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Redefining ALL classification: toward detecting high-risk ALL and implementing precision medicine.

ABSTRACT: Acute lymphoblastic leukemia (ALL) is the commonest childhood tumor and remains a leading cause of cancer death in the young. In the last decade, microarray and sequencing analysis of large ALL cohorts has revolutionized our understanding of the genetic basis of this disease. These studies have identified new ALL subtypes, each characterized by constellations of structural and sequence alterations that perturb key cellular pathways, including lymphoid development, cell-cycle regulation, and tumor suppression; cytokine receptor, kinase, and Ras signaling; and chromatin modifications. Several of these pathways, particularly kinase-activating lesions and epigenetic alterations, are logical targets for new precision medicine therapies. Genomic profiling has also identified important interactions between inherited genetic variants that influence the risk of leukemia development and the somatic genetic alterations that are required to establish the leukemic clone. Moreover, sequential sequencing studies at diagnosis, remission, and relapse have provided important insights into the relationship among genetic variants, clonal heterogeneity, and the risk of relapse. Ongoing studies are extending our understanding of coding and noncoding genetic alterations in B-progenitor and T-lineage ALL and using these insights to inform the development of faithful experimental models to test the efficacy of new treatment approaches.

SUBMITTER: Hunger SP

PROVIDER: S-EPMC4481590 | biostudies-other | 2015 Jun

REPOSITORIES: biostudies-other

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Redefining ALL classification: toward detecting high-risk ALL and implementing precision medicine.

Hunger Stephen P SP Mullighan Charles G CG

Blood 20150521 26

Acute lymphoblastic leukemia (ALL) is the commonest childhood tumor and remains a leading cause of cancer death in the young. In the last decade, microarray and sequencing analysis of large ALL cohorts has revolutionized our understanding of the genetic basis of this disease. These studies have identified new ALL subtypes, each characterized by constellations of structural and sequence alterations that perturb key cellular pathways, including lymphoid development, cell-cycle regulation, and tumo ...[more]

PMID: 25999453

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Project description:BackgroundIntertumoral heterogeneity represents a significant hurdle to identifying optimized targeted therapies in gastric cancer (GC). To realize precision medicine for GC patients, an actionable gene alteration-based molecular classification that directly associates GCs with targeted therapies is needed.MethodsA total of 207 Japanese patients with GC were included in this study. Formalin-fixed, paraffin-embedded (FFPE) tumor tissues were obtained from surgical or biopsy specimens and were subjected to DNA extraction. We generated comprehensive genomic profiling data using a 435-gene panel including 69 actionable genes paired with US Food and Drug Administration-approved targeted therapies, and the evaluation of Epstein-Barr virus (EBV) infection and microsatellite instability (MSI) status.ResultsComprehensive genomic sequencing detected at least one alteration of 435 cancer-related genes in 194 GCs (93.7%) and of 69 actionable genes in 141 GCs (68.1%). We classified the 207 GCs into four The Cancer Genome Atlas (TCGA) subtypes using the genomic profiling data; EBV (N = 9), MSI (N = 17), chromosomal instability (N = 119), and genomically stable subtype (N = 62). Actionable gene alterations were not specific and were widely observed throughout all TCGA subtypes. To discover a novel classification which more precisely selects candidates for targeted therapies, 207 GCs were classified using hypermutated phenotype and the mutation profile of 69 actionable genes. We identified a hypermutated group (N = 32), while the others (N = 175) were sub-divided into six clusters including five with actionable gene alterations: ERBB2 (N = 25), CDKN2A, and CDKN2B (N = 10), KRAS (N = 10), BRCA2 (N = 9), and ATM cluster (N = 12). The clinical utility of this classification was demonstrated by a case of unresectable GC with a remarkable response to anti-HER2 therapy in the ERBB2 cluster.ConclusionsThis actionable gene-based classification creates a framework for further studies for realizing precision medicine in GC.

Project description:IntroductionBy improving our understanding of the neurobiological mechanisms underlying addiction, neuroimaging research is helping to identify new targets for personalized treatment interventions. When trying to quit, smokers with larger electrophysiological responses to cigarette-related, compared with pleasant, stimuli ("C > P") are more likely to relapse than smokers with the opposite brain reactivity profile ("P > C").Aim and methodThe goal was to (1) build a classification algorithm to identify smokers characterized by P > C or C > P neuroaffective profiles and (2) validate the algorithm's classification outcomes in an independent data set where we assessed both smokers' electrophysiological responses at baseline and smoking abstinence during a quit attempt. We built the classification algorithm applying discriminant function analysis on the event-related potentials evoked by emotional images in 180 smokers.ResultsThe predictive validity of the classifier showed promise in an independent data set that included new data from 177 smokers interested in quitting; the algorithm classified 111 smokers as P > C and 66 as C > P. The overall abstinence rate was low; 15 individuals (8.5% of the sample) achieved CO-verified 12-month abstinence. Although individuals classified as P > C were nearly 2.5 times more likely to be abstinent than smokers classified as C > P (12 vs. 3, or 11% vs. 4.5%), this result was nonsignificant, preliminary, and in need of confirmation in larger trials.ConclusionThese results suggest that psychophysiological techniques have the potential to advance our knowledge of the neurobiological underpinnings of nicotine addiction and improve clinical applications. However, larger sample sizes are necessary to reliably assess the predictive ability of our algorithm.ImplicationsWe assessed the clinical relevance of a neuroimaging-based classification algorithm on an independent sample of smokers enrolled in a smoking cessation trial and found those with the tendency to attribute more relevance to rewards than cues were nearly 2.5 times more likely to be abstinent than smokers with the opposite brain reactivity profile (11% vs. 4.5%). Although this result was not statistically significant, it suggests our neuroimaging-based classification algorithm can potentially contribute to the development of new precision medicine interventions aimed at treating substance use disorders. Regardless, these findings are still preliminary and in need of confirmation in larger trials.

Dataset Information

Redefining ALL classification: toward detecting high-risk ALL and implementing precision medicine.

Publications

Redefining ALL classification: toward detecting high-risk ALL and implementing precision medicine.

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