Early peripheral clearance of leukemia-associated immunophenotypes in AML: centralized analysis of a randomized trial

Although genetics is a relevant risk factor in acute myeloid leukemia (AML), it can be minimally informative and/or not readily available for the early identification of patients at risk for treatment failure. In a randomized trial comparing standard vs high-dose induction (ClinicalTrials.gov #NCT00495287), we studied early peripheral blast cell clearance (PBC) as a rapid predictive assay of chemotherapy response to determine whether it correlates with the achievement of complete remission (CR), as well as postremission outcome, according to induction intensity. Individual leukemia-associated immunophenotypes (LAIPs) identified pretherapy by flow cytometry were validated and quantified centrally after 3 days of treatment, expressing PBC on a logarithmic scale as the ratio of absolute LAIP+ cells on day 1 and day 4. Of 178 patients, 151 (84.8%) were evaluable. Patients in CR exhibited significantly higher median PBC (2.3 log) compared with chemoresistant patients (1.0 log; P < .0001). PBC < 1.0 predicted the worst outcome (CR, 28%). With 1.5 log established as the most accurate cutoff predicting CR, 87.5% of patients with PBC >1.5 (PBChigh, n = 96) and 43.6% of patients with PBC ≤1.5 (PBClow, n = 55) achieved CR after single-course induction (P < .0001). CR and PBChigh rates were increased in patients randomized to the high-dose induction arm (P = .04) and correlated strongly with genetic/cytogenetic risk. In multivariate analysis, PBC retained significant predictive power for CR, relapse risk, and survival. Thus, PBC analysis can provide a very early prediction of outcome, correlates with treatment intensity and disease subset, and may support studies of customized AML therapy.

Plasma KRAS as a biomarker for pancreatic ductal adenocarcinoma (PDAC).

316 Background: PDAC needs validated diagnostic biomarkers for early detection and predictive markers for outcome. As 95% of PDACs harbor KRAS mutations (mKRAS), circulating tumor DNA (ctDNA) has potential utility in PDAC. We assessed the ability to detect and correlate mKRAS in a metastatic PDAC cohort from Memorial Sloan Kettering Cancer Center. Methods: 10 mL of whole blood was collected. cfDNA was extracted with QIAmp or QIAsymphony DNA extraction kits (Qiagen, Valencia, CA). Directed (KRAS G12D, G12R, G12V, Q61H) or multiplex (G12A, G12C, G12D, G12R, G12S, G12V, G13D) digital droplet PCR (ddPCR) was performed with Raindrop Plus (Raindance Technologies, Billerica, MA) or QX200 (BioRad, Hercules, CA) ddPCR systems. Number and size of liver, lung and lymph node metastases, peritoneal disease (mild, moderate, severe), ascites (trace, small, large) and bone mets (Y/N) were assessed by CT scan. Results: See table. 21 (55%) had detectable ctDNA (ctDNA(+)) with mean mutant allele fraction of 4.5% (0.015-36.8). ctDNA (+) vs (-) PFS and OS from collection were 6.9 and 8.4 months vs. 9.9 and 10.5 (p=0.89 for both). CA19-9, PFS and OS did not correlate with ctDNA tertile (p=0.15, 0.54 & 0.50). On treatment and disease activity were not associated with ctDNA status (p= 0.20 & 0.60). Number and size of liver mets were associated with ctDNA (+) (p=0.006 & 0.007). Conclusions: ctDNA KRAS detection was measurable in metastatic disease with rates consistent with other PDAC reports. Median PFS, OS were lower in ctDNA (+) group but not statistically significant in this diverse cohort. Number and size of liver metastases were significantly higher in ctDNA (+). Future study should focus on practice changing applications with standardized collection, intra-patient comparisons and role of liver disease burden. We have initiated studies to evaluate plasma KRAS prior to and during treatment to address its value as a predictive assay and explore factors affecting ctDNA detection. [Table: see text]

Drug Discovery Testing Compounds in Patient Samples by Automated Flow Cytometry

Functional ex vivo assays that predict a patient’s clinical response to anticancer drugs for guiding cancer treatment have long been a goal, but few have yet proved to be reliable. To address this, we have developed an automated flow cytometry platform for drug screening that evaluates multiple endpoints with a robust data analysis system that can capture the complex mechanisms of action across different compounds. This system, called PharmaFlow, is used to test peripheral blood or bone marrow samples from patients diagnosed with hematological malignancies. Functional assays that use the whole sample, retaining all the microenvironmental components contained in the sample, offer an approach to ex vivo testing that may give results that are clinically relevant. This new approach can help to predict the patients’ response to existing treatments or to drugs under development, for hematological malignancies or other tumors. In addition, relevant biomarkers can be identified that determine the patient’s sensitivity, resistance, or toxicity to a given treatment. We propose that this approach, which better recapitulates the human microenvironment, constitutes a more predictive assay for personalized medicine and preclinical drug discovery.