Genomic Analysis of Breast Cancer Heralds a Changing Treatment Paradigm

2014 ◽  
Vol 12 (5S) ◽  
pp. 750-752
Author(s):  
Matthew Ellis
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Molecular Biology ◽  
Trial Design ◽  
Molecular Subtypes ◽  
Clinical Trial Design ◽  
Genomic Analysis ◽  
Driver Mutations ◽  
Annual Conference ◽  
Treatment Paradigm

Deep genomic analysis in breast cancer and the identification of driver mutations will result in treatments based on molecular subtypes and pathways. Mutations not yet familiar to most oncologists will become part of the clinical oncology vernacular. Such discoveries will advance the concept of “biology first, not drug first,” because molecular biology will drive drug development and clinical trial design involving small, molecularly defined subsets of patients, according to a presentation at the NCCN 19th Annual Conference.

scholarly journals Circulating Tumor DNA Reflects Uveal Melanoma Responses to Protein Kinase C Inhibition

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1740
Author(s):  
John J. Park ◽  
Russell J. Diefenbach ◽  
Natalie Byrne ◽  
Georgina V. Long ◽  
Richard A. Scolyer ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Benefit ◽  
Trial Design ◽  
Phase 1 ◽  
Clinical Trial Design ◽  
Roc Curves ◽  
Circulating Tumor Dna ◽  
Resistance Mechanisms ◽  
Driver Mutations ◽  
Targeted Next Generation Sequencing

The prognosis for patients with UM is poor, and recent clinical trials have failed to prolong overall survival (OS) of these patients. Over 95% of UM harbor activating driver mutations, and this allows for the investigation of ctDNA. In this study, we investigated the value of ctDNA for adaptive clinical trial design in metastatic UM. Longitudinal plasma samples were analyzed for ctDNA in 17 metastatic UM patients treated with PKCi-based therapy in a phase 1 clinical trial setting. Plasma ctDNA was assessed using digital droplet PCR (ddPCR) and a custom melanoma gene panel for targeted next generation sequencing (NGS). Baseline ctDNA strongly correlated with baseline lactate dehydrogenase (LDH) (p < 0.001) and baseline disease burden (p = 0.002). Early during treatment (EDT) ctDNA accurately predicted patients with clinical benefit to PKCi using receiver operator characteristic (ROC) curves (AUC 0.84, [95% confidence interval 0.65–1.0, p = 0.026]). Longitudinal ctDNA assessment was informative for establishing clinical benefit and detecting disease progression with 7/8 (88%) of patients showing a rise in ctDNA and targeted NGS of ctDNA revealed putative resistance mechanisms prior to radiological progression. The inclusion of longitudinal ctDNA monitoring in metastatic UM can advance adaptive clinical trial design.

An Empirical Investigation of Bayesian Clinical Trial Design in Metastatic Breast Cancer

2019 ◽  
Vol 54 (4) ◽  
pp. 861-869
Author(s):  
Erik Bloomquist ◽  
Susan Jin ◽  
Jiaxi Zhou ◽  
Shenghui Tang ◽  
Rajeshwari Sridhara
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Metastatic Breast Cancer ◽  
Trial Design ◽  
Empirical Investigation ◽  
Metastatic Breast ◽  
Clinical Trial Design

Challenges in breast cancer clinical trial design in the postgenomic era

2004 ◽  
Vol 16 (6) ◽  
pp. 536-541 ◽  
Author(s):  
Sherene Loi ◽  
Marc Buyse ◽  
Christos Sotiriou ◽  
Fatima Cardoso
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Cancer Clinical Trial

scholarly journals Review of Statistical Methods for Biomarker-Driven Clinical Trials

2019 ◽  
pp. 1-9
Author(s):  
Richard Simon
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Statistical Methods ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Adaptive Methods ◽  
Phase Iii ◽  
Driver Mutations ◽  
New Methods ◽  
Targeted Treatments

The discovery of somatic driver mutations in kinases and receptors has stimulated the development of molecularly targeted treatments that require companion diagnostics and new approaches to clinical development. This article reviews some of the clinical trial designs that have been developed to address these opportunities, including phase II basket and platform trials as well as phase III enrichment and biomarker adaptive designs. It also re-examines some of the conventional wisdom that previously dominated clinical trial design and discusses development and internal validation of a predictive biomarker as a new paradigm for optimizing the intended-use subset for a treatment. Statistical methods now being used in adaptive biomarker-driven clinical trials are reviewed. Some previous paradigms for clinical trial design can limit the development of more effective methods on the basis of prospectively planned adaptive methods, but useful new methods have been developed for analysis of genome-wide data and for the design of adaptively enriched studies. In many cases, the heterogeneity of populations eligible for clinical trials as traditionally defined makes it unlikely that molecularly targeted treatments will be effective for a majority of the eligible patients. New methods for dealing with patient heterogeneity in therapeutic response should be used in the design of phase III clinical trials.

Integrating radiomics into clinical trial design

2019 ◽  
Vol 63 (4) ◽  
Author(s):  
Jessica J. Waninger ◽  
Michael D. Green ◽  
Catherine Cheze Le Rest ◽  
Benjamin Rosen ◽  
Issam El Naqa
Keyword(s):  
Clinical Trial ◽  
Trial Design ◽  
Clinical Trial Design
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