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PubMed2000

Numerical aberrations of chromosomes 1 and 17 correlate with tumor site in human gastric carcinoma of the diffuse and intestinal types. Fluorescence in situ hybridization analysis on gastric biopsies.

Recent studies predict that tumor aneuploidy plays a direct role in tumor instability. The relationship between interphase cytogenetics, histology, grade, and tumor site was analyzed in 20 primary gastric carcinomas. Using fluorescence in-situ hybridization, the numerical changes of centromeric sequences of chromosomes 1, 3, 10, and 17 were directly analyzed in gastric biopsies. Polysomic copy numbers of chromosomes 1 and 17 were discovered in 63% (10 of 16) and 59% (10 of 17), respectively, of informative cancer cases. Chromosome 3 and 10 signal number changes were found in only 6% (1 of 16) and 13% (1 of 8), respectively, of informative cancer cases. There was a positive correlation between the appearance of polysomic nuclear target sites of chromosomes 1 and 17 (correlation coefficient r = 0.72; p < 0.005). Copy number changes were not significantly related to histologic subtypes of either the Laurén or WHO classifications. However, incidence of cancers having dual polysomic signal number abnormalities for both chromosomes 1 and 17 was significantly correlated to tumor location at the cardia. The data suggests that (i) human gastric cancer appears in two genomic groups that can be reliably diagnosed by fluorescence in-situ hybridization on routine biopsy sections, (ii) numerical aberrations of chromosomes 1, 3, 10, and 17 are largely independent of histologic subtypes, and (iii) polysomic copy number abnormalities of chromosomes 1 and 17 correlate to intragastric tumor site and are highest in cardia cancers, suggesting high tumor instability at this particular location.

Aged↗

PubMed2006

Predicting survival within the lung cancer histopathological hierarchy using a multi-scale genomic model of development.

BACKGROUND: The histopathologic heterogeneity of lung cancer remains a significant confounding factor in its diagnosis and prognosis-spurring numerous recent efforts to find a molecular classification of the disease that has clinical relevance. METHODS AND FINDINGS: Molecular profiles of tumors from 186 patients representing four different lung cancer subtypes (and 17 normal lung tissue samples) were compared with a mouse lung development model using principal component analysis in both temporal and genomic domains. An algorithm for the classification of lung cancers using a multi-scale developmental framework was developed. Kaplan-Meier survival analysis was conducted for lung adenocarcinoma patient subgroups identified via their developmental association. We found multi-scale genomic similarities between four human lung cancer subtypes and the developing mouse lung that are prognostically meaningful. Significant association was observed between the localization of human lung cancer cases along the principal mouse lung development trajectory and the corresponding patient survival rate at three distinct levels of classical histopathologic resolution: among different lung cancer subtypes, among patients within the adenocarcinoma subtype, and within the stage I adenocarcinoma subclass. The earlier the genomic association between a human tumor profile and the mouse lung development sequence, the poorer the patient's prognosis. Furthermore, decomposing this principal lung development trajectory identified a gene set that was significantly enriched for pyrimidine metabolism and cell-adhesion functions specific to lung development and oncogenesis. CONCLUSIONS: From a multi-scale disease modeling perspective, the molecular dynamics of murine lung development provide an effective framework that is not only data driven but also informed by the biology of development for elucidating the mechanisms of human lung cancer biology and its clinical outcome.

Adenocarcinoma↗

PubMed2021

Automated Extraction of Tumor Staging and Diagnosis Information From Surgical Pathology Reports.

PURPOSE: Typically stored as unstructured notes, surgical pathology reports contain data elements valuable to cancer research that require labor-intensive manual extraction. Although studies have described natural language processing (NLP) of surgical pathology reports to automate information extraction, efforts have focused on specific cancer subtypes rather than across multiple oncologic domains. To address this gap, we developed and evaluated an NLP method to extract tumor staging and diagnosis information across multiple cancer subtypes. METHODS: The NLP pipeline was implemented on an open-source framework called Leo. We used a total of 555,681 surgical pathology reports of 329,076 patients to develop the pipeline and evaluated our approach on subsets of reports from patients with breast, prostate, colorectal, and randomly selected cancer subtypes. RESULTS: Averaged across all four cancer subtypes, the NLP pipeline achieved an accuracy of 1.00 for International Classification of Diseases, Tenth Revision codes, 0.89 for T staging, 0.90 for N staging, and 0.97 for M staging. It achieved an F1 score of 1.00 for International Classification of Diseases, Tenth Revision codes, 0.88 for T staging, 0.90 for N staging, and 0.24 for M staging. CONCLUSION: The NLP pipeline was developed to extract tumor staging and diagnosis information across multiple cancer subtypes to support the research enterprise in our institution. Although it was not possible to demonstrate generalizability of our NLP pipeline to other institutions, other institutions may find value in adopting a similar NLP approach-and reusing code available at GitHub-to support the oncology research enterprise with elements extracted from surgical pathology reports.

Humans↗

PubMed2002

Survival in patients with rare subtypes of renal cell carcinoma.

OBJECTIVE: To evaluate the survival of patients with rare malignant histological subtypes of renal cancer. PATIENTS AND METHODS: The Heidelberg classification of renal cell carcinoma (RCC) divides tumours into clear cell carcinoma (CCC), papillary cancer (PC), chromophobic cancer (ChC) and collecting duct carcinoma (CDC). Sarcomatoid tumours are in a different subgroup treated as a final stage of histological progression. Between 1990 and 1997, 319 nephrectomies were undertaken because of RCC in 317 patients. In 42 patients (13%) the pathological findings showed other than CCC; in 13 PC was confirmed histologically, in nine ChC, in 11 a mixed type of CCC and sarcomatoid type, in seven a sarcomatoid tumour and in four, CDC. RESULTS: One patient of the 13 with PC and two of the nine with ChC died. The worst prognosis was in those with CDC, CCC-sarcomatoid and sarcomatoid tumours, as all these patients died. CONCLUSION: The histopathological differentiation of RCC into subtypes gives additional useful prognostic information.

Adult↗

PubMed2006

Interobserver agreement and reproducibility in classification of invasive breast carcinoma: an NCI breast cancer family registry study.

The United States National Cancer Institute Breast/Ovarian Cancer Family Registry is the largest international Registry of this type; over 37 724 individuals have been enrolled to date. One activity of this Registry is the semicentralized pathologic review of tumors from all probands. Given the semicentralized nature of the review, this study was undertaken to determine the reproducibility, source(s) of classification discrepancies and stratagems to circumvent discrepancies for histologic subtyping and grading of invasive breast cancer among the reviewing pathologists. A total of 13 pathologists reviewed 35 invasive breast cancers and classified them by primary and secondary histologic type, Nottingham grade and score. Lymph-vascular space invasion, circumscribed margins, syncytial growth and lymphocytic infiltrate were also evaluated. A training session using a separate set of slides was conducted prior to the study. General agreement, in terms of category-specific kappa's and percent agreement, and accuracy of classification relative to a reference standard were determined. Classification of histologic subtype was most consistent (and accurate) for mucinous carcinoma (kappa=1.0), followed by tubular (kappa=0.8) and lobular subtypes (kappa=0.8). Classification of medullary subtype was moderate (kappa=0.4), but additional evaluation of degree of lymphocytic infiltrate, syncytial growth and circumscribed margins identified most cases. Category-specific kappa's were moderate to good for Nottingham grade (kappa=0.5-0.7), with the greatest agreement obtained in categorizing grade I (kappa=0.7), and grade III tumors (kappa=0.7). A flexible classification strategy that employs individual and combined criteria provides good interobserver agreement for invasive breast cancers with uniform, unambiguous histology and compensates for classification discrepancies in the more histologically ambiguous or heterogeneous cancers.

Breast↗

PubMed2004

Primary cutaneous B-cell lymphomas. Current concepts. I.

BACKGROUND: Primary cutaneous B-cell lymphoma (pCBCL) has only recently been recognized as a distinct clinicopathologic entity. This entity represents a wide spectrum of lymphoproliferative disorders that must be separated from non-Hodgkin's B-cell lymphomas secondarily involving the skin and cutaneous B-cell pseudolymphomas. They are defined as B-cell lymphomas originating in the skin, with no evidence of extracutaneous disease at presentation, as assessed by adequate staging procedures. INFORMATION SOURCES: With the advent of improved immunophenotyping and immunogenotyping, increasing numbers of pCBCL cases are being diagnosed. However, there is still confusion regarding the classification, treatment, and prognosis of these patients. The aim of this paper is to provide the clinician with a concise summary of the diagnosis, course, and treatment of pCBCL. Currently, the European Organization for Research and Treatment of Cancer classification is the most adequate classification scheme, identifying the subtypes of pCBCL by clinical behavior and histopathologic findings, and allowing a better management of the patients. STATE OF THE ART: Based on this classification, the most common subtypes of pCBCL are follicular center cell lymphoma and marginal zone B-cell lymphoma, indolent lymphomas with an excellent prognosis (>95% 5-year survival rate in our series). Although local cutaneous recurrences are observed in about 25% of patients, dissemination to internal organs is rare. A less common, specific subtype - the so-called large B-cell lymphoma of the leg(s) - generally has a poorer prognosis, with a 5-year survival rate of approximately 60%. PERSPECTIVES: As a rule, pCBCL is highly responsive to radiation therapy, which should be considered the treatment of choice. Polychemotherapy should be restricted to patients with involvement of several noncontiguous anatomic sites, those refractory or plurirelapsed after radiotherapy, or those with extracutaneous spread.

Adult↗

PubMed2006

Clinicopathologic significance of the basal-like subtype of breast cancer: a comparison with hormone receptor and Her2/neu-overexpressing phenotypes.

DNA microarray profiling studies have led to the classification of invasive breast carcinoma into luminal/estrogen receptor-positive, normal breast-like, Her2/neu-overexpressing, and basal-like types. Among these groups, the basal-like subtype is associated with the poorest clinical outcome in Western countries. To date, the clinicopathologic characteristics of the basal-like carcinomas, compared with other subtypes, have not been described in the Korean population. In this study, we used tissue microarray to examine the expression of basal cytokeratins (CK) (CK5 and CK14) and luminal CK (CK8/18), epidermal growth factor receptor, c-kit, hormone receptors (HRs), p53, and Her2/neu in 776 consecutive patients diagnosed with invasive breast carcinoma from January 1993 to December 1998 and categorized these cases into 5 subgroups (basal-like, HR-expressing, Her2/neu-overexpressing, HR and Her2/neu-expressing, and null subtypes negative for all markers), based on the immunohistochemical data. We identified cases of 114 (14.7%) basal-like, 345 (44.5%) HR-expressing, 133 (17.1%) Her2/neu-overexpressing, 61 (7.8%) HR and Her2/neu-expressing, and 123 (15.9%) null subtypes. Histologically, most basal-like breast cancers were invasive ductal carcinoma, not otherwise specified (98 cases, 86.0%), with high nuclear and/or histologic grades, and most metaplastic carcinomas (6 [75.0%] of 8 cases) were the basal-like subtype. Both basal-like and Her2/neu-overexpressing subtypes were associated with larger tumor sizes (mean, 3.6 and 3.3 cm, respectively) than the HR-expressing group (mean, 2.8 cm) (P = .001 and P = .036, respectively). Nodal stage of Her2/neu-overexpressing subtype was higher than that of basal-like subtype; however, overall stage was not different between the 2 groups (P = .010 and .123, respectively). Distant metastasis was most frequently observed in the Her2/neu-overexpressing subtype (33.8%), which was prognostically the worst subgroup of breast cancers. In contrast to previous findings from Western countries, our analyses reveal that the Her2/neu status is the most important prognostic factor of breast cancers.

Adult↗

PubMed2026

Integrating molecular subtypes, genomics and functional dependencies to identify context-specific therapeutic vulnerabilities in small cell lung cancer.

