LBA4517 Background: A preplanned interim analysis of COU-AA-301 showed that AA, a selective androgen biosynthesis (CYP17) inhibitor, significantly improves OS in mCRPC. This is the first phase III study to prospectively assess CTC as a surrogate biomarker as part of a regulatory qualification process, here using updated OS data. Methods: 1,195 patients (pts) with mCRPC post docetaxel were randomized 2:1 to AA (1 g QD) + P (5 mg BID) (n = 797) or placebo + P (n = 398). CTCs (screening and baseline [BL]; post BL at weeks 4, 8 and 12) were enumerated (cells/7.5 mL) at MSKCC and The ICR using CellSearch and analyzed with other prognostic covariates as dichotomous and continuous variables using updated OS data at 775 events (prior to crossover from placebo to AA). CTC (as part of a biomarker panel - LDH, PSA, Hg, AlkPhos) was examined as a surrogate for OS. Multivariate (Cox model) analyses were conducted. Results: At median follow up (FU) of 20.2 mo, the difference in median OS between the 2 groups improved from 3.9 to 4.6 mo (AA 15.8 mo vs placebo 11.2 mo; HR = 0.74; p < .0001). CTC counts were evaluated in 972 pts at screening and BL, 838 at 4 wks, 783 at 8 wks and 723 pts at 12 wks. High concordance between screening and BL values was observed (r = 0.83). CTC conversion using standard definition for unfavorable (CTC ≥ 5) and favorable (CTC < 5) counts was predictive of OS as early as 4 wks after treatment and its inclusion significantly reduced the treatment effect at all post-treatment time points (HR: from 0.74 to 0.97). A reduced model incorporating CTCs and LDH was developed. Conclusions: The magnitude of the treatment effect of AA on OS increased with longer FU. Using standard definition of CTC conversion, the biomarker panel demonstrated a level of surrogacy for OS by correlating well with survival and in a model-adjusted analysis dramatically attenuating the treatment effect. BL CTCs and CTC conversion, along with LDH, were key predictors of OS. Future trials will further evaluate the CTC-based surrogate developed from COU-AA-301.
BackgroundThere has been a dramatic increase in T cell receptor (TCR) sequencing spurred, in part, by the widespread adoption of this technology across academic medical centers and by the rapid commercialization of TCR sequencing. While the raw TCR sequencing data has increased, there has been little in the way of approaches to parse the data in a biologically meaningful fashion. The ability to parse this new type of 'big data' quickly and efficiently to understand the T cell repertoire in a structurally relevant manner has the potential to open the way to new discoveries about how the immune system is able to respond to insults such as cancer and infectious diseases.
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