Survival outcome and prognostic model of patients with colorectal cancer on phase 1 trials

Kam, Audrey E.; Pendurti, Gopichand; Shah, Umang; Ghalib, Mohammad H.; Chaudhary, Imran; Chuy, Jennifer; Rajdev, Lakshmi; Kaubisch, Andreas; Aparo, Santiago; Mantzaris, Ioannis; Goel, Sanjay

doi:10.1007/s10637-018-0675-9

Cited by 6 publications

(5 citation statements)

References 32 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, studies have reported different genetic models for predicting human cancer. [39][40][41][42][43] A weighted prognosis signature of six lncRNAs, including LINC01583, LINC00276, LU-NAR1, DKFZp434J0226, SFTA1P, and OGFOD3, was constructed for predicting CRC prognosis. However, the samples were only acquired from GSE datasets, and the number of samples was <100, which may not provide enough accuracy.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis

Shi

Guo

Yang

et al. 2022

Cancer Biotherapy and Radiopharmaceuticals

View full text Add to dashboard Cite

Background: Knowledge about the prognostic role of long noncoding RNA (lncRNA) in colorectal cancer (CRC) is limited. Therefore, we constructed a lncRNA-related prognostic model based on data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Materials and Methods: CRC transcriptome and clinical data were downloaded from the GSE20916 dataset and the TCGA database, respectively. R software was used for data processing and analysis. The differential lncRNA expression within the two datasets was first screened, and then intersections were measured. Cox regression and the Kaplan-Meier method were used to evaluate the effects of various factors on prognosis. The area under the curve (AUC) of the receiver operating characteristic curve and a nomogram based on multivariate Cox analysis were used to estimate the prognostic value of the lncRNA-related model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to elucidate the significantly involved biological functions and pathways. Results: A total of 11 lncRNAs were crossed. The univariate Cox analysis screened out two lncRNAs, which were analyzed in the multivariate Cox analysis. A nomogram based on the two lncRNAs and other clinicopathological risk factors was constructed. The AUC of the nomogram was 0.56 at 3 years and 0.71 at 5 years. The 3-year nomogram model was compared with the ideal model, which showed that some indices of the 3-year model were consistent with the ideal model, suggesting that our model was highly accurate. The GO and KEGG enrichment analyses showed that positive regulation of secretion by cells, positive regulation of secretion, positive regulation of exocytosis, endocytosis, and the calcium signaling pathway were differentially enriched in the two-lncRNA-associated phenotype. Conclusions: A two-lncRNA prognostic model of CRC was constructed by bioinformatics analysis. The model had moderate prediction accuracy, LncRNA BBOX1-AS1 and lncRNA FOXP4-AS1 were identified as prognostic biomarkers.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis

Shi

Guo

Yang

et al. 2022

Cancer Biotherapy and Radiopharmaceuticals

View full text Add to dashboard Cite

show abstract

“…To improve patient selection, several studies have established prognostic factors and scoring systems to better estimate OS. In all studies, multivariable analysis consistently identified lactate dehydrogenase (LDH), PS, albumin level, number of metastatic sites, platelet count, hemoglobin, lymphocyte count, and serum sodium as important prognostic variables …”

Section: Introductionmentioning

confidence: 95%

“…In all studies, multivariable analysis consistently identified lactate dehydrogenase (LDH), PS, albumin level, number of metastatic sites, platelet count, hemoglobin, lymphocyte count, and serum sodium as important prognostic variables. [5][6][7][8][11][12][13][14][15][16][17][18][19][20][21] There is a disconcerting disparity between the racial and ethnic distribution of patients undergoing cancer therapy and those enrolled in clinical trials. There is well-established literature that non-Hispanic White patients account for more than 90% of all cancer clinical trial accruals, [22][23][24][25] while constituting only 59.3% of the US population.…”

Section: Introductionmentioning

confidence: 99%

Outcomes Among Racial and Ethnic Minority Patients With Advanced Cancers in Phase 1 Trials

Goel,

Negassa,

Ghalib

et al. 2024

JAMA Netw Open

View full text Add to dashboard Cite

ImportancePatients from racial and ethnic minority groups (eg, Asian, Hispanic, and non-Hispanic Black patients) have low representation in clinical trials, especially in phase 1 trials in cancer. These trials represent valuable options for patients with advanced cancer who experience disease progression with standard therapy.ObjectiveTo determine whether the benefit of enrollment to phase 1 cancer trials extends to Asian, Hispanic, and non-Hispanic Black patients as much as it does for non-Hispanic White patients.Data SourcesPatient records at a single institution from January 1999 to December 2016 were reviewed. Treatment-related responses, toxic effects, and deaths were recorded.Study SelectionAll phase 1 studies were included.Data Extraction and SynthesisData underwent independent extraction by multiple observers following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline.Main Outcomes and MeasuresThe primary outcome was overall survival (OS), assessed using univariate and multivariable time-to-event analyses.ResultsA total of 738 patients (median [range], 60 [22-93] years; 467 [63.3] female) including 197 Hispanic patients (26.7%), 238 non-Hispanic Black patients (32.2%), and 282 non-Hispanic White patients (38.2%), were enrolled in 64 phase 1 trials, including 33 cytotoxic trials (51.5%), 21 biologic trials (32.8%), and 10 combined therapy trials (15.6%). The primary cancer diagnoses were colorectal (187 patients [25.3%]), ovarian (141 patients [19.1%]), lung (58 patients [7.9%]), uterine (49 patients [6.6%]), and breast (41 patients [5.6%]). Patients underwent a median (range) of 3 (0-13) therapies prior to trial enrollment. Among 558 patients evaluated for response, the clinical benefit rate (ie, stable disease plus response rates) was 49.1%, and the overall response rate was 6.5%. Grade 3 or 4 nonhematological toxic effects were observed in 27.8% (95% CI, 24.6%-31.3%) of patients and grade 3 or 4 hematological toxic effects were observed in 19.7% (95% CI, 17.0%-22.8%) of patients. The treatment-related mortality rate was 0.9% (95% CI, 0.4%-1.9%). Median OS was 9.6 (95% CI, 8.2-11.0) months among Hispanic patients, 8.3 (95% CI, 6.7-10.4) months among non-Hispanic Black patients, and 9.8 (95% CI, 8.5-11.4) months among non-Hispanic White patients (P = .13). In a multivariable analysis, age older than 60 years, Eastern Cooperative Oncology Group performance status score of 2 or greater, more than 2 metastatic sites, lactate dehydrogenase grade 1 or 2, grade 2 or greater low albumin, grade 1 or greater total bilirubin, and grade 2 or greater anemia were associated with worse prognosis, whereas leukocytosis greater than grade 1 was associated with better OS.Conclusions and RelevanceIn this meta-analysis assessing outcomes in phase 1 cancer trials among patients from racial and ethnic minority groups, Hispanic and non-Hispanic Black patients had benefits similar to those of non-Hispanic White patients.

show abstract

“…Studies have classified the prominent clinicopathological factors into multiple categories based on their features [ 6 , 7 ]. Clinicopathological factors including the baseline characteristics, tumor burden, and tumor markers, which can be affected or associated with short- and long-term survival outcomes in colorectal cancer, are considered to be prognostic factors [ 8 , 9 , 10 ]. Baseline characteristics of patients with cancer include the age at which cancer was diagnosed, sex, and comorbidity status.…”

Section: Introductionmentioning

confidence: 99%

“…Studies have particularly reported the effect of the patient’s baseline characteristics, tumor burden, and molecular markers on progressive disease in cancer [ 8 , 9 , 10 ]. However, the effect of baseline characteristics and tumor burden on the expression of tumor markers (such as vaspin) and progressive disease has seldom been discussed.…”

Section: Introductionmentioning

confidence: 99%

Effect of Baseline Characteristics and Tumor Burden on Vaspin Expression and Progressive Disease in Operable Colorectal Cancer

et al. 2020

View full text Add to dashboard Cite

Colorectal cancer is a highly heterogeneous malignancy in the Asian population, and it is considered an important prognostic factor for baseline characteristics, tumor burden, and tumor markers. This study investigated the effect of baseline characteristics and tumor burden on tumor marker expression and progressive disease in colorectal cancer by using partial least squares variance-based path modeling (PLS-PM). PLS-PM can be used to evaluate the complex relationship between prognostic variables and progressive disease status with a small sample of measurements and structural models. A total of 89 tissue samples of colorectal cancer were analyzed. Our results suggested that the expression of visceral adipose tissue-derived serpin (vaspin) is a potential indicator of colorectal cancer progression and may be affected by baseline characteristics such as age, sex, body mass index, and diabetes mellitus. Moreover, according to the characteristics of tumor burden, the expression of vaspin was generally higher in each progressive disease patient. The overall findings suggest that vaspin is a potential indicator of the progressive disease and may be affected by the baseline characteristics of patients.

show abstract

Survival outcome and prognostic model of patients with colorectal cancer on phase 1 trials

Cited by 6 publications

References 32 publications

Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis

Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis

Outcomes Among Racial and Ethnic Minority Patients With Advanced Cancers in Phase 1 Trials

Effect of Baseline Characteristics and Tumor Burden on Vaspin Expression and Progressive Disease in Operable Colorectal Cancer

Contact Info

Product

Resources

About