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Contemporary chemotherapy for childhood acute lymphoblastic leukemia (ALL) is risk-adapted based on clinical features, leukemia genomics and minimal residual disease (MRD); however, the pharmacological basis of these prognostic variables remains unclear. Analyzing samples from 805 children with newly diagnosed ALL from three consecutive clinical trials, we determined the ex vivo sensitivity of primary leukemia cells to 18 therapeutic agents across 23 molecular subtypes defined by leukemia genomics. There was wide variability in drug response, with favorable ALL subtypes exhibiting the greatest sensitivity to L-asparaginase and glucocorticoids. Leukemia sensitivity to these two agents was highly associated with MRD although with distinct patterns and only in B cell ALL. We identified six patient clusters based on ALL pharmacotypes, which were associated with event-free survival, even after adjusting for MRD. Pharmacotyping identified a T cell ALL subset with a poor prognosis that was sensitive to targeted agents, pointing to alternative therapeutic strategies. Our study comprehensively described the pharmacological heterogeneity of ALL, highlighting opportunities for further individualizing therapy for this most common childhood cancer. Pharmacotyping analyses of large cohorts of pediatric acute lymphoblastic leukemia identify correlations between drug sensitivities and clinical outcomes across different genomic subtypes.

FLT3 is an attractive therapeutic target in acute lymphoblastic leukemia (ALL) but the mechanism for its activation in this cancer is incompletely understood. Profiling global gene expression in large ALL cohorts, we identify over-expression of FLT3 in ZNF384 -rearranged ALL, consistently across cases harboring different fusion partners with ZNF384 . Mechanistically, we discover an intergenic enhancer element at the FLT3 locus that is exclusively activated in ZNF384 -rearranged ALL, with the enhancer-promoter looping directly mediated by the fusion protein. There is also a global enrichment of active enhancers within ZNF384 binding sites across the genome in ZNF384 -rearranged ALL cells. Downregulation of ZNF384 blunts FLT3 activation and decreases ALL cell sensitivity to FLT3 inhibitor gilteritinib in vitro. In patient-derived xenograft models of ZNF384 -rearranged ALL, gilteritinib exhibits significant anti-leukemia efficacy as a monotherapy in vivo. Collectively, our results provide insights into FLT3 regulation in ALL and point to potential genomics-guided targeted therapy for this patient population. Different molecular subtypes defined by specific gene rearrangements have been described for acute lymphoblastic leukaemia (ALL). Here, the authors show that ZNF384 fusion activates FLT3 expression conferring a therapeutic vulnerability for ZNF384- rearranged ALL subtype.

Nucleobase and nucleoside analogs (NNA) are widely used as anti-viral and anti-cancer agents, and NNA phosphorylation is essential for the activity of this class of drugs. Recently, diphosphatase NUDT15 was linked to thiopurine metabolism with NUDT15 polymorphism associated with drug toxicity in patients. Profiling NNA drugs, we identify acyclovir (ACV) and ganciclovir (GCV) as two new NNAs metabolized by NUDT15. NUDT15 hydrolyzes ACV and GCV triphosphate metabolites, reducing their effects against cytomegalovirus (CMV) in vitro. Loss of NUDT15 potentiates cytotoxicity of ACV and GCV in host cells. In hematopoietic stem cell transplant patients, the risk of CMