Table 3.

Comparison of prevalence of gene alteration in the substudy eligibility criteria between FMI and TCGA

Drug (TT, NMT) manufacturer substudy IDGeneAlteration typeFMI prevalence (n = 108 lung squamous cell carcinoma samples)TCGA prevalencea (n = 178 lung squamous cell carcinoma samples)FMI vs. TCGA Difference P value (Fisher exact test)
AZD4547 AstraZeneca Substudy DFGFR1Substitution0.0%0.6%1
Fusion0.0%NANA
Amplification7.4%16.9%0.03
FGFR2Substitution0.0%2.2%0.30
Fusion0.0%NANA
Amplification0.0%0.0%1
FGFR3Substitution3.7%2.2%0.48
Fusion0.0%NANA
Amplification0.0%0.6%1
Palbociclib Pfizer Substudy CCDK4Amplification0.9%0.0%0.38
CCND1Amplification8.3%12.4%0.33
CCND2Amplification2.8%2.2%1
CCND3Amplification1.9%0.6%0.55
Rilotumumab [GDC-0032] Genentech Substudy BPIK3CASubstitution9.3%11.8%0.56

NOTE: This table compares prevalence of gene alterations in the substudy eligibility criteria between FMI and TCGA lung SCC datasets (P values from Fisher exact test shown). The observed prevalences are similar between the two datasets, with the exception of FGFR1 amplifications, observed at a lower prevalence in the FMI dataset.

  • aTCGA data of SCC (2) were retrieved using cBioPortal (41, 42). Because FMI detects copy-number alterations by fitting a statistical copy-number model to normalized coverage and allele frequencies, whereas the TCGA data used in this comparison were generated using the GISTIC algorithm (43) and application of a per-sample variable threshold, the absolute level at which amplifications are called could not be directly compared. Given that amplifications in the FMI approach are called at an estimated 6 copies or above and adjusted to 7 copies for triploid and 8 copies for tetraploid specimens, it is likely that the difference is explainable by the more stringent definition of amplification in the FMI approach.