Faculty Bibliography

Intratumoral heterogeneity and subclonal diversity, characterized by the coexistence of genetically and functionally distinct leukemic cell populations within a single patient, have long been recognized as major contributors to chemotherapy resistance and disease relapse in acute myeloid leukemia. In this issue of Blood Cancer Discovery, Karigane and colleagues delve into the mechanisms that underlie the interactions between distinct leukemic subpopulations and identify IFN signaling as a critical regulator that determines clonal dominance and expansion. See related article by Karigane et al., p. XX .

Long noncoding RNAs (lncRNAs) are a significant yet largely uncharted component of the cancer transcriptome, with their isoform-specific functions remaining poorly understood. In this study, we employed RNA-targeting CRISPR-Cas13d to uncover and characterize hundreds of tumor-essential (te)-lncRNA isoforms with clinical relevance. Focusing on multiple myeloma (MM), we targeted the lncRNA transcriptome expressed in tumor cells from MM patients and revealed both MM-specific and pan-cancer dependencies across diverse cancer cell lines, which we further validated in animal models. Additionally, we mapped the subcellular localization of these te-lncRNAs, identifying over 30 cytosolic isoforms that proved essential when targeted by cytosol-localized Cas13d. Notably, a specific isoform of SNHG6, enriched in the endoplasmic reticulum, interacts with heat shock proteins to maintain cellular proteostasis. We also integrated functional and clinical data into the publicly accessible LongDEP Portal, providing a valuable resource for the research community. Our study offers a comprehensive characterization of te-lncRNAs, underscoring their oncogenic roles and therapeutic potential.

BH3-mimetics are used as an efficient strategy to induce cell death in several blood malignancies, including acute myeloid leukemia (AML). Venetoclax, a potent BCL-2 antagonist, is used clinically in combination with hypomethylating agents for the treatment of AML. Moreover, MCL-1 or dual BCL-2/BCL-xL antagonists are under investigation. Yet, resistance to single or combinatorial BH3-mimetics therapies eventually ensues. Integration of multiple genome-wide CRISPR/Cas9 screens revealed that loss of mitophagy modulators sensitizes AML cells to various BH3-mimetics targeting different BCL-2 family members. One such regulator is MFN2, whose protein levels positively correlate with drug resistance in patients with AML. MFN2 overexpression is sufficient to drive resistance to BH3-mimetics in AML. Insensitivity to BH3-mimetics is accompanied by enhanced mitochondria-endoplasmic reticulum interactions and augmented mitophagy flux which acts as a pro-survival mechanism to eliminate mitochondrial damage. Genetic or pharmacologic MFN2 targeting synergizes with BH3-mimetics by impairing mitochondrial clearance and enhancing apoptosis in AML.

Expression levels of long non-coding RNAs (lncRNAs) have been shown to associate with clinical outcome of patients with cytogenetically normal acute myeloid leukemia (CN-AML). However, the frequency and clinical significance of genetic variants in the nucleotide sequences of lncRNAs in AML patients is unknown. Herein, we analyzed total RNA sequencing data of 377 younger adults (aged.

Recently, a novel knowledge bank (KB) approach to predict outcomes of individual patients with acute myeloid leukemia (AML) was developed using unbiased machine learning. To validate its prognostic value, we analyzed 1612 adults with de novo AML treated on Cancer and Leukemia Group B front-line trials who had pretreatment clinical, cytogenetics, and mutation data on 81 leukemia/cancer-associated genes available. We used receiver operating characteristic (ROC) curves and the area under the curve (AUC) to evaluate the predictive values of the KB algorithm and other risk classifications. The KB algorithm predicted 3-year overall survival (OS) probability in the entire patient cohort (AUC_KB = 0.799), and both younger (< 60 years) (AUC_KB = 0.747) and older patients (AUC_KB = 0.770). The KB algorithm predicted non-remission death (AUC_KB = 0.860) well but was less accurate in predicting relapse death (AUC_KB = 0.695) and death in first complete remission (AUC_KB = 0.603). The KB algorithm's 3-year OS predictive value was higher than that of the 2017 European LeukemiaNet (ELN) classification (AUC_2017ELN = 0.707, p < 0.001) and 2010 ELN classification (AUC_2010ELN = 0.721, p < 0.001) but did not differ significantly from that of the 17-gene stemness score (AUC_17-gene = 0.732, p = 0.10). Analysis of additional cytogenetic and molecular markers not included in the KB algorithm revealed that taking into account atypical complex karyotype, infrequent recurrent balanced chromosome rearrangements and mutational status of the SAMHD1, AXL and NOTCH1 genes may improve the KB algorithm. We conclude that the KB algorithm has a high predictive value that is higher than those of the 2017 and 2010 ELN classifications. Inclusion of additional genetic features might refine the KB algorithm.

BACKGROUND:Older patients (≥ 60 years) with acute myeloid leukemia (AML) often have multiple, sequentially acquired, somatic mutations that drive leukemogenesis and are associated with poor outcome. Beat AML is a Leukemia and Lymphoma Society-sponsored, multicenter umbrella study that algorithmically segregates AML patients based upon cytogenetic and dominant molecular abnormalities (variant allele frequencies (VAF) ≥ 0.2) into different cohorts to select for targeted therapies. During the conception of the Beat AML design, a historical dataset was needed to help in the design of the genomic algorithm for patient assignment and serve as the basis for the statistical design of individual genomic treatment substudies for the Beat AML study. METHODS:We classified 563 newly diagnosed older AML patients treated with standard intensive chemotherapy on trials conducted by Cancer and Leukemia Group B based on the same genomic algorithm and assessed clinical outcomes. RESULTS:Our classification identified core-binding factor and NPM1-mutated/FLT3-ITD-negative groups as having the best outcomes, with 30-day early death (ED) rates of 0 and 20%, respectively, and median overall survival (OS) of > 1 year and 3-year OS rates of ≥ 20%. All other genomic groups had ED rates of 17-42%, median OS ≤ 1 year and 3-year OS rates of ≤ 15%. CONCLUSIONS:By classifying patients through this genomic algorithm, outcomes were poor and not unexpected from a non-algorithmic, non-dominant VAF approach. The exception is 30-day ED rate typically is not available for intensive induction for individual genomic groups and therefore difficult to compare outcomes with targeted therapeutics. This Alliance data supported the use of this algorithm for patient assignment at the initiation of the Beat AML study. This outcome data was also used for statistical design for Beat AML substudies for individual genomic groups to determine goals for improvement from intensive induction and hopefully lead to more rapid approval of new therapies. Trial registration ClinicalTrials.gov Identifiers: NCT00048958 (CALGB 8461), NCT00900224 (CALGB 20202), NCT00003190 (CALGB 9720), NCT00085124 (CALGB 10201), NCT00742625 (CALGB 10502), NCT01420926 (CALGB 11002), NCT00039377 (CALGB 10801), and NCT01253070 (CALGB 11001).

Although ∼80% of adult patients with cytogenetically normal acute myeloid leukemia (CN-AML) achieve a complete remission (CR), more than half of them relapse. Better identification of patients who are likely to relapse can help to inform clinical decisions. We performed RNA sequencing on pretreatment samples from 268 adults with de novo CN-AML who were younger than 60 years of age and achieved a CR after induction treatment with standard "7+3" chemotherapy. After filtering for genes whose expressions were associated with gene mutations known to impact outcome (ie, CEBPA, NPM1, and FLT3-internal tandem duplication [FLT3-ITD]), we identified a 10-gene signature that was strongly predictive of patient relapse (area under the receiver operating characteristics curve [AUC], 0.81). The signature consisted of 7 coding genes (GAS6, PSD3, PLCB4, DEXI, JMY, NRP1, C10orf55) and 3 long noncoding RNAs. In multivariable analysis, the 10-gene signature was strongly associated with relapse (P < .001), after adjustment for the FLT3-ITD, CEBPA, and NPM1 mutational status. Validation of the expression signature in an independent patient set from The Cancer Genome Atlas showed the signature's strong predictive value, with AUC = 0.78. Implementation of the 10-gene signature into clinical prognostic stratification could be useful for identifying patients who are likely to relapse.