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Estimation of Solid Tumor Doubling Times from Progression-Free Survival Plots Using a Novel Statistical Approach.

ABSTRACT: Tumor doubling time can significantly affect the outcome of anticancer therapy, but it is very challenging to determine. Here, we present a statistical approach that extracts doubling times from progression-free survival (PFS) plots, which inherently contains information regarding the growth of solid tumors. Twelve cancers were investigated and multiple PFS plots were evaluated for each type. The PFS plot showing fastest tumor growth was deemed to best represent the inherent growth kinetics of the solid tumor, and selected for further analysis. The exponential tumor growth rates were extracted from each PFS plot, along with associated variabilities, which ultimately allowed for the estimation of solid tumor doubling times. The mean simulated doubling times for pancreatic cancer, melanoma, hepatocellular carcinoma (HCC), renal cell carcinoma, triple negative breast cancer, non-small cell lung cancer, hormone receptor positive (HR+) breast cancer, human epidermal growth factor receptor-2 positive (HER-2+) breast cancer, gastric cancer, glioblastoma multiforme, colorectal cancer, and prostate cancer were 5.06, 3.78, 3.06, 2.67, 2.38, 2.40, 4.31, 4.12, and 3.84 months, respectively. For all cancers, clinically reported doubling times were within the estimated ranges. For all cancers, except HCC, the growth rates were best characterized by a log-normal distribution. For HCC, the gamma distribution best described the data. The statistical approach presented here provides a qualified method for extracting tumor growth rates and doubling times from PFS plots. It also allows estimation of the distributional characteristics for tumor growth rates and doubling times in a given patient population.

SUBMITTER: Kay K

PROVIDER: S-EPMC8383152 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeTo determine whether a machine learning approach optimizes survival estimation for patients with symptomatic bone metastases (SBM), we developed the Bone Metastases Ensemble Trees for Survival (BMETS) to predict survival using 27 prognostic covariates. To establish its relative clinical utility, we compared BMETS with 2 simpler Cox regression models used in this setting.Methods and materialsFor 492 bone sites in 397 patients evaluated for palliative radiation therapy (RT) for SBM from January 2007 to January 2013, data for 27 clinical variables were collected. These covariates and the primary outcome of time from consultation to death were used to build BMETS using random survival forests. We then performed Cox regressions as per 2 validated models: Chow's 3-item (C-3) and Westhoff's 2-item (W-2) tools. Model performance was assessed using cross-validation procedures and measured by time-dependent area under the curve (tAUC) for all 3 models. For temporal validation, a separate data set comprised of 104 bone sites treated in 85 patients in 2018 was used to estimate tAUC from BMETS.ResultsMedian survival was 6.4 months. Variable importance was greatest for performance status, blood cell counts, recent systemic therapy type, and receipt of concurrent nonbone palliative RT. tAUC at 3, 6, and 12 months was 0.83, 0.81, and 0.81, respectively, suggesting excellent discrimination of BMETS across postconsultation time points. BMETS outperformed simpler models at each time, with respective tAUC at each time of 0.78, 0.76, and 0.74 for the C-3 model and 0.80, 0.78, and 0.77 for the W-2 model. For the temporal validation set, respective tAUC was similarly high at 0.86, 0.82, and 0.78.ConclusionsFor patients with SBM, BMETS improved survival predictions versus simpler traditional models. Model performance was maintained when applied to a temporal validation set. To facilitate clinical use, we developed a web platform for data entry and display of BMETS-predicted survival probabilities.

Project description:In Chronic Myeloid Leukemia (CML), successful treatment requires accurate molecular monitoring to evaluate disease response and provide timely interventions for patients failing to achieve the desired outcomes. We wanted to determine whether measuring BCR-ABL1 mRNA doubling-times (DTs) could distinguish inconsequential rises in the oncogene's expression from resistance to tyrosine kinase inhibitors (TKIs). Thus, we retrospectively examined BCR-ABL1 evolution in 305 chronic-phase CML patients receiving imatinib mesylate (IM) as a first line treatment. Patients were subdivided in two groups: those with a confirmed rise in BCR-ABL1 transcripts without MR3.0 loss and those failing IM. We found that the DTs of the former patients were significantly longer than those of patients developing IM resistance (57.80 vs. 41.45 days, p = 0.0114). Interestingly, the DT values of individuals failing second-generation (2G) TKIs after developing IM resistance were considerably shorter than those observed at the time of IM failure (27.20 vs. 41.45 days; p = 0.0035). We next wanted to establish if decreases in BCR-ABL1 transcripts would identify subjects likely to obtain deep molecular responses. We therefore analyzed the BCR-ABL1 halving-times (HTs) of a different cohort comprising 174 individuals receiving IM in first line and observed that, regardless of the time point selected for our analyses (6, 12, or 18 months), HTs were significantly shorter in subjects achieving superior molecular responses (p = 0.002 at 6 months; p < 0.001 at 12 months; p = 0.0099 at 18 months). Moreover, 50 patients receiving 2G TKIs as first line therapy and obtaining an MR3.0 (after 6 months; p = 0.003) or an MR4.0 (after 12 months; p = 0.019) displayed significantly shorter HTs than individuals lacking these molecular responses. Our findings suggest that BCR-ABL1 DTs and HTs are reliable tools to, respectively, identify subjects in MR3.0 that are failing their assigned TKI or to recognize patients likely to achieve deep molecular responses that should be considered for treatment discontinuation.

Project description:BackgroundSurvival or outcome information is important for clinical routine as well as for clinical research and should be collected completely, timely and precisely. This information is relevant for multiple usages including quality control, clinical trials, observational studies and epidemiological registries. However, the local hospital information system (HIS) does not support this documentation and therefore this data has to generated by paper based or spreadsheet methods which can result in redundantly documented data. Therefore we investigated, whether integrating the follow-up documentation of different departments in the HIS and reusing it for survival analysis can enable the physician to obtain survival curves in a timely manner and to avoid redundant documentation.MethodsWe analysed the current follow-up process of oncological patients in two departments (urology, haematology) with respect to different documentation forms. We developed a concept for comprehensive survival documentation based on a generic data model and implemented a follow-up form within the HIS of the University Hospital Muenster which is suitable for a secondary use of these data. We designed a query to extract the relevant data from the HIS and implemented Kaplan-Meier plots based on these data. To re-use this data sufficient data quality is needed. We measured completeness of forms with respect to all tumour cases in the clinic and completeness of documented items per form as incomplete information can bias results of the survival analysis.ResultsBased on the form analysis we discovered differences and concordances between both departments. We identified 52 attributes from which 13 were common (e.g. procedures and diagnosis dates) and were used for the generic data model. The electronic follow-up form was integrated in the clinical workflow. Survival data was also retrospectively entered in order to perform survival and quality analyses on a comprehensive data set. Physicians are now able to generate timely Kaplan-Meier plots on current data. We analysed 1029 follow-up forms of 965 patients with survival information between 1992 and 2010. Completeness of forms was 60.2%, completeness of items ranges between 94.3% and 98.5%. Median overall survival time was 16.4 years; median event-free survival time was 7.7 years.ConclusionIt is feasible to integrate survival information into routine HIS documentation such that Kaplan-Meier plots can be generated directly and in a timely manner.

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Estimation of Solid Tumor Doubling Times from Progression-Free Survival Plots Using a Novel Statistical Approach.

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