Ultrasensitive NGS MRD assessment in Ph+ ALL: Prognostic impact and correlation with RT-PCR for BCR::ABL1.

Reverse transcription polymerase chain reaction (RT-PCR) for BCR::ABL1 is the most common and widely accepted method of measurable residual disease (MRD) assessment in Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL); however, RT-PCR may not be an optimal measure of MRD in many cases of Ph+ ALL. We evaluated the clinical impact of a highly sensitive next-generation sequencing (NGS) MRD assay (sensitivity of 10-6 ) and its correlation with RT-PCR for BCR::ABL1 in patients with Ph+ ALL. Overall, 32% of patients had a discordance between MRD assessment by RT-PCR and NGS, and 31% of patients who achieved NGS MRD negativity were PCR+ at the same timepoint. Among eight patients with long-term detectable BCR::ABL1 by PCR, six were PCR+/NGS-. These patients generally had stable PCR levels that persisted despite therapeutic interventions, and none subsequently relapsed; in contrast, patients who were PCR+/NGS+ had more variable PCR values that responded to therapeutic intervention. In a separate cohort of prospectively collected clinical samples, 11 of 65 patients (17%) with Ph+ ALL who achieved NGS MRD negativity had detectable BCR::ABL1 by PCR, and none of these patients relapsed. Relapse-free survival and overall survival were similar in patients who were PCR+/NGS- and PCR-/NGS-, suggesting that PCR for BCR::ABL1 did not provide additional prognostic information in patients who achieved NGS MRD negativity. NGS-based assessment of MRD is prognostic in Ph+ ALL and identifies patients with low-level detectable BCR::ABL1 who are unlikely to relapse nor to benefit from therapeutic interventions.

High-sensitivity next-generation sequencing MRD assessment in ALL identifies patients at very low risk of relapse.

Measurable residual disease (MRD) is highly prognostic for relapse and overall survival (OS) in acute lymphoblastic leukemia (ALL), although many patients with apparent "MRD negativity" by standard assays still relapse. We evaluated the clinical impact of a highly sensitive next-generation sequencing (NGS) MRD assay in 74 adults with ALL undergoing frontline therapy. Among remission samples that were MRD negative by multiparameter flow cytometry (MFC), 46% were MRD+ by the NGS assay. After 1 cycle of induction chemotherapy, MRD negativity by MFC at a sensitivity of 1 × 10-4 and NGS at a sensitivity of 1 × 10-6 was achieved in 66% and 23% of patients, respectively. The 5-year cumulative incidence of relapse (CIR) among patients who achieved MRD negativity by MFC at complete remission (CR) was 29%; in contrast, no patients who achieved early MRD negativity by NGS relapsed, and their 5-year OS was 90%. NGS MRD negativity at CR was associated with significantly decreased risk of relapse compared with MRD positivity (5-year CIR, 0% vs 45%, respectively; P = .04). Among patients who were MRD negative by MFC, detection of low levels of MRD by NGS identified patients who still had a significant risk of relapse (5-year CIR, 39%). Early assessment of MRD using a highly sensitive NGS assay adds clinically relevant prognostic information to standard MFC-based approaches and can identify patients with ALL undergoing frontline therapy who have a very low risk of relapse and excellent long-term survival.

Ultra-accurate Duplex Sequencing for the assessment of pretreatment ABL1 kinase domain mutations in Ph+ ALL.

Mutations of ABL1 are the dominant mechanism of relapse in Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph + ALL). We performed highly accurate Duplex Sequencing of exons 4-10 of ABL1 on bone marrow or peripheral blood samples from 63 adult patients with previously untreated Ph + ALL who received induction with intensive chemotherapy plus a BCR-ABL1 TKI. We identified ABL1 mutations prior to BCR-ABL1 TKI exposure in 78% of patients. However, these mutations were generally present at extremely low levels (median variant allelic frequency 0.008% [range, 0.004%-3.71%] and did not clonally expand and lead to relapse in any patient, even when the pretreatment mutation was known to confer resistance to the TKI received. In relapse samples harboring a TKI-resistant ABL1 mutation, the corresponding mutation could not be detected pretreatment, despite validated sequencing sensitivity of Duplex Sequencing down to 0.005%. In samples under the selective pressure of ongoing TKI therapy, we detected low-level, emerging resistance mutations up to 5 months prior to relapse. These findings suggest that pretreatment ABL1 mutation assessment should not guide upfront TKI selection in Ph + ALL, although serial testing while on TKI therapy may allow for early detection of clinically actionable resistant clones.

Thresher: determining the number of clusters while removing outliers.

BACKGROUND: Cluster analysis is the most common unsupervised method for finding hidden groups in data. Clustering presents two main challenges: (1) finding the optimal number of clusters, and (2) removing "outliers" among the objects being clustered. Few clustering algorithms currently deal directly with the outlier problem. Furthermore, existing methods for identifying the number of clusters still have some drawbacks. Thus, there is a need for a better algorithm to tackle both challenges. RESULTS: We present a new approach, implemented in an R package called Thresher, to cluster objects in general datasets. Thresher combines ideas from principal component analysis, outlier filtering, and von Mises-Fisher mixture models in order to select the optimal number of clusters. We performed a large Monte Carlo simulation study to compare Thresher with other methods for detecting outliers and determining the number of clusters. We found that Thresher had good sensitivity and specificity for detecting and removing outliers. We also found that Thresher is the best method for estimating the optimal number of clusters when the number of objects being clustered is smaller than the number of variables used for clustering. Finally, we applied Thresher and eleven other methods to 25 sets of breast cancer data downloaded from the Gene Expression Omnibus; only Thresher consistently estimated the number of clusters to lie in the range of 4-7 that is consistent with the literature. CONCLUSIONS: Thresher is effective at automatically detecting and removing outliers. By thus cleaning the data, it produces better estimates of the optimal number of clusters when there are more variables than objects. When we applied Thresher to a variety of breast cancer datasets, it produced estimates that were both self-consistent and consistent with the literature. We expect Thresher to be useful for studying a wide variety of biological datasets.