1
|
Buchheit JT, Schacht D, Kulkarni SA. Update on Management of Ductal Carcinoma in Situ. Clin Breast Cancer 2024; 24:292-300. [PMID: 38216382 DOI: 10.1016/j.clbc.2023.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/15/2023] [Accepted: 12/22/2023] [Indexed: 01/14/2024]
Abstract
Ductal carcinoma in situ (DCIS) represents 18% to 25% of all diagnosed breast cancers, and is a noninvasive, nonobligate precursor lesion to invasive cancer. The diagnosis of DCIS represents a wide range of disease, including lesions with both low and high risk of progression to invasive cancer and recurrence. Over the past decade, research on the topic of DCIS has focused on the possibility of tailoring treatment for patients according to their risk for progression and recurrence, which is based on clinicopathologic, biomolecular and genetic factors. These efforts are ongoing, with recently completed and continuing clinical trials spanning the continuum of cancer care. We conducted a review to identify recent advances on the topic of diagnosis, risk stratification and management of DCIS. While novel imaging techniques have increased the rate of DCIS diagnosis, questions persist regarding the optimal management of lesions that would not be identified with conventional methods. Additionally, among trials investigating the potential for omission of surgery and use of active surveillance, 2 trials have completed accrual and 2 clinical trials are continuing to enroll patients. Identification of novel genetic patterns is expanding our potential for risk stratification and aiding our ability to de-escalate radiation and systemic therapies for DCIS. These advances provide hope for tailoring of DCIS treatment in the near future.
Collapse
Affiliation(s)
- Joanna T Buchheit
- Northwestern Quality Improvement, Research, & Education in Surgery (NQUIRES), Northwestern University Feinberg School of Medicine, Chicago, IL
| | - David Schacht
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Swati A Kulkarni
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL.
| |
Collapse
|
2
|
Roy S, Singh J, Ray SS. Weighted Combination of Łukasiewicz implication and Fuzzy Jaccard similarity in Hybrid Ensemble Framework (WCLFJHEF) for Gene Selection. Comput Biol Med 2024; 170:107981. [PMID: 38262204 DOI: 10.1016/j.compbiomed.2024.107981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 01/02/2024] [Accepted: 01/12/2024] [Indexed: 01/25/2024]
Abstract
A framework is developed for gene expression analysis by introducing fuzzy Jaccard similarity (FJS) and combining Łukasiewicz implication with it through weights in hybrid ensemble framework (WCLFJHEF) for gene selection in cancer. The method is called weighted combination of Łukasiewicz implication and fuzzy Jaccard similarity in hybrid ensemble framework (WCLFJHEF). While the fuzziness in Jaccard similarity is incorporated by using the existing Gödel fuzzy logic, the weights are obtained by maximizing the average F-score of selected genes in classifying the cancer patients. The patients are first divided into different clusters, based on the number of patient groups, using average linkage agglomerative clustering and a new score, called WCLFJ (weighted combination of Łukasiewicz implication and fuzzy Jaccard similarity). The genes are then selected from each cluster separately using filter based Relief-F and wrapper based SVMRFE (Support Vector Machine with Recursive Feature Elimination). A gene (feature) pool is created by considering the union of selected features for all the clusters. A set of informative genes is selected from the pool using sequential backward floating search (SBFS) algorithm. Patients are then classified using Naïve Bayes'(NB) and Support Vector Machine (SVM) separately, using the selected genes and the related F-scores are calculated. The weights in WCLFJ are then updated iteratively to maximize the average F-score obtained from the results of the classifier. The effectiveness of WCLFJHEF is demonstrated on six gene expression datasets. The average values of accuracy, F-score, recall, precision and MCC over all the datasets, are 95%, 94%, 94%, 94%, and 90%, respectively. The explainability of the selected genes is shown using SHapley Additive exPlanations (SHAP) values and this information is further used to rank them. The relevance of the selected gene set are biologically validated using the KEGG Pathway, Gene Ontology (GO), and existing literatures. It is seen that the genes that are selected by WCLFJHEF are candidates for genomic alterations in the various cancer types. The source code of WCLFJHEF is available at http://www.isical.ac.in/~shubhra/WCLFJHEF.html.
Collapse
Affiliation(s)
- Sukriti Roy
- Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India.
| | - Joginder Singh
- Center for Soft Computing Research, Indian Statistical Institute, Kolkata 700108, India.
| | - Shubhra Sankar Ray
- Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India; Center for Soft Computing Research, Indian Statistical Institute, Kolkata 700108, India.
| |
Collapse
|
3
|
He K, Li J, Huang X, Zhao W, Wang K, Wang T, Chen J, Wang Z, Yi J, Zhao S, Zhao L. KNL1 is a prognostic and diagnostic biomarker related to immune infiltration in patients with uterine corpus endometrial carcinoma. Front Oncol 2023; 13:1090779. [PMID: 36776306 PMCID: PMC9913269 DOI: 10.3389/fonc.2023.1090779] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 01/11/2023] [Indexed: 01/29/2023] Open
Abstract
Background The incidence and mortality of uterine corpus endometrial carcinoma (UCEC) are increasing yearly. There is currently no screening test for UCEC, and progress in its treatment is limited. It is important to identify new biomarkers for screening, diagnosing and predicting the outcomes of UCEC. A large number of previous studies have proven that KNL1 is crucial in the development of lung cancer, colorectal cancer and cervical cancer, but there is a lack of studies about the role of KNL1 in the development of UCEC. Methods The mRNA and protein expression data of KNL1 in The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and UALCAN databases and related clinical data were used to analyze the expression differences and clinical correlations of KNL1 in UCEC. A total of 108 clinical samples were collected, and the results of bioinformatics analysis were verified by immunohistochemistry. KNL1 and its related differentially expressed genes were used to draw a volcano map, construct a PPI protein interaction network, and perform gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA) and immune infiltration analysis to predict the function of KNL1 during UCEC progression. The prognostic data of TCGA and 108 clinical patients were used to analyze the correlation of KNL1 expression with the survival of patients, and KM survival curves were drawn. The UCEC cell lines Ishikawa and Hec-1-A were used to verify the function of KNL1. Results KNL1 is significantly overexpressed in UCEC and is associated with a poor prognosis. KNL1 overexpression is closely related to cell mitosis, the cell cycle and other functions and is correlated with the International Federation of Gynecology and Obstetrics (FIGO) stage, histological grade and other characteristics of UCEC patients. Knockdown of KNL1 expression in UCEC cell lines can inhibit their proliferation, invasion, metastasis and other phenotypes. Conclusion KNL1 is a prognostic and diagnostic biomarker associated with immune evasion in patients with UCEC.
Collapse
Affiliation(s)
- Kang He
- Department of Rehabilitation, School of Nursing, Jilin University, Changchun, China
| | - Jingze Li
- Department of Rehabilitation, School of Nursing, Jilin University, Changchun, China
| | - Xuemiao Huang
- Department of Rehabilitation, School of Nursing, Jilin University, Changchun, China
| | - Weixin Zhao
- The Department of Obstetrics and Gynecology, The Second Hospital of Jilin University, Changchun, Jilin, China
| | - Kai Wang
- Department of Rehabilitation, School of Nursing, Jilin University, Changchun, China
| | - Taiwei Wang
- Department of Rehabilitation, School of Nursing, Jilin University, Changchun, China
| | - Junyu Chen
- The Department of Obstetrics and Gynecology, The Second Hospital of Jilin University, Changchun, Jilin, China
| | - Zeyu Wang
- Department of Rehabilitation, School of Nursing, Jilin University, Changchun, China
| | - Jiang Yi
- Department of Rehabilitation, The Second Hospital of Jilin University, Changchun, Jilin, China
| | - Shuhua Zhao
- The Department of Obstetrics and Gynecology, The Second Hospital of Jilin University, Changchun, Jilin, China,*Correspondence: Lijing Zhao, ; Shuhua Zhao,
| | - Lijing Zhao
- Department of Rehabilitation, School of Nursing, Jilin University, Changchun, China,*Correspondence: Lijing Zhao, ; Shuhua Zhao,
| |
Collapse
|