Jacobson, Sawyer

343 Background: Alliance/CALGB 80802, a randomized phase III trial, evaluated sorafenib plus doxorubicin vs. doxorubicin in pts with HCC and showed no improvement in median overall survival (OS) (HR[95CI] 1.05[0.83-1.31]) or PFS (HR[95CI] 0.93[0.75-1.16]). In HCC surrogacy of tumor response with OS remains controversial, in part due to varying criteria used for response evaluation (e.g., RECIST1.1 and mRECIST). We evaluated the performance of several models to predict OS using pretreatment clinical and radiomic variables. Methods: In CALBG 80802, we segmented all measurable tumor lesions on sequential CT scans. A lesion’s imaging phenotype was deciphered with 23 uncorrelated quantitative imaging features measured at baseline and week (wk) 10 (first follow-up). An OS landmark survival analysis was conducted at wk 10. Patients were randomly assigned (3:1) to training (n = 92) and validation (n = 37) sets. In a training set, 6 random forest predictive models (6 signatures) used features that best predicted OS using 3 sets of variables: radiomics only (n = 23), clinical only (n = 9), radiomics and clinical (n = 32). Two time points (baseline only or baseline + wk 10) were assessed. Each signature's output was an individualized prediction and a continuous value ranging from 0 to 1 (from most to least favorable predicted OS). The primary endpoint was to compare these models' performance to predict OS using error rate (Harrell's concordance-index) in the validation set. Results: Of the 6 training signatures evaluated, the one achieving the highest performance in the validation set was an 8-feature signature combining radiomics and clinical variables measured at two time points (baseline + wk 10) with an error rate of 35.6%. The variables [rank of importance] (table) selected by the signature included baseline clinical features (albumin[1], AFP[2], Child-Pugh[4]), baseline radiomics features (component 17[3], component 1[5], component 9[7], tumor volume[8]) and wk 10 radiomics features (delta tumor volume[6]). Variable delta tumor volume [6] used a more enhanced estimation of tumor burden at baseline and a delta tumor volumetric measurement; compared to RECIST1.1 measurement of percentage change in unidimensional measurement of a subset of target lesions. The four quartiles of the signature were significantly associated with OS (Log-Rank, P < 0.0001). Conclusions: The selected combined radiomic and clinical composite signature provided the best prediction for OS in the 80802 study patients’ population. It is a suggested way forward to go beyond single anatomic measurement techniques such as RECIST or mRECIST. [Table: see text]

325 Background: Alliance/CALGB 80802 randomized phase III trial evaluated DO+S vs. S in pts with aHCC, and showed no improvement in median OS. Multi-drug resistant pathway mitigation by the Ras/Raf/MEK/ERK pathway and bFGF-mediated activation of Raf-1 promotes the formation of antiapoptotic Raf-1 and ASK1 complex, induced by anthracyclines. S efficiently blocks NS5A-recruited c-Raf mediated HCV replication and viral gene expression. Once inhibited by S, VEGF expression of HepG2 may limit HCV cellular entry. Release of Raf-1-Ask-1 dimer and inhibition of Raf-1 via S putatively differ in the presence or absence of DO. We hypothesize treatment with S reduces HCV titer levels (TL) and influence pts’ outcome. Methods: In 80802 HCV pts, TL were evaluated in both arms at baseline and post-baseline at Day 1 of Cycles 2, 3, and every 2 cycles and at progression or discontinuation of therapy. HCV undetectable (HCV-UN) levels were defined as < 50 copies/mL. TL were evaluated in relation to OS and PFS. HCV RNA levels were measured by TaqMan PCR and by genotype. Results: Of 356 pts, 83 were HCV+ with more Black/African American (25/50 = 50%) vs. White (54/239 = 23%) or other race groups (4/67 = 6%) (p < 0.0001). HCV titer data were available on 54 pts (S: 28, DO+S: 26). At baseline, 12 pts (S: 7, DO+S: 5) were HCV-UN, and post-baseline HCV TL did not significantly differ between treatment arms; one patient in each arm went from detectable (HCV-D) to HCV-UN. Post-baseline, 40 pts were HCV-D vs. 14 who were HCV-UN (S+DO: 8, S: 6 pts). Except for the two pts who became HCV-UN, baseline HCV-D vs. HCV-UN titers was similar to that status post-baseline. PFS and OS between HCV-D and HCV-UN both at baseline and post-baseline are delineated in the table. Conclusions: We observed that S did not influence HCV TL. Pts treated with DO+S vs. S had worse PFS if they had HCV-UN, and further that higher levels of HCV titers at baseline were associated with significantly improved PFS. Given the small sample size, these findings warrant further prospective evaluation. Support: U10CA180821, U10CA180882, U24CA196171; Bayer, Bristol-Myers-Squibb, and Sanofi. https://acknowledgments.alliancefound.org . Clinical trial information: NCT01015833. [Table: see text]