Small cell lung cancer is one of the most aggressive malignancies, characterized by rapid tumor growth, early metastatic spread and extremely poor survival. Although most patients initially respond to platinum-based chemotherapy, relapse is almost inevitable and treatment options at recurrence remain limited. The recent introduction of immune checkpoint inhibitors has provided only modest clinical benefit, largely due to the fact that these tumors are immunologically cold. These limitations highlight the urgent need to better understand the molecular features of small cell lung cancer in order to identify more effective therapeutic strategies. In this review, we summarize current knowledge of the molecular landscape of small cell lung cancer, with particular emphasis on transcriptome-based classifications that have identified four major molecular subtypes defined by distinct transcriptional regulators and gene expression programs. We discuss how these classifications have improved the biological understanding of the disease and stimulated efforts to develop subtype-specific therapeutic strategies. At the same time, we highlight important limitations of this framework, including the remarkable transcriptional plasticity of tumor cells, which allows dynamic transitions between subtypes and may contribute to therapeutic resistance. To address these challenges, we examine additional molecular features that may represent more stable vulnerabilities, including recurrent genomic alterations, such as the widespread loss of tumor suppressor genes or oncogene amplifications through extrachromosomal DNA. We also discuss emerging approaches aimed at identifying novel context-specific cancer dependencies, including genome-scale functional screens in vitro and in vivo and genetic restraint analyses. Finally, we consider the growing potential of liquid biopsy strategies, which exploit the high level of circulating tumor DNA in patients with this disease to detect clinically relevant genomic alterations and monitor tumor evolution. Overall, this review highlights both the opportunities and challenges associated with molecular stratification in small cell lung cancer. The integration of transcriptional classifications with genomic and functional approaches may help identify more robust therapeutic vulnerabilities and guide the development of more effective treatments for this highly aggressive disease.

Cancer vulnerabilities↗

PubMed2025

MWENA: a novel sample re-weighting-based algorithm for disease classification and data interpretation using extracellular vesicles omics data.

BACKGROUND AND OBJECTIVE: Extracellular vesicles (EVs), considered as a form of liquid biopsy, have gained significant attention in recent years due to their stability and the preservation of disease markers. Research studies underscore the clinical significance of molecules found in EVs, highlighting their role as communicative mediators between cells. However, analyzing this data is challenging due to noisy measurements, having far more variables than samples, and some groups (e.g., disease subtypes or experimental conditions) having much less data than others. We therefore develop an algorithm to address aforementioned challenges for the classification of imbalanced EVs omics data. METHODS AND RESULTS: We propose the EV Meta-Weight Elastic Net Algorithm (MWENA), which utilizes logistic regression with elastic net regularization for the classification and identification of EV signatures, effectively addressing the challenges posed by high-dimensional small sample sizes. To mitigate issues related to class imbalance and high noise levels, MWENA incorporates an automatic sample re-weighting function, which uses a meta-net to adaptively learn generalizable patterns directly from the data itself. We validate the MWENA algorithm on both simulated data and EVs omics data, covering six classification tasks that involve four different types of diseases (pancreatic ductal adenocarcinoma, interstitial lung diseases, colorectal cancer, and ovarian cancer) and three clinical scenarios (disease diagnosis, disease-stage screening, and disease-subtype classification). Compared to other machine learning methods, MWENA demonstrates superiority in identifying small class samples and achieves the highest scores in both sensitivity and G-means. Biological analysis is also performed to further explore the significance of selected signatures as biological markers and their roles in disease mechanisms. CONCLUSIONS: We anticipate that our proposed approach will take a modest step in harnessing EV omics data to discover biomarkers, aiding researchers in gaining a comprehensive understanding of biological processes.

Extracellular Vesicles↗

PubMed2000

Primary cutaneous B-cell lymphoma: review and current concepts.

PURPOSE: Primary cutaneous B-cell lymphoma (PCBCL) has only recently been recognized as a distinct clinical entity. With the advent of improved immunophenotyping and immunogenotyping, increasing numbers of PCBCL cases are being diagnosed. However, there is much confusion regarding the classification, treatment, and prognosis of these patients. The purpose of this article is to review and analyze the available data to provide the clinician with a concise summary of the diagnosis, prognosis, and treatment of PCBCL. DESIGN: We conducted a thorough review of the medical literature on PCBCL, with a focus on classification, prognosis, and treatment trials. RESULTS AND CONCLUSION: PCBCL is defined as a B-cell lymphoma originating in the skin. There is no evidence of extracutaneous disease at presentation and for 6 months after diagnosis, as assessed by adequate staging procedures. Currently, the European Organization for Research and Treatment of Cancer classification is the most concise disease classification scheme, dividing the subtypes of PCBCL by clinical behavior and histopathologic findings. Based on this classification, the most common subtype of PCBCL is follicular center cell lymphoma. PCBCL is generally an indolent form of lymphoma with a good prognosis. Although local cutaneous recurrences are observed in 25% to 68% of patients, dissemination to internal organs is rare. Five-year survival rates typically range from 89% to 96%. A specific subtype, large B-cell lymphoma of the leg, is noted to have a poorer prognosis, with a 5-year survival rate of 58%. Overly aggressive treatment of PCBCL has not been shown to improve survival or prevent relapse. The treatment of choice usually varies depending on the type of PCBCL, the body surface area, and the location of the involvement, as well as the age and general health condition of the patient. The majority of studies indicate that PCBCL is highly responsive to radiation therapy. Polychemotherapy should be reserved for involvement of noncontiguous anatomic sites or those with extracutaneous spread.

Humans↗

PubMed2026

Immune subtyping of colorectal adenoma identifies a subtype with activated adaptive immunity ahead of progressing to cancer.

BACKGROUND: Colorectal adenomas (CRA) represent precursor lesions with varying risks of malignant transformation. However, molecular subtyping, particularly immune-related classification, remains underexplored in adenomas. This study aims to characterize the immune landscape of CRA through immune subtyping and evaluate its association with cancer progression, gene expression signatures, and functional pathways. METHODS: We conducted a retrospective analysis of transcriptomic data from multiple cohorts of CRA samples. Immune subtypes were identified using non-negative matrix factorization (NMF) based on immune-related genes. Diverse deconvolution algorithms were used to estimate immune cell infiltration. The immune status alteration in premalignant lesion was further consolidated by single-cell transcriptome data. Differential gene expression analysis was performed between subtypes, followed by functional enrichment analyses (Gene Ontology [GO] and Kyoto Encyclopedia of Genes and Genomes [KEGG]). RESULTS: Two distinct immune subtypes were identified: an immune-enriched subtype characterized by high lymphocyte infiltration and elevated expression of immune-related genes, and an immune-deficient subtype with suppressed immune activity. Differential expression analysis revealed significant upregulation of immune response genes (e.g., CD4, CD86, HLA-DRA) in the immune-enriched subtype. GO and KEGG analyses highlighted enrichments in leukocyte transendothelial migration, chemokine signaling, and antigen processing and presentation pathways. Single-cell result revealed an early occurrence of TIGIT activation and exhausted CD8 T cell features in adenoma when compared to normal tissue. CONCLUSION: This study delineates distinct immune subtypes within CRAs. The immune-enriched subtype demonstrates activated adaptive immunity and may reflect a higher potential for immune surveillance, while the immune-deficient subtype exhibits stromal features suggestive of progressive transformation. These findings provide insights into early immune microenvironment alterations and may inform strategies for risk stratification and immunoprevention in colorectal carcinogenesis.

Colorectal adenoma↗

PubMed2004

Gene selection and classification from microarray data using kernel machine.

The discrimination of cancer patients (including subtypes) based on gene expression data is a critical problem with clinical ramifications. Central to solving this problem is the issue of how to extract the most relevant genes from the several thousand genes on a typical microarray. Here, we propose a methodology that can effectively select an informative subset of genes and classify the subtypes (or patients) of disease using the selected genes. We employ a kernel machine, kernel Fisher discriminant analysis (KFDA), for discrimination and use the derivatives of the kernel function to perform gene selection. Using a modified form of KFDA in the minimum squared error (MSE) sense and the gradients of the kernel functions, we construct an effective gene selection criterion. We assess the performance of the proposed methodology by applying it to three gene expression datasets: leukemia dataset, breast cancer dataset and colon cancer dataset. Using a few informative genes, the proposed method accurately and reliably classified cancer subtypes (or patients). Also, through a comparison study, we verify the reliability of the gene selection and discrimination results.

Algorithms↗

PubMed2026

Expression patterns of potential targets for antibody-directed therapy in metastatic castration-resistant prostate cancer patients.

INTRODUCTION: Survival in metastatic castration-resistant prostate cancer (mCRPC) patients remains limited and treatment is complicated by tumor heterogeneity. As antibody-based therapeutics emerge, identifying actionable antigen targets and patient subgroups most likely to benefit is essential. MATERIALS & METHODS: Gene expression of 62 antibody-targetable proteins was analyzed in 296 mCRPC biopsies. These genes encode proteins targeted by approved or investigational antibody-based cancer therapeutics. Associations between target expression with genomic classifications and transcriptomic subtypes were evaluated. Target expression was also assessed in tumors with low expression of established mCRPC targets. Subgroup-specific targets were validated in an independent cohort and single-cell transcriptomics. RESULTS: Established targets KLK2, FOLH1 (PSMA) and STEAP1 showed the highest median expression across the cohort. Target expression did not correlate with genomic classifications, including homologous recombination deficiency, microsatellite instability, CDK12, TP53, PTEN or AR alterations Target expression did associate with transcriptomic subtypes: CRPC-AR (driven by androgen receptor-signaling) and CRPC-SCL (stem cell-like features, AP-1/YAP/TAZ-driven), displayed the highest expression of multiple targets, including KLK2, FOLH1, and SLC44A4. CRPC-NE (neuroendocrine phenotype) showed heterogeneous expression, with high CD46 expression, whereas CRPC-WNT (Wnt-signaling driven) generally showed low target expression. Notably, CD46 was highly expressed in tumors with low KLK2, FOLH1, and STEAP1 expression, a subgroup associated with poor prognosis. CONCLUSIONS: Although several antibody targets showed broad expression in mCRPC-tumors, expression varied by transcriptomic subtype. Subgroups such as CRPC-WNT expressed fewer targets, suggesting the need for alternative therapeutic strategies. CD46 emerged as a promising target, with wide expression across multiple subtypes, including clinically challenging CRPC-NE and mCRPC tumors lacking expression of established targets.

Humans↗

PubMed2003

Thymoma: a clinicopathologic study based on the new World Health Organization classification.

OBJECTIVE: This study explored the relationship between the histologic subtype of thymoma according to the new World Health Organization histologic classification and the clinical findings, as well as the prognostic significance of the classification. METHODS: A total of 130 patients with thymoma, who underwent resection at the National Cancer Center Hospital, Tokyo, from 1962 to 2000, were studied retrospectively. The histologic subtype of thymoma was determined according to the new World Health Organization histologic classification. The stage was also determined according to a modified Masaoka's classification as stage I, II, III, IVa, or IVb. To determine the factors that may affect the prognosis of thymoma, a multivariate analysis with Cox's proportional hazards regression model was performed. RESULTS: The distribution of histologic subtype was type A (n = 18), type AB (n = 56), type B1 (n = 15), type B2 (n = 29), and type B3 (n = 12). A close correlation was seen between the histologic subtype and stage (P =.000). The overall survivals at 5 and 10 years were 92% and 91%, respectively. The 5- and 10-year survivals according to stage were 100% and 100% (stage I, n = 40; stage II, n = 54), 81% and 76% (stage III, n = 25), and 47% and 47% (stage IV, n = 11), respectively. The difference in survival between stage III and stage IV was significant (P =.000). Patients with type A or AB thymoma demonstrated a 100% survival at both 5 and 10 years. Recurrences were seen in 12 patients with complete resection. According to a multivariate analysis, tumor size (P =.001), completeness of resection (P =.002), histologic subtype (P =.011), and stage (P =.00) were significant prognostic factors. CONCLUSION: The World Health Organization histologic classification significantly correlated with the clinical stage. Tumor size, completeness of resection, histologic subtype, and stage predicted the prognosis of thymoma.

Adult↗

PubMed2025

HallmarkGraph: a cancer hallmark informed graph neural network for classifying hierarchical tumor subtypes.

MOTIVATION: Accurate tumor subtype diagnosis is crucial for precision oncology, yet current methodologies face significant challenges. These include balancing model accuracy with interpretability and the high costs of generating multi-omics data in clinical settings. Moreover, there is a lack of validated models capable of classifying hierarchical tumor subtypes across a comprehensive pan-cancer cohort. RESULTS: We present a graph neural network, HallmarkGraph, the first biologically informed model developed to classify hierarchical tumor subtypes in human cancer. Inspired by cancer hallmarks, the model's architecture integrates transcriptome profiles and gene regulatory interactions to perform multi-label classification. We evaluate the model on a comprehensive pan-cancer cohort comprising 11 476 samples from 26 primary cancers with 405 subtypes up to eight levels. The model demonstrates exceptional performance, achieving 5-fold cross-validation accuracy between 85% and 99% for tumor subtypes labeled with increasing details of genomic information. It also shows good generalizability on a validation dataset of 887 samples, assessed using three metrics that consider tumor subtypes at individual, combined, and sample levels. Benchmarking and ablation experiments show that hallmark-based embeddings slightly influence model performance, while the integrated multilayer perceptron plays a significant role in determining classifier accuracy. Additionally, we use the SHAP method to link cancer hallmarks with genes, identifying key features that influence model decisions. Our findings present a biologically informed machine learning framework capable of tracking tumor transcriptomic trajectories and distinguishing inter- and intra-tumor heterogeneity in pan-cancer. This approach holds promise for enhancing cancer diagnostics. AVAILABILITY AND IMPLEMENTATION: HallmarkGraph is accessible at https://github.com/laixn/HallmarkGraph.

Humans↗

PubMed2006

Location of early colorectal cancers at fold-top may reduce the risk of lymph node metastasis.

PURPOSE: This study was designed to look for significant correlations between location of early colorectal cancer, distance from muscularis mucosae to muscularis propria, and the frequency of lymph node metastasis. METHODS: A total of 166 early colorectal cancers, including 67 surgically resected lesions, were evaluated. The cancers were divided into two groups: metastatic and nonmetastatic. Cancer lesions were further subtyped at the fold-top or fold-bottom. Macroscopic classifications and histology were performed. Absolute invasive depth and distance from muscularis mucosae to muscularis propria was measured. Multivariate analysis was used to assess relationships among the variables. RESULTS: The percentage of polypoid cancer lesions at fold-bottom was higher than at fold-top (74.5 vs. 51.8 percent), whereas flat-type cancer lesions at fold-bottom occurred less often than at fold-top (8.2 vs. 30.4 percent). Logistic regression showed that deep absolute invasive depth, lymphatic and vessel invasion, and cancer location (at fold-bottom) were the significant risk factors for early colorectal cancers leading to lymph-node metastasis. The distance from muscularis mucosae to muscularis propria with lymph-node metastasis (1,396.7 +/- 728.4 microm) was shorter than without lymph-node metastasis (3,533.9 +/- 2,507.8 microm; P < 0.01). Multivariate analysis showed that distance from muscularis mucosae to muscularis propria was a statistically significant factor for early colorectal cancers leading to lymph node metastasis (P = 0.0054). CONCLUSIONS: We conclude that early colorectal cancers at the fold-top or with a long distance from muscularis mucosae to muscularis propria have less tendency to metastasize to lymph nodes. Clinically, these results provide evidence of a new indicator of endoscopic mucosal resection for early colorectal cancers at the fold-top.

Colorectal Neoplasms↗

PubMed2005

GenSo-FDSS: a neural-fuzzy decision support system for pediatric ALL cancer subtype identification using gene expression data.

OBJECTIVE: Acute lymphoblastic leukemia (ALL) is the most common malignancy of childhood, representing nearly one third of all pediatric cancers. Currently, the treatment of pediatric ALL is centered on tailoring the intensity of the therapy applied to a patient's risk of relapse, which is linked to the type of leukemia the patient has. Hence, accurate and correct diagnosis of the various leukemia subtypes becomes an important first step in the treatment process. Recently, gene expression profiling using DNA microarrays has been shown to be a viable and accurate diagnostic tool to identify the known prognostically important ALL subtypes. Thus, there is currently a huge interest in developing autonomous classification systems for cancer diagnosis using gene expression data. This is to achieve an unbiased analysis of the data and also partly to handle the large amount of genetic information extracted from the DNA microarrays. METHODOLOGY: Generally, existing medical decision support systems (DSS) for cancer classification and diagnosis are based on traditional statistical methods such as Bayesian decision theory and machine learning models such as neural networks (NN) and support vector machine (SVM). Though high accuracies have been reported for these systems, they fall short on certain critical areas. These included (a) being able to present the extracted knowledge and explain the computed solutions to the users; (b) having a logical deduction process that is similar and intuitive to the human reasoning process; and (c) flexible enough to incorporate new knowledge without running the risk of eroding old but valid information. On the other hand, a neural fuzzy system, which is synthesized to emulate the human ability to learn and reason in the presence of imprecise and incomplete information, has the ability to overcome the above-mentioned shortcomings. However, existing neural fuzzy systems have their own limitations when used in the design and implementation of DSS. Hence, this paper proposed the use of a novel neural fuzzy system: the generic self-organising fuzzy neural network (GenSoFNN) with truth-value restriction (TVR) fuzzy inference, as a fuzzy DSS (denoted as GenSo-FDSS) for the classification of ALL subtypes using gene expression data. RESULTS AND CONCLUSION: The performance of the GenSo-FDSS system is encouraging when benchmarked against those of NN, SVM and the K-nearest neighbor (K-NN) classifier. On average, a classification rate of above 90% has been achieved using the GenSo-FDSS system.

Algorithms↗

PubMed2006

Classification and risk stratification of invasive breast carcinomas using a real-time quantitative RT-PCR assay.

INTRODUCTION: Predicting the clinical course of breast cancer is often difficult because it is a diverse disease comprised of many biological subtypes. Gene expression profiling by microarray analysis has identified breast cancer signatures that are important for prognosis and treatment. In the current article, we use microarray analysis and a real-time quantitative reverse-transcription (qRT)-PCR assay to risk-stratify breast cancers based on biological 'intrinsic' subtypes and proliferation. METHODS: Gene sets were selected from microarray data to assess proliferation and to classify breast cancers into four different molecular subtypes, designated Luminal, Normal-like, HER2+/ER-, and Basal-like. One-hundred and twenty-three breast samples (117 invasive carcinomas, one fibroadenoma and five normal tissues) and three breast cancer cell lines were prospectively analyzed using a microarray (Agilent) and a qRT-PCR assay comprised of 53 genes. Biological subtypes were assigned from the microarray and qRT-PCR data by hierarchical clustering. A proliferation signature was used as a single meta-gene (log2 average of 14 genes) to predict outcome within the context of estrogen receptor status and biological 'intrinsic' subtype. RESULTS: We found that the qRT-PCR assay could determine the intrinsic subtype (93% concordance with microarray-based assignments) and that the intrinsic subtypes were predictive of outcome. The proliferation meta-gene provided additional prognostic information for patients with the Luminal subtype (P = 0.0012), and for patients with estrogen receptor-positive tumors (P = 3.4 x 10-6). High proliferation in the Luminal subtype conferred a 19-fold relative risk of relapse (confidence interval = 95%) compared with Luminal tumors with low proliferation. CONCLUSION: A real-time qRT-PCR assay can recapitulate microarray classifications of breast cancer and can risk-stratify patients using the intrinsic subtype and proliferation. The proliferation meta-gene offers an objective and quantitative measurement for grade and adds significant prognostic information to the biological subtypes.

Breast Neoplasms↗