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. 2023 Apr;193(4):392–403. doi: 10.1016/j.ajpath.2022.12.013

Figure 3.

Figure 3

Fusion gene algorithms from UPMC cohort improve PSA-free survival predictions by Gleason score, serum PSA, or the combination of both in the Stanford/Wisconsin cohort. Top panels: Kaplan-Meier analyses of PSA-free survival in prostate cancer patients in the Stanford/Wisconsin cohort predicted by Gleason (cutoff = 8, left), PSA (cutoff = 9.77 ng/mL, middle), or Gleason score + PSA (logistic, right). Bottom panels: Kaplan-Meier analyses of PSA-free survival in prostate cancer patients in the Stanford/Wisconsin cohort, predicted by linear discriminant analysis (LDA) model using the detection of four fusion genes [TRMT11-GRIK2 (CT ≤ 43), CCNH-C5orf30 (negative), CLTC-ETV1 (CT ≤ 37), and ACPP-SEC13 (CT ≤ 40)] + Gleason score (left), a logistic model using the detection of three fusion genes [TRMT11-GRIK2 (CT ≤ 43), CCNH-C5orf30 (negative), and ACPP-SEC13 (CT ≤ 40)] + PSA (middle), and an LDA model using the detection of four fusion genes [TRMT11-GRIK2 (CT ≤ 43), CCNH-C5orf30 (negative), ACPP-SEC13 (CT ≤ 40), and DOCK7-OLR1 (CT ≤ 41)] + Gleason score + PSA (right).