Implications of Selection Bias Due to Delayed Study Entry in Clinical Genomic Studies

Brown, Samantha; Lavery, Jessica A.; Shen, Ronglai; Martin, Axel; Kehl, Kenneth L.; Sweeney, Shawn M.; Lepisto, Eva M.; Rizvi, Hira; McCarthy, Caroline G.; Schultz, Nikolaus; Warner, Jeremy L.; Park, Ben Ho; Bédard, Philippe L.; Riely, Gregory J.; Schrag, Deborah; Panageas, Katherine S.

doi:10.1001/jamaoncol.2021.5153

Cited by 30 publications

(18 citation statements)

References 24 publications

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“…Overall survival (OS) was calculated from start of treatment to death from any cause, and patients with no record of mortality were right censored at the date of last clinic visit or structured activity. Because patients cannot enter the database until a CGP report is delivered, OS risk intervals were left truncated to the date of CGP report to account for immortal time (23,24). Flatiron Health database mortality information is a composite derived from 3 sources: documents within the EHR, Social Security Death Index, and a commercial death dataset mining data from obituaries and funeral homes.…”

Section: Discussionmentioning

confidence: 99%

Real-world Validation of TMB and Microsatellite Instability as Predictive Biomarkers of Immune Checkpoint Inhibitor Effectiveness in Advanced Gastroesophageal Cancer

Graf

Fisher

Creeden

et al. 2022

Cancer Research Communications

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Patients with advanced gastroesophageal cancer (mEG) and tumor mutational burden ≥ 10 mut/mb (TMB≥10) have more favorable outcomes on immune checkpoint inhibitor (ICPI) monotherapy compared to chemotherapy in subgroup analyses of randomized controlled trials. We sought to evaluate the robustness of these associations in real-world settings where patients and practices are more diverse. 362 2L and 692 1L patients respectively received ICPI (n = 99, 33) or chemotherapy (n = 263, 659) across approximately 280 U.S. academic or community-based cancer clinics March 2014-July 2021. De-identified data was captured into a real-world clinico-genomic database (CGDB). All patients underwent Foundation Medicine testing. Time to next treatment (TTNT) and overall survival (OS) comparing ICPI vs. chemotherapy were adjusted for treatment assignment imbalances using propensity scores. 2L: TMB≥10 had more favorable TTNT (Median 24 vs. 4.1 months, HR: 0.19, 95%CI: 0.09–0.44, p=0.0001) and OS (median 43.1 vs. 6.2 months, HR: 0.24, 95%CI: 0.011–0.54, p=0.0005), TMB<10 did not (p>0.05). 1L: TMB≥10 had more favorable TTNT (not reached vs. median 4.1 months, HR: 0.13, 95%CI: 0.03–0.48, p=0.0024) and OS (not reached vs. median 17.1 months, HR: 0.30, 95%CI: 0.08–1.14, p=0.078), TMB<10 had less favorable TTNT (median 2.8 vs. 6.5 months, HR: 2.36, 95%CI: 1.25–4.45, p=0.008) and OS (median 4.5 vs. 13.1 months, HR: 1.82, 95%CI: 0.87–3.81, p=0.11). TMB ≥ 10 robustly identifies mEG patients with more favorable outcomes on 2L ICPI monotherapy vs. chemotherapy. 1L data are more limited, but effects are consistent with 2L.

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Section: Discussionmentioning

confidence: 99%

Real-world Validation of TMB and Microsatellite Instability as Predictive Biomarkers of Immune Checkpoint Inhibitor Effectiveness in Advanced Gastroesophageal Cancer

Graf

Fisher

Creeden

et al. 2022

Cancer Research Communications

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“…Cox proportional hazards models were used to estimate hazard ratios of prognostic factors, and the proportionality of hazards was assessed using log(−log) survival plots. Accounting for the left‐truncation issue and the immortal time bias was implemented [ 36 ], and the correction was applied to the delayed entry between diagnosis and performing genomic sequencing. The association between baseline genetic alterations and progression‐free survival (PFS)/OS was analyzed for altered genes identified in at least two patients in the baseline cohort, and FDR correction for the multiple comparison issue was applied over these genes as well as baseline clinical characteristics.…”

Section: Methodsmentioning

confidence: 99%

Prognostic value of baseline genetic features and newly identified TP53 mutations in advanced breast cancer

Zhang

Sun

Zhao³

et al. 2022

Molecular Oncology

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Approximately 30% of breast cancer (BC) patients suffer from disease relapse after definitive treatment. Monitoring BC at baseline and disease progression using comprehensive genomic profiling would facilitate the prediction of prognosis. We retrospectively studied 101 BC patients ultimately experiencing relapse and/or metastases. The baseline and circulating tumor DNA-monitoring cohorts included patients with baseline tumor tissue and serial plasma samples, respectively. Samples were analyzed with targeted next-generation sequencing of 425 cancer-relevant genes. Of 35 patients in the baseline cohort, patients with TP53 mutations (P < 0.01), or CTCF/ GNAS mutations (P < 0.01) displayed inferior disease-free survival, and patients harboring TP53 (P = 0.06) or NOTCH1 (P = 0.06) mutations showed relatively poor overall survival (OS), compared to patients with wild-type counterparts. Of the 59 patients with serial plasma samples, 11 patients who were newly detected with TP53 mutations had worse OS than patients whose TP53 mutational status remained negative (P < 0.01). These results indicate that an inferior prognosis of advanced breast cancer was potentially associated with baseline TP53, CTCF, and NOTCH1 alterations. Newly identified TP53 mutations after relapse and/or metastasis was another potential prognostic biomarker of poor prognosis.

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“…This model incorporated the demographic, clinical, and genomic covariates used in the time-to-metastasis models (age, sex, race, ethnicity, smoking history, stage at diagnosis, and 10 total mutation/copy number alteration variables). Risk set adjustment 21 was not performed, since informative cohort entry has previously been demonstrated in clinico-genomic datasets 12 , 13 , and risk set adjustment could still yield biased results in the event of informative entry. Since this analysis was designed to specifically assess the effect of TP53 mutations on patient survival, no correction for multiple hypotheses was performed.…”

Section: Methodsmentioning

confidence: 99%

Genomic analysis of early-stage lung cancer reveals a role for TP53 mutations in distant metastasis

Egeren

Kohli²,

Catalano³

et al. 2022

Sci Rep

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Patients with non-small cell lung cancer (NSCLC) who have distant metastases have a poor prognosis. To determine which genomic factors of the primary tumor are associated with metastasis, we analyzed data from 759 patients originally diagnosed with stage I–III NSCLC as part of the AACR Project GENIE Biopharma Collaborative consortium. We found that TP53 mutations were significantly associated with the development of new distant metastases. TP53 mutations were also more prevalent in patients with a history of smoking, suggesting that these patients may be at increased risk for distant metastasis. Our results suggest that additional investigation of the optimal management of patients with early-stage NSCLC harboring TP53 mutations at diagnosis is warranted in light of their higher likelihood of developing new distant metastases.

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Implications of Selection Bias Due to Delayed Study Entry in Clinical Genomic Studies

Cited by 30 publications

References 24 publications

Real-world Validation of TMB and Microsatellite Instability as Predictive Biomarkers of Immune Checkpoint Inhibitor Effectiveness in Advanced Gastroesophageal Cancer

Real-world Validation of TMB and Microsatellite Instability as Predictive Biomarkers of Immune Checkpoint Inhibitor Effectiveness in Advanced Gastroesophageal Cancer

Prognostic value of baseline genetic features and newly identified TP53 mutations in advanced breast cancer

Genomic analysis of early-stage lung cancer reveals a role for TP53 mutations in distant metastasis

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