Development and validation of a real-world clinicogenomic database.

Singal, Gaurav; Miller, Peter G.; Agarwala, Vineeta; He, Jie; Gossai, Anala; Frank, Shannon; Bourque, David; Bowser, Bryan; Caron, Thomas; Baydur, Ezra; Seidl‐Rathkopf, Kathi; Ivanov, Ivan N.; Parker, Alex; Guria, Ameet; Frampton, Garrett M.; Jaskiw, Ann; Feuchtbaum, Dana; Nussbaum, Nathan; Abernethy, Amy Pickar; Miller, Vincent A.

doi:10.1200/jco.2017.35.15_suppl.2514

Cited by 10 publications

(15 citation statements)

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“…We consider the Clinico-Genomic Database offered jointly by Flatiron Health and Foundation Medicine (FH-FMI CGDB) 26 . The CGDB is a US-based de-identified oncology database that combines real-world, patient-level clinical data and outcomes with patient-level genomic data to provide a comprehensive patient and tumor profile.…”

Section: Methodsmentioning

confidence: 99%

Penalized regression for left-truncated and right-censored survival data

McGough¹,

Incerti²,

Lyalina³

et al. 2021

Preprint

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High-dimensional data are becoming increasingly common in the medical field as large volumes of patient information are collected and processed by high-throughput screening, electronic health records (EHRs), and comprehensive genomic testing. Statistical models that attempt to study the effects of many predictors on survival typically implement feature selection or penalized methods to mitigate the undesirable consequences of overfitting. In some cases survival data is also left-truncated which can give rise to an immortal time bias, but penalized survival methods that adjust for left truncation are not commonly implemented. To address these challenges, we apply a penalized Cox proportional hazards model for left-truncated and right-censored survival data and assess implications of left truncation adjustment on bias and interpretation. We use simulation studies and a high-dimensional, real-world clinico-genomic database (CGDB) to highlight the pitfalls of failing to account for left truncation in survival modeling.

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Section: Methodsmentioning

confidence: 99%

Penalized regression for left-truncated and right-censored survival data

McGough¹,

Incerti²,

Lyalina³

et al. 2021

Preprint

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“…42 These patients from the Flatiron Health EMR database had also undergone comprehensive genomic profiling by Foundation Medicine. 43,44 The population in this study consisted of mNSCLC patients diagnosed in the Flatiron Health network (250 community and academic oncology practices), representing 1.5 million active patients with cancer across the United States between January 2011 and December 2016. Flatiron's technology-assisted data abstraction and the nationally distributed population enabled the RWD sample for the analysis.…”

Section: Methodsmentioning

confidence: 99%

Clinical Characteristics, Treatments, and Concurrent Mutations in Non–Small Cell Lung Cancer Patients With NF1 Mutations

Bowman

Tiu

Smyth

et al. 2021

Clinical Lung Cancer

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Limited real-world data available on concurrent mutations, treatment patterns, and health outcomes in patients with metastatic nonesmall cell lung cancer (mNSCLC). We undertook a retrospective data analysis in patients with mNSCLC treated in Flatiron Health network between 2011 and 2016. Overall, 103 patients had NF1 mutation, with KRAS oncogene homolog (16.5%) and EGFR fusion (6.8%) being the most frequent concurrent mutations. Patients with NF1 mutations may have different progression rates, and survival than other mutations, administered less systemic therapy after first line and may have shorter survival. Objectives: Metastatic nonesmall cell lung cancer (mNSCLC) is characterized by complex genomic alterations. NF1 mutations may confer distinct clinical characteristics within NSCLC, and real-world evidence on concurrent mutations, treatment patterns, and health outcomes is lacking. Materials and Methods: This retrospective study was performed in patients with mNSCLC treated in the Flatiron Health network who underwent the FoundationOne tumor-sequencing. Anticancer therapies, concurrent mutations, real-world progression-free survival (rwPFS), and overall survival (OS) were assessed. Results: Of the 1663 patients, 103 patients were identified with NF1 mutation. Concurrent mutations with Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (16.5%) and epidermal growth factor receptor fusion (6.8%) were the most frequent. In patients with NF1 mutation only (n ¼ 57), 42% were women, 86% patients had smoking history, and 70% had non-squamous cell carcinoma type. Most (51%) of the patients with NF1 mutations received a single line of therapy versus other mutations and the overall treated population (44%). Platinum-based chemotherapy was the predominant first-line therapy, with programmed cell death-1/programmed cell death-ligand-1 inhibitors as subsequent lines of therapy. The NF1 mutation only group had numerically the shortest median rwPFS (82 days) than other mutation groups. Median OS for the NF1 mutation group in first, second, and third lines of therapy was 321, 498, and 210 days, respectively. Conclusions: NF1 mutations confer distinct clinical characteristics in patients with mNSCLC. These patients may have different trajectories for progression and survival than seen for other mutations, experience less systemic therapy after first-line therapy, and may have shorter survival.

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“…We consider the CGDB offered jointly by Flatiron Health and Foundation Medicine. 26 The CGDB is a US-based deidentified oncology database that combines real-world, patient-level clinical data and outcomes with patient-level genomic data to provide a comprehensive patient and tumor profile. 27,28 To date, over 400 000 samples have been sequenced from patients across the US.…”

Section: Real-world Use Casementioning

confidence: 99%

Penalized regression for left‐truncated and right‐censored survival data

McGough¹,

Incerti²,

Lyalina³

et al. 2021

Statistics in Medicine

View full text Add to dashboard Cite

High-dimensional data are becoming increasingly common in the medical field as large volumes of patient information are collected and processed by high-throughput screening, electronic health records, and comprehensive genomic testing. Statistical models that attempt to study the effects of many predictors on survival typically implement feature selection or penalized methods to mitigate the undesirable consequences of overfitting. In some cases survival data are also left-truncated which can give rise to an immortal time bias, but penalized survival methods that adjust for left truncation are not commonly implemented. To address these challenges, we apply a penalized Cox proportional hazards model for left-truncated and right-censored survival data and assess implications of left truncation adjustment on bias and interpretation.We use simulation studies and a high-dimensional, real-world clinico-genomic database to highlight the pitfalls of failing to account for left truncation in survival modeling.

show abstract

Development and validation of a real-world clinicogenomic database.

Cited by 10 publications

References 0 publications

Penalized regression for left-truncated and right-censored survival data

Penalized regression for left-truncated and right-censored survival data

Clinical Characteristics, Treatments, and Concurrent Mutations in Non–Small Cell Lung Cancer Patients With NF1 Mutations

Penalized regression for left‐truncated and right‐censored survival data

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