Purpose Somatic deletions that affect the lymphoid transcription factor–coding gene IKZF1 have previously been reported as independently associated with a poor prognosis in pediatric B-cell precursor (BCP) acute lymphoblastic leukemia (ALL). We have now refined the prognostic strength of IKZF1 deletions by analyzing the effect of co-occurring deletions. Patients and Methods The analysis involved 991 patients with BCP ALL treated in the Associazione Italiana Ematologia ed Oncologia Pediatrica–Berlin-Frankfurt-Muenster (AIEOP-BFM) ALL 2000 trial with complete information for copy number alterations of IKZF1, PAX5, ETV6, RB1, BTG1, EBF1, CDKN2A, CDKN2B, Xp22.33/Yp11.31 (PAR1 region; CRLF2, CSF2RA, and IL3RA), and ERG; replication of findings involved 417 patients from the same trial. Results IKZF1 deletions that co-occurred with deletions in CDKN2A, CDKN2B, PAX5, or PAR1 in the absence of ERG deletion conferred the worst outcome and, consequently, were grouped as IKZF1plus. The IKZF1plus group comprised 6% of patients with BCP ALL, with a 5-year event-free survival of 53 ± 6% compared with 79 ± 5% in patients with IKZF1 deletion who did not fulfill the IKZF1plus definition and 87 ± 1% in patients who lacked an IKZF1 deletion ( P ≤ .001). Respective 5-year cumulative relapse incidence rates were 44 ± 6%, 11 ± 4%, and 10 ± 1% ( P ≤ .001). Results were confirmed in the replication cohort, and multivariable analyses demonstrated independence of IKZF1plus. The IKZF1plus prognostic effect differed dramatically in analyses stratified by minimal residual disease (MRD) levels after induction treatment: 5-year event-free survival for MRD standard-risk IKZF1plus patients was 94 ± 5% versus 40 ± 10% in MRD intermediate- and 30 ± 14% in high-risk IKZF1plus patients ( P ≤ .001). Corresponding 5-year cumulative incidence of relapse rates were 6 ± 6%, 60 ± 10%, and 60 ± 17% ( P ≤ .001). Conclusion IKZF1plus describes a new MRD-dependent very-poor prognostic profile in BCP ALL. Because current AIEOP-BFM treatment is largely ineffective for MRD-positive IKZF1plus patients, new experimental treatment approaches will be evaluated in our upcoming trial AIEOP-BFM ALL 2017.

Key Points• RAS pathway mutations are prevalent in relapsed childhood ALL, and KRAS mutations are associated with a poorer overall survival.• RAS pathway mutations confer sensitivity to mitogenactivated protein kinase kinase inhibitors.For most children who relapse with acute lymphoblastic leukemia (ALL), the prognosis is poor, and there is a need for novel therapies to improve outcome. We screened samples from children with B-lineage ALL entered into the ALL-REZ BFM 2002 clinical trial (www. clinicaltrials.gov, #NCT00114348) for somatic mutations activating the Ras pathway (KRAS, NRAS, FLT3, and PTPN11) and showed mutation to be highly prevalent (76 from 206). Clinically, they were associated with high-risk features including early relapse, central nervous system (CNS) involvement, and specifically for NRAS/KRAS mutations, chemoresistance. KRAS mutations were associated with a reduced overall survival. Mutation screening of the matched diagnostic samples found many to be wild type (WT); however, by using more sensitive allelic-specific assays, low-level mutated subpopulations were found in many cases, suggesting that they survived up-front therapy and subsequently emerged at relapse. Preclinical evaluation of the mitogen-activated protein kinase kinase 1/2 inhibitor selumetinib (AZD6244, ARRY-142886) showed significant differential sensitivity in Ras pathway-mutated ALL compared with WT cells both in vitro and in an orthotopic xenograft model engrafted with primary ALL; in the latter, reduced RAS-mutated CNS leukemia. Given these data, clinical evaluation of selumetinib may be warranted for Ras pathway-mutated relapsed ALL. (Blood. 2014;124(23):3420-3430)

Minimal residual disease (MRD) as measured by multiparameter flow cytometry (FCM) is an independent and strong prognostic factor in B-cell acute lymphoblastic leukemia (B-ALL). However, reliable flow cytometric detection of MRD strongly depends on operator skills and expert knowledge. Hence, an objective, automated tool for reliable FCM-MRD quantification, able to overcome the technical diversity and analytical subjectivity, would be most helpful. We developed a supervised machine learning approach using a combination of multiple Gaussian Mixture Models (GMM) as a parametric density model. The approach was used for finding the weights of a linear combination of multiple GMMs to represent new, "unseen" samples by an interpolation of stored samples. The experimental data set contained FCM-MRD data of 337 bone marrow samples collected at day 15 of induction therapy in three different laboratories from pediatric patients with B-ALL for which accurate, expert-set gates existed. We compared MRD quantification by our proposed GMM approach to operator assessments, its performance on data from different laboratories, as well as to other state-of-the-art automated readout methods. Our proposed GMM-combination approach proved superior over support vector machines, deep neural networks, and a single GMM approach in terms of precision and average F 1 -scores. A high correlation of expert operator-based and automated MRD assessment was achieved with reliable automated MRD quantification (F 1 -scores >0.5 in more than 95% of samples) in the clinically relevant range. Although best performance was found, if test and training samples were from the same system (i.e., flow cytometer and staining panel; lowest median F 1 -score 0.92), cross-system performance remained high with a median F 1 -score above 0.85 in all settings. In conclusion, our proposed automated approach could potentially be used to assess FCM-MRD in B-ALL in an objective and standardized manner across different laboratories.