Utilizing Data Visualization to Identify Survival and Treatment Differences Between Women With De Novo and Recurrent Metastatic Breast Cancer

Gilbert, Aidan; Williams, Courtney; Azuero, Andres; Burkard, Mark E.; Kenzik, Kelly; Garrett‐Mayer, Elizabeth; Meersman, Stephen C.; Rocque, Gabrielle Betty

doi:10.1016/j.clbc.2020.11.009

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…Early metastatic recurrence is more likely associated with HR-negative breast cancer. Subtypes of breast cancer based on HER2 and HR have differences in metastatic recurrence; HR negative subgroup has an unfavorable prognosis and earlier recurrence compared to HR-positive subgroups (Gilbert et al, 2021). In the current study, survival between early distant recurrence and de novo cancer remained significantly different even after adjusting HR status.…”

Section: Metastatic Recurrencecontrasting

confidence: 42%

Prognosis of Metastatic Breast Cancer: Differences Between Patients With De Novo and Recurrent Metastatic Breast Cancer

Rashid

Shafiq²,

Ismail

et al. 2023

Biol Clin Sci Res J

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The retrospective study was conducted in the Department of Oncology from January 2021 to January 2022 to evaluate prognostic factors and the prognosis of the patients with recurrent metastatic breast cancer compared to those with de novo metastatic breast cancer. The study was conducted on a total of 400 patients. Patients were categorized into three groups: A) de novo metastatic cancer (MFI < 3 months), B) recurrent metastatic cancer (MFI ⩽24 months), and C) recurrent metastatic cancer (MFI >24 months). The mean follow-up period was 37.2 months, after which 148 (37%) survived. Mean survival in de novo cancer was 30.1 months, and in recurrent cancer was 22.1 months (P=0.14). Subjects with de novo cancer had significantly better survival than those with early metastatic recurrence (MFI <24 months) (P<0.0001). However, the difference between the survival of de novo and late metastatic recurrence (MFI > 24 months) was not statistically significant (P< 0.73). The difference in the impact of prognostic factors among the three metastatic groups was statistically significant (P=0.98). Thus, MFI is a significant prognostic factor for metastatic breast cancer; short MFI indicates an unfavorable prognosis. However, in our study, de novo cancer did not have a very poor prognosis.

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Section: Metastatic Recurrencecontrasting

confidence: 42%

Prognosis of Metastatic Breast Cancer: Differences Between Patients With De Novo and Recurrent Metastatic Breast Cancer

Rashid

Shafiq²,

Ismail

et al. 2023

Biol Clin Sci Res J

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“…A highly positive relationship was observed between the expression of lnc-RAB11B-AS1 and RAB11B . According to a former study, visualization techniques and descriptive indicators can be used to reflect how survival differs with disease subtype, treatment status, and treatment practice patterns [ 42 ]. Through screening the clinicopathological information in the database and referring to relevant literature, we first differentiated HCC patients with different treatment statuses in TCGA, among which 249 were untreated, 18 were treated, 90 were recurrent, and 22 were unreported cases.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis and Validation of the Role of Lnc-RAB11B-AS1 in the Development and Prognosis of Hepatocellular Carcinoma

Wang

Chen

et al. 2022

Cells

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Lnc-RAB11B-AS1 is reported to be dysregulated in several types of cancers and can function as both an oncogene and tumor suppressor gene. To evaluate the potential role of lnc-RAB11B-AS1 in hepatocellular carcinoma (HCC), we investigated and evaluated its expression in HCC based on the data mining of a series of public databases, including TCGA, GEO, ICGC, HPA, DAVID, cBioPortal, GeneMIANA, TIMER, and ENCORI. The data showed downregulation of lnc-RAB11B-AS1 in HCC and was accompanied by the synchronous downregulation of the targeted RAB11B mRNA and its protein. Low expression of lnc-RAB11B-AS1 was associated with shorter overall survival (OS) and disease-free survival (DFS) of HCC patients, PD1/PD-L1 was correlated with low expression of RAB11B. Furthermore, Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed a correlation between immune cell change and non-alcoholic fatty liver disease. The above findings revealed that lnc-RAB11B-AS1 was down-regulated in HCC and closely associated with the clinical stage of the HCC patients, suggesting that lnc-RAB11B-AS1 could be a possible predictor for HCC and a potential new therapeutic target for the treatment of HCC.

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“…For instance, CancerLinQ, a non-profit subsidiary of ASCO, collects and analyzes data from multiple healthcare systems across the USA [171]. It provides real-world data on patient populations that are representative of those encountered in practice, thus reflecting patient variability while enabling analysis for scientific or other purposes [172][173][174]. Other computational technologies have developed diagnostic and therapeutic algorithms to analyze data from published literature, physician clinical notes, and scientific guidelines, including guidelines from the National Comprehensive Cancer Network (NCCN) Clinical Practice in Oncology.…”

Section: Challenges and Considerationsmentioning

confidence: 99%

Clinical trial design in the era of precision medicine

et al. 2022

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Recent rapid biotechnological breakthroughs have led to the identification of complex and unique molecular features that drive malignancies. Precision medicine has exploited next-generation sequencing and matched targeted therapy/immunotherapy deployment to successfully transform the outlook for several fatal cancers. Tumor and liquid biopsy genomic profiling and transcriptomic, immunomic, and proteomic interrogation can now all be leveraged to optimize therapy. Multiple new trial designs, including basket and umbrella trials, master platform trials, and N-of-1 patient-centric studies, are beginning to supplant standard phase I, II, and III protocols, allowing for accelerated drug evaluation and approval and molecular-based individualized treatment. Furthermore, real-world data, as well as exploitation of digital apps and structured observational registries, and the utilization of machine learning and/or artificial intelligence, may further accelerate knowledge acquisition. Overall, clinical trials have evolved, shifting from tumor type-centered to gene-directed and histology-agnostic trials, with innovative adaptive designs and personalized combination treatment strategies tailored to individual biomarker profiles. Some, but not all, novel trials now demonstrate that matched therapy correlates with superior outcomes compared to non-matched therapy across tumor types and in specific cancers. To further improve the precision medicine paradigm, the strategy of matching drugs to patients based on molecular features should be implemented earlier in the disease course, and cancers should have comprehensive multi-omic (genomics, transcriptomics, proteomics, immunomic) tumor profiling. To overcome cancer complexity, moving from drug-centric to patient-centric individualized combination therapy is critical. This review focuses on the design, advantages, limitations, and challenges of a spectrum of clinical trial designs in the era of precision oncology.

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Utilizing Data Visualization to Identify Survival and Treatment Differences Between Women With De Novo and Recurrent Metastatic Breast Cancer

Cited by 5 publications

References 25 publications

Prognosis of Metastatic Breast Cancer: Differences Between Patients With De Novo and Recurrent Metastatic Breast Cancer

Prognosis of Metastatic Breast Cancer: Differences Between Patients With De Novo and Recurrent Metastatic Breast Cancer

Bioinformatics Analysis and Validation of the Role of Lnc-RAB11B-AS1 in the Development and Prognosis of Hepatocellular Carcinoma

Clinical trial design in the era of precision medicine

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