The Belgian Molecular Profiling Program of Metastatic Cancer for Clinical Decision and Treatment Assignment

2019 ◽  
Author(s):  
Keyword(s):  
Metastatic Cancer ◽  
Clinical Decision ◽  
Molecular Profiling ◽  
Treatment Assignment

scholarly journals Contributions on Clinical Decision Support from the 2018 Literature

2019 ◽  
Vol 28 (01) ◽  
pp. 135-137 ◽  
Author(s):  
Vassilis Koutkias ◽  
Jacques Bouaud ◽  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Care Delivery ◽  
Metastatic Cancer ◽  
Clinical Decision ◽  
Data Driven ◽  
Editorial Team ◽  
Cancer Center ◽  
Bibliographic Databases ◽  
Free Text

Objectives: To summarize recent research and select the best papers published in 2018 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook. Methods: A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation. Results: Among 1,148 retrieved articles, 15 best paper candidates were selected, the review of which resulted in the selection of four best papers. The first paper introduces a deep learning model for estimating short-term life expectancy (>3 months) of metastatic cancer patients by analyzing free-text clinical notes in electronic medical records, while maintaining the temporal visit sequence. The second paper takes note that CDSSs become routinely integrated in health information systems and compares statistical anomaly detection models to identify CDSS malfunctions which, if remain unnoticed, may have a negative impact on care delivery. The third paper fairly reports on lessons learnt from the development of an oncology CDSS using artificial intelligence techniques and from its assessment in a large US cancer center. The fourth paper implements a preference learning methodology for detecting inconsistencies in clinical practice guidelines and illustrates the applicability of the proposed methodology to antibiotherapy. Conclusions: Three of the four best papers rely on data-driven methods, and one builds on a knowledge-based approach. While there is currently a trend for data-driven decision support, the promising results of such approaches still need to be confirmed by the adoption of these systems and their routine use.

Treatment According to a Comprehensive Molecular Profiling Can Lead to a Better Outcome in Heavily Pretreated Patients With Metastatic Cancer

2019 ◽  
Vol 25 (2) ◽  
pp. 73-79 ◽  
Author(s):  
Andreas Seeber ◽  
Georges Chahine ◽  
Fadi Nasr ◽  
Andrew Dean ◽  
Mira Miranova ◽  
...  
Keyword(s):  
Metastatic Cancer ◽  
Molecular Profiling ◽  
Heavily Pretreated ◽  
Pretreated Patients

Princess Margaret Cancer Centre (PMCC) Integrated Molecular Profiling in Advanced Cancers Trial (IMPACT) using genotyping and targeted next-generation sequencing (NGS).

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 11002-11002 ◽  
Author(s):  
Philippe L. Bedard ◽  
Amit M. Oza ◽  
Ming-Sound Tsao ◽  
Natasha B. Leighl ◽  
Frances A. Shepherd ◽  
...  
Keyword(s):  
Targeted Therapies ◽  
Clinical Decision Making ◽  
Clinical Laboratory ◽  
Screening Program ◽  
Illumina Miseq ◽  
Response Assessment ◽  
Clinical Decision ◽  
Molecular Profiling ◽  
Routine Care ◽  
Targeted Next Generation Sequencing

11002 Background: IMPACT is an institution-wide screening program to identify patients (pts) treated at PMCC with somatic alterations that can be matched to targeted therapies. Methods: Pts with advanced breast, colorectal (CRC), non-small cell lung (NSCLC), ovarian cancers and selected other solid tumors treated at PMCC were eligible. Tumor DNA was isolated from a FFPE archived sample and genotyped using a customized Sequenom panel (23 genes, 280 mutations) in a CLIA-certified laboratory. Verified mutations were reported in pts electronic health records. Selected FFPE samples were further characterized by NGS with the Illumina MiSeq TruSeq Amplicon Cancer Panel (48 genes, 212 amplicons, ≥500x coverage) for platform validation. Results: From Mar 1/12-Jan 10/13, 485 pts were enrolled with median 1 prior treatment for advanced disease (range 0-6). Of 33 (7%) screen failures, 5% were for insufficient tissue and 2% for clinical deterioration. Median DNA quantity from FFPE = 4250ng (range 15-32550ng). The median time from tissue receipt to reporting was 5 weeks (range 1-23). Mutations were identified by Sequenom in 137/349 (39%) pts, including 24/79 (30%) breast, 40/80 (50%) CRC, 54/88 (61%) NSCLC, 17/78 (22%) ovarian, and 2/24 (8%) other cancers. Mutations detected were: 76 KRAS, 35 PIK3CA, 22 EGFR, 5 NRAS, 5 ERBB2, 5 CTNNB1, 4 BRAF, and 1 AKT1. MiSeq was concordant with Sequenom in 112/113 (99%) pts, with mutations identified in 94/114 (82%). The average number of mutations detected by MiSeq was 1.72/pt (range 0-7) compared with 0.49/pt by Sequenom (range 0-2). After a median follow up of 5.0 months, 31/137 (23%) pts with mutations have been matched to targeted therapies, including 14 pts enrolled in clinical trials (15 trials) matched to their genotype. Of the 10 trial pts with at least one response assessment, 3 PR (1 confirmed) and 2 SD ≥ 24 weeks have been observed. Conclusions: Molecular profiling can be integrated into the routine care of advanced cancer pts. Genotyping and targeted NGS are feasible in a clinical laboratory using stored archival FFPE tumor samples. NGS identifies additional actionable mutations to inform clinical-decision making. Clinical trial information: NCT01505400.

A community-based program for personalized cancer care using next-generation sequencing (NGS).

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 11102-11102
Author(s):  
Shile Liang ◽  
Pranil Chandra ◽  
Zeqiang Ma ◽  
Debbie Haynes ◽  
James Prescott ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Cancer Patients ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Molecular Profiling ◽  
Targeted Agents ◽  
Community Based ◽  
Off Label ◽  
Molecular Abnormalities ◽  
Tumor Types

11102 Background: Despite growing interest and need, molecular profiling of tumor samples is largely unavailable in community cancer centers, where nearly 80% of cancer patients (pts) are treated. In 10/12, Sarah Cannon Research Institute (SCRI) launched a community-based molecular profiling program to: 1) better understand the molecular constituency of cancer patients, 2) identify appropriate pts for phase I and II clinical trials of targeted agents, and 3) identify pts with molecular abnormalities responsive to FDA-approved agents. Methods: Eligible pts consented to testing of available biospecimens, which were interrogated for alterations in 35 cancer-related genes using NGS (1000X average coverage) in a CLIA/CAP laboratory. Results were reported to the treating physician within 14 days and stored in a database to enable correlation with clinical outcomes. Results: As of 1/13, 209 pts had been enrolled with 84% having sufficient material for assay. At least 1 mutation was detected in 46% of tumors. Results in the 3 most commonly assayed tumor types are summarized (Table). Mutations for which there are FDA-approved targeted agents were found in 14 off-label tumors (EGFR 4, KIT 3, SMO 3, BRAF 2, HER2 2). 40 pts (27%) were subsequently enrolled in clinical trials; in 19 of these, assay results influenced clinical trial selection. Conclusions: This program provides molecular profiling data to community oncologists for clinical decision making. Experience to date indicates this information can be provided in a timely manner for incorporation into clinical practice. Profiling results will enable: 1) selection of pts with appropriate tumor targets for investigational targeted agents, 2) enhanced study enrollment, 3) evaluation of FDA approved targeted agents in off-label tumor types, and 4) correlation of treatment outcomes with patterns of tumor molecular abnormalities. [Table: see text]

Analysis of 1261 metastatic cancer patients evaluated by comprehensive molecular profiling (CMP) including next-gen sequencing (NGS) from a single institution.

2016 ◽  
Vol 34 (15_suppl) ◽  
pp. 11570-11570 ◽  
Author(s):  
Jason Claud Chandler ◽  
Ari M. Vanderwalde ◽  
Bradley G. Somer ◽  
Gregory A. Vidal ◽  
Lee Steven Schwartzberg
Keyword(s):  
Cancer Patients ◽  
Metastatic Cancer ◽  
Molecular Profiling ◽  
Single Institution ◽  
Next Gen Sequencing

scholarly journals Metastatic Cancer of Cowper's Gland: A Rare Cancer Managed Successfully by Molecular Profiling

2014 ◽  
Vol 7 (1) ◽  
pp. 52-57 ◽  
Author(s):  
Charles E. Myers ◽  
Zoran Gatalica ◽  
Anthony Spinelli ◽  
Michael Castro ◽  
Erica Linden ◽  
...  
Keyword(s):  
Metastatic Cancer ◽  
Molecular Profiling ◽  
Rare Cancer

scholarly journals Precision cancer therapy by molecular profiling – A Theoretical Study

2021 ◽  
Author(s):  
V. Sah
Keyword(s):  
Large Scale ◽  
Clinical Decision Making ◽  
Single Cell Analysis ◽  
Clinical Decision ◽  
Molecular Profiling ◽  
Outcome Analysis ◽  
Precision Oncology ◽  
Liquid Biopsies ◽  
Next Generation Sequencing Technology ◽  
Genomic Studies

The amount of druggable tumor-specific molecular aberrations has increased significantly over the last decade, with biomarker-matched therapies demonstrating a major survival advantage in many cancer forms. Therefore, molecular pathology has been critical not just for tumor detection and prognosis, but also for clinical decision-making in everyday practice. The advent of next-generation sequencing technology and the proliferation of large-scale tumor molecular profiling services through universities worldwide have transformed the area of precision oncology. When systematic genomic studies become more accessible in clinical and laboratory environments, healthcare professionals face the difficult challenge of outcome analysis and translation. This study summarizes existing and future methods to implementing precision cancer medicine, outlining the obstacles and possible strategies for facilitating the understanding and maximization of molecular profiling findings. Beyond tumor DNA sequencing, we discuss innovative molecular characterization techniques such as transcriptomics, immunophenotyping, epigenetic profiling, and single-cell analysis. Additionally, we discuss present and future uses of liquid biopsies for evaluating blood-based biomarkers such as circulating tumor cells and nucleic acids. Finally, the shortcomings of genotype-based treatments give insight into opportunities to extend personalized medicine beyond genomics.

scholarly journals Best Paper Selection

2019 ◽  
Vol 28 (01) ◽  
pp. 138-139
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Decision Support ◽  
Cancer Patients ◽  
Clinical Decision Support ◽  
Metastatic Cancer ◽  
Clinical Decision ◽  
Preference Learning ◽  
Free Text ◽  
Statistical Anomaly Detection

Banerjee I, Gensheimer MF, Wood DJ, Henry S, Aggarwal S, Chang DT, Rubin DL. Probabilistic prognostic estimates of survival in metastatic cancer patients (PPES-Met) utilizing free-text clinical narratives. Sci Rep 2018 Jul 3;8(1):10037 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030075/ Ray S, McEvoy DS, Aaron S, Hickman TT, Wright A. Using statistical anomaly detection models to find clinical decision support malfunctions. J Am Med Inform Assoc 2018 Jul 1;25(7):862-71 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6016695/ Simon G, DiNardo CD, Takahashi K, Cascone T, Powers C, Stevens R, Allen J, Antonoff MB, Gomez D, Keane P, Suarez Saiz F, Nguyen Q, Roarty E, Pierce S, Zhang J, Hardeman Barnhill E, Lakhani K, Shaw K, Smith B, Swisher S, High R, Futreal PA, Heymach, Chin L. Applying Artificial Intelligence to address the knowledge gaps in cancer care. Oncologist 2018 Nov 16 pii: theoncologist.2018-0257 http://theoncologist.alphamedpress.org/content/24/6/772.long Tsopra R, Lamy JB, Sedki K. Using preference learning for detecting inconsistencies in clinical practice guidelines: methods and application to antibiotherapy. Artif Intell Med 2018 Jul;89:24-33 https://www.sciencedirect.com/science/article/pii/S0933365718300873?via%3Dihub

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