Dataset Information

Estimation of the Percentage of US Patients With Cancer Who Benefit From Genome-Driven Oncology.

ABSTRACT: Importance:To date, the benefit of genome-driven cancer therapy has not been quantified. Objective:We sought to estimate the annual percentage of patients in the United States with advanced or metastatic cancer who could be eligible for and benefit from US Food and Drug Administration (FDA)-approved genome-driven therapy from 2006 to 2018. Design, Setting, and Participants:Retrospective cross-sectional study using publically available data of (1) demographic characteristics of patients with advanced or metastatic cancer; (2) FDA data on cancer drugs approved from January 2006 through January 2018; (3) measures of response and duration of response from drug labels; and (4) published reports estimating the frequency of various genomic aberrations used to estimate what percentage of patients would have been eligible for and would have benefited from genome-driven therapy during the studied period. Main Outcomes and Measures:Estimated percentage of US patients with cancer eligible for and benefiting from genome-targeted and genome-informed therapy by year, response rate of genome-informed indications, and duration of response. Results:A total of 31 drugs with 38 FDA-approved indications met our inclusion criteria for genome-targeted or genome-informed therapy from January 1, 2006, through January 31, 2018. The estimated number of patients eligible for genome-targeted therapy in 2006 was 28 729 of a total 564 830 patients with metastatic cancer, or 5.09% (95% CI, 5.03%-5.14%). By 2018, this number had increased to 50 811 of 609 640, or 8.33% (95% CI, 8.26%-8.40%). For genome-informed therapy in 2006, the eligible number of patients was 59 301 of 564 830, or 10.50% (95% CI, 10.42%-10.58%). In 2018, genome-informed treatment could be offered to 94 157 of 609 640, or 15.44% (95% CI, 15.35%-15.53%) of patients with metastatic cancer. The percentage of patients with cancer estimated to benefit from genome-targeted therapy in 2006 was 0.70% (95% CI, 0.68%-0.72%), and in 2018, it had increased to 4.90% (95% CI, 4.85%-4.95%). For genome-informed treatment in 2006, the percentage estimated to benefit was 1.31% (95% CI, 1.28%-1.34%), and in 2018, it had increased to 6.62% (95% CI, 6.56%-6.68%). The median overall response rate for all genome-informed drugs through January 2018 was 54%, and the median duration of response was 29.5 months. Conclusions and Relevance:Although the number of patients eligible for genome-driven treatment has increased over time, these drugs have helped a minority of patients with advanced cancer. To accelerate progress in precision oncology, novel trial designs of genomic therapies should be developed, and broad portfolios of drug development, including immunotherapeutic and cytotoxic approaches, should be pursued.

SUBMITTER: Marquart J

PROVIDER: S-EPMC6143048 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ImportanceImmunotherapy checkpoint inhibitors have generated considerable interest because of durable responses in a number of hitherto intractable tumor types.ObjectiveTo estimate the percentage of patients with cancer in the United States who are eligible for and respond to checkpoint inhibitor drugs approved for oncology indications by the US Food and Drug Administration (FDA).Design, setting, and participantsRetrospective cross-sectional study performed from June 2018 through October 2018 using publicly available data to determine (1) demographic characteristics of patients with advanced or metastatic cancer, (2) FDA data on checkpoint inhibitors approved from January 2011 through August 2018, (3) measures of response from drug labels, and (4) published reports estimating the frequency of various inclusion criteria.Main outcomes and measuresThe estimated percentages of US patients with cancer who are eligible for and who respond to immunotherapy checkpoint inhibitor drugs, by year.ResultsSix checkpoint inhibitor drugs were approved for 14 indications between March 25, 2011, and August 17, 2018. The estimated percentage of patients with cancer who were eligible for checkpoint inhibitor drugs increased from 1.54% (95% CI, 1.51%-1.57%) in 2011 to 43.63% (95% CI, 43.51%-43.75%) in 2018. The percentage of patients with cancer estimated to respond to checkpoint inhibitor drugs was 0.14% (95% CI, 0.13%-0.15%) in 2011 when ipilimumab was approved for unresectable or metastatic melanoma and increased to 5.86% (95% CI, 5.80%-5.92%) by 2015. By 2018, the estimated percentage of responders increased to 12.46% (95% CI, 12.37%-12.54%).Conclusions and relevanceThe estimated percentages of patients who are eligible for and who respond to checkpoint inhibitor drugs are higher than reported estimates for drugs approved for genome-driven oncology but remain modest. Future research should explore biomarkers to maximize the benefit of immunotherapy among patients receiving it.

Project description:ImportanceWhen a novel drug is granted accelerated approval, both its on-label and off-label uses must be taken into account.ObjectivesTo estimate the potential upper bound of off-label use of erdafitinib to treat advanced cancer with fibroblast growth factor receptor gene (FGFR) alterations, compare it to the upper bound of on-label use in urothelial cancer, and to review studies that may support off-label use.Design, setting, and participantsThis cross-sectional study used frequency data on FGFR alterations by cancer type and the estimated number of deaths from all cancers for 2019 in the United States. Mortality statistics were used as surrogates for patients with advanced cancer. Analysis was conducted in May 2019.ExposurePercentage of patients with an FGFR2 or FGFR3 alteration.Main outcomes and measuresEstimated number of patients with advanced cancer expressing an FGFR2 or FGFR3 alteration eligible for off-label use of erdafitinib by cancer type; number of studies investigating FGFR-targeting drugs for patients with cancer; and number of ongoing clinical trials on erdafitinib by cancer type.ResultsA total of 15 cancer types had reported FGFR alterations. Of 455 440 estimated patients who died of cancer in 2019, 17 019 (3.7%) were estimated to have FGFR2 or FGFR3 alterations. Of these patients, 12 955 (76.1%) could be eligible for off-label treatment with erdafitinib. A total of 29 completed studies evaluated FGFR-targeting drugs in 11 cancer types, and 10 ongoing studies are studying erdafitinib for different oncological indications.Conclusions and relevanceThis study indicates that the potential for off-label use of FGFR inhibitors such as erdafitinib spans a number of cancer types and a large patient population. Systematic trials exploring off-label uses may be desirable for drugs that target clear, identifiable molecular alterations because this may be more efficient than off-label use in identifying clinical scenarios where the agent has activity.

Project description:PURPOSE:Matching patients to investigational therapies requires new tools to support physician decision making. We designed and implemented Precision Insight Support Engine (PRECISE), an automated, just-in-time, clinical-grade informatics platform to identify and dynamically track patients on the basis of molecular and clinical criteria. Real-world use of this tool was analyzed to determine whether PRECISE facilitated enrollment to early-phase, genome-driven trials. MATERIALS AND METHODS:We analyzed patients who were enrolled in genome-driven, early-phase trials using PRECISE at Memorial Sloan Kettering Cancer Center between April 2014 and January 2018. Primary end point was the proportion of enrolled patients who were successfully identified using PRECISE before enrollment. Secondary end points included time from sequencing and PRECISE identification to enrollment. Reasons for a failure to identify genomically matched patients were also explored. RESULTS:Data were analyzed from 41 therapeutic trials led by 19 principal investigators. In total, 755 patients were accrued to these studies during the period that PRECISE was used. PRECISE successfully identified 327 patients (43%) before enrollment. Patients were diagnosed with 29 tumor types and harbored alterations in 43 oncogenes, most commonly ERBB2 (21.3%), PIK3CA (14.1%), and BRAF (8.7%). Median time from sequencing to enrollment was 163 days (interquartile range, 66 to 357 days), and from PRECISE identification to enrollment 87 days (interquartile range, 37 to 180 days). Common reasons for failing to identify patients before enrollment included accrual on the basis of molecular alterations that did not match pre-established PRECISE genomic eligibility (140 [33%] of 428) and external sequencing not available for parsing (127 [30%] of 428). CONCLUSION:PRECISE identified 43% of all patients accrued to a diverse cohort of early-phase, genome-matched studies. Purpose-built informatics platforms represent a novel and potentially effective method for matching patients to molecularly selected studies.

Project description:The rapid advancement of high-throughput tools for quantitative measurement of proteins has demonstrated the potential for the identification of proteins associated with cancer. However, the quantitative results on cancer tissue specimens are usually confounded by tissue heterogeneity, e.g. regions with cancer usually have significantly higher epithelium content yet lower stromal content.It is therefore necessary to develop a tool to facilitate the interpretation of the results of protein measurements in tissue specimens.Epithelial cell adhesion molecule (EpCAM) and cathepsin L (CTSL) are two epithelial proteins whose expressions in normal and tumorous prostate tissues were confirmed by measuring staining intensity with immunohistochemical staining (IHC). The expressions of these proteins were measured by ELISA in protein extracts from OCT embedded frozen prostate tissues. To eliminate the influence of tissue heterogeneity on epithelial protein quantification measured by ELISA, a color-based segmentation method was developed in-house for estimation of epithelium content using H&E histology slides from the same prostate tissues and the estimated epithelium percentage was used to normalize the ELISA results. The epithelium contents of the same slides were also estimated by a pathologist and used to normalize the ELISA results. The computer based results were compared with the pathologist's reading.We found that both EpCAM and CTSL levels, measured by ELISA assays itself, were greatly affected by epithelium content in the tissue specimens. Without adjusting for epithelium percentage, both EpCAM and CTSL levels appeared significantly higher in tumor tissues than normal tissues with a p value less than 0.001. However, after normalization by the epithelium percentage, ELISA measurements of both EpCAM and CTSL were in agreement with IHC staining results, showing a significant increase only in EpCAM with no difference in CTSL expression in cancer tissues. These results were obtained with normalization by both the computer estimated and pathologist estimated epithelium percentage.Our results show that estimation of tissue epithelium percentage using our color-based segmentation method correlates well with pathologists' estimation of tissue epithelium percentages. The epithelium contents estimated by color-based segmentation may be useful in immuno-based analysis or clinical proteomic analysis of tumor proteins. The codes used for epithelium estimation as well as the micrographs with estimated epithelium content are available online.

Project description:Breast density, which is a measure of the relative amount of fibroglandular tissue within the breast area, is one of the most important breast cancer risk factors. Accurate segmentation of fibroglandular tissues and breast area is crucial for computing the breast density. Semiautomatic and fully automatic computer-aided design tools have been developed to estimate the percentage of breast density in mammograms. However, the available approaches are usually limited to specific mammogram views and are inadequate for complete delineation of the pectoral muscle. These tools also perform poorly in cases of data variability and often require an experienced radiologist to adjust the segmentation threshold for fibroglandular tissue within the breast area. This study proposes a new deep learning architecture that automatically estimates the area-based breast percentage density from mammograms using a weight-adaptive multitask learning approach. The proposed approach simultaneously segments the breast and dense tissues and further estimates the breast percentage density. We evaluate the performance of the proposed model in both segmentation and density estimation on an independent evaluation set of 7500 craniocaudal and mediolateral oblique-view mammograms from Kuopio University Hospital, Finland. The proposed multitask segmentation approach outperforms and achieves average relative improvements of 2.88% and 9.78% in terms of F-score compared to the multitask U-net and a fully convolutional neural network, respectively. The estimated breast density values using our approach strongly correlate with radiologists' assessments with a Pearson's correlation of [Formula: see text] (95% confidence interval [0.89, 0.91]). We conclude that our approach greatly improves the segmentation accuracy of the breast area and dense tissues; thus, it can play a vital role in accurately computing the breast density. Our density estimation model considerably reduces the time and effort needed to estimate density values from mammograms by radiologists and therefore, decreases inter- and intra-reader variability.

Dataset Information

Estimation of the Percentage of US Patients With Cancer Who Benefit From Genome-Driven Oncology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets