, we found no significant correlation between tissue and plasma levels of MMP-2, MMP-9, and TIMP-2 and cavernous sinus invasion in pituitary tumors. Further investigation is needed to elucidate the potential role of these markers in invasiveness of pituitary tumors.

Histopathological spectrum of ovarian tumors in Jharkhand, India: A retrospective study

ABSTRACT Background: Ovarian tumors are the most prevalent neoplasms worldwide, affecting women of all ages. According to Globocan’s 2022 projections, by 2050, the number of women diagnosed with ovarian cancer worldwide will increase by over 55% to 503,448. The number of women dying from ovarian cancer is projected to increase to 350,956 each year, an increase of almost 70% from 2022. Aims and Objectives: The aim of this study was to analyze the various histopathological spectra of ovarian tumors according to the latest 2020 WHO classification and to assess the age distribution, frequency of incidence, and laterality of different subtypes of ovarian tumors. Materials and Methods: This retrospective study included 190 cases of histopathologically proven ovarian tumors reported by the pathology department from March 2020 to March 2024 at the Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India. Results: A total of 190 cases were studied, 154 (81.1%) were benign, 8 (4.2%) were borderline, and 28 (14.7%) cases were malignant. Surface epithelial tumors (SETs) were the most common (n = 139, 73.2%), followed by germ cell tumors (GCTs) (n = 38, 20%). Serous cystadenomas (n = 63, 33%) were the most common benign tumors, whereas the most common malignant tumors were serous carcinomas ( n=11, 5.7% ). Most ovarian tumors (n = 45, 23.68%) occurred in the 31–40-year-old age group. Conclusion: The present study showed various histopathological patterns of ovarian tumors. This study indicated a slight increase in the prevalence of malignant ovarian tumors in the middle-aged group and and a relative increase in the percentage of SETs over GCTs in recent years in our tertiary care center in Jharkhand, India. With limited resources in our institute, histopathological examination remains the mainstay for the early diagnosis of these tumors and their timlely and appropriate management. Categories: Pathology.

Identifying predictors of treatment response and molecular changes induced by neoadjuvant chemotherapy and endocrine therapy in hormone receptor-positive/HER2-negative breast cancer: the NEOENDO translational study

Predictors of response to neoadjuvant chemotherapy (NACT) and endocrine therapy (NET) in hormone receptor-positive (HoR+)/human epidermal growth factor receptor 2 (HER2)-negative breast cancer (BC) are required. Also, pathological and molecular changes induced by both strategies and their impact on patients' outcomes have not been reported so far. In a cohort of 186 patients with early-stage HoR+/HER2-negative BC treated with NACT or NET, we assessed the association of baseline main clinicopathological features and PAM50 gene expression (GE), intrinsic subtypes (IS) and risk-of-relapse (ROR-P) score with pathological outcomes according to treatment strategy. Molecular NACT/NET-induced changes were described and compared, along with their associations with event-free survival (EFS). Comparison of the two cohorts after propensity score matching (PSM) was used as sensitivity analysis. Molecular changes were confirmed in cell lines. NACT was associated with higher rates of residual cancer burden (RCB)-0/I than NET in the overall population (38.2% versus 13.5%, P < 0.001) and after PSM (P= 0.036). PAM50 non-luminal IS were the only independent and positive predictor of RCB-0/I (P= 0.024) in the NACT cohort, while MMP11 messenger RNA levels were the only independent and negative predictor (P= 0.014) in the NET cohort. Both treatments shifted the tumor types toward less aggressive forms (i.e. PAM50 luminal A/normal-like), lowered the risk of recurrence in terms of ROR-P, up-regulated selected immune genes and PAM50 basal-like-related genes/signature and significantly downregulated proliferation-/luminal-/HER2-related genes/signatures, though NACT more than NET. Molecular findings were confirmed after PSM. A net reduction in proliferation-related genes and ROR-P was confirmed in cell lines with chemotherapy and endocrine therapy. Different baseline molecular features associated with diverse kind of responses (ROR-P downstaging, Ki67 reduction or pathological responses) with NACT and NET. Decreasing ROR-P and transitioning the tumor subtype to resemble normal tissue (i.e. PAM50 normal-like) suggested improved EFS. NACT was more effective in the molecular and dimensional tumor 'downstaging' than NET but baseline molecular features associated with differential responses according to treatment strategy. Examining baseline and post-treatment GE might help tailor more personalized and effective care.

A clustering approach to integrative analyses of multiomic cancer data

Rapid technological advances have allowed for molecular profiling across multiple omics domains for clinical decision-making in many diseases, especially cancer. However, as tumor development and progression are biological processes involving composite genomic aberrations, key challenges are to effectively assimilate information from these domains to identify genomic signatures and druggable biological entities, develop accurate risk prediction profiles for future patients, and identify novel patient subgroups for tailored therapy and monitoring. We propose integrative frameworks for high-dimensional multiple-domain cancer data. These Bayesian mixture model-based approaches coherently incorporate dependence within and between domains to accurately detect tumor subtypes, thus providing a catalog of genomic aberrations associated with cancer taxonomy. The flexible and scalable Bayesian nonparametric strategy performs simultaneous bidirectional clustering of the tumor samples and genomic probes to achieve dimension reduction. We describe an efficient variable selection procedure that can identify relevant genomic aberrations and potentially reveal underlying drivers of disease. Although the work is motivated by lung cancer datasets, the proposed methods are broadly applicable in a variety of contexts involving high-dimensional data. The success of the methodology is demonstrated using artificial data and lung cancer omics profiles publicly available from The Cancer Genome Atlas.

Advances in machine learning for tumour classification in cancer of unknown primary: A mini-review

Cancers of unknown primary (CUP) are a heterogeneous group of aggressive metastatic cancers where standardised diagnostic techniques fail to identify the organ where it originated, resulting in a poor prognosis and resistance to treatment. Recent advances in large-scale sequencing techniques have enabled the identification of mutational signatures specific to particular tumour subtypes, even from liquid biopsy samples such as blood. This breakthrough paves the way for the development of new cost-effective diagnostic strategies. This mini-review explores recent advancements in Machine Learning (ML) and its application to tumour classification methods for CUP patients, identifying its weaknesses and strengths when classifying the tumour type. In the era of multi-omics, integrating several sources of information (e.g., imaging, molecular biomarkers, and family history) requires important theoretical advancements: increasing the dimensionality of the problem can result in lowering the predictive accuracy and robustness when data is scarce. Here, we review and discuss different architectures and strategies for incorporating cutting-edge machine learning into CUP diagnosis, aiming to bridge the gap between theory and clinical practice.

Therapeutic Outcomes in 691 Angiosarcomas of the Head and Neck-A Retrospective SEER Study.

Angiosarcomas (AS) are an aggressive subtype of head and neck tumors. Current outcome research is limited to single-surgeon and/or single-institution data. This study analyzes the Surveillance, Epidemiology, and End Results program (SEER) database data to improve outcomes of head and neck AS. In this study, the authors accessed the SEER database from 2000 to 2020 to identify patients who underwent surgical and/or nonsurgical treatment of head and neck AS. Data included demographics, diagnostic workup, tumor characteristics, treatment pathways, and therapy outcomes. The study included 691 patients (210 females, 481 males) with AS of the head and neck. The median tumor size was 40mm. Most patients (63%) underwent surgery, with surgery followed by radiation being the most common treatment combination (50%). Median survival was 16 months. Patients treated with surgery showed significantly longer survival (18mo) compared with nonsurgery cases (12mo, P<0.001). Neoadjuvant treatment significantly improved survival (28mo) compared with non-neoadjuvant cases (15mo, P=0.023). Adjuvant treatment also improved overall survival (19mo) versus nonadjuvant treatment (14mo, P<0.001). There was no significant difference between neoadjuvant and adjuvant treatment outcomes (P=0.270). Bottom of Form This study compares the outcomes of multimodal treatments, especially surgery and radiotherapy, for head and neck AS, emphasizing tailored approaches to optimize patient outcomes and extend survival. Future research should refine these strategies.

Identification of tumor-antigen signatures and immune subtypes for mRNA vaccine selection in muscle-invasive bladder cancer

BackgroundMuscle-invasive bladder cancer continues to lack reliable diagnostic and prognostic biomarkers. Recently, tumor vaccines targeting specific molecules have emerged as a promising treatment in inhibiting tumor progression, which was rekindled under the background of coronavirus disease-2019 pandemic. However, the application of mRNA vaccine targeting muscle-invasive bladder cancer–specific antigens remains limited, and there has been a lack of comprehensive studies or validations to identify suitable patient subgroups for vaccination. This study aims to explore novel muscle-invasive bladder cancer antigen signatures to identify patients most likely to benefit from vaccination. MethodsGene expression profiles of muscle-invasive bladder cancer samples, along with corresponding clinical data, were retrieved from the Cancer Genome Atlas Program. The least absolute shrinkage and selection operator model was applied to develop signatures for stratifying muscle-invasive bladder cancer patients. Prognostic accuracy of each factor was assessed using receiver operating characteristic analysis. Tumor Immune Estimation Resource was employed to visualize the relationship between the proportion of antigen-presenting cells and the expression of selected genes. The CIBERSORT and WGCNA R packages were used to identify differences in immune infiltration levels across muscle-invasive bladder cancer subgroups. Additionally, the STRING database and Cytoscape were used to construct the protein-protein interaction network. CCK-8 and colony formation assays were employed in invitro experiments. ResultsA total of 49 potential tumor antigens were identified. Using least absolute shrinkage and selection operator Cox regression, 14 tumor antigens were selected to develop a risk evaluation signature. The risk score signature can serve as a valuable tool for predicting the outcomes of muscle-invasive bladder cancer patients. Based on differential clinical, molecular, and immune-related gene profiles, muscle-invasive bladder cancer patients were classified into 2 subtypes: the immune “cold” subtype (immune score 1) and the immune “hot” subtype (immune score 2). The immune score signature, developed using a logistic score model, effectively distinguishes between patients more likely to belong to immune score 1 or 2. Notably, patients with a high risk score exhibited a higher proportion of immune score 2 compared to those with a low risk score. Additionally, the prognostic accuracy was significantly enhanced when the risk score and immune score were combined. Different tumor subtypes displayed distinct immune landscapes and signaling pathways. Moreover, novel tumor antigens associated with oxidative stress were identified. ConclusionThe risk score and immune score signatures based on tumor antigens have identified potential effective neo-antigens for the development of mRNA vaccines targeting muscle-invasive bladder cancer. Patients with low risk score and immune score 1 subtype are more likely to benefit from mRNA vaccination. Additionally, this study highlights the critical role of oxidative stress in modulating the efficacy of the mRNA vaccine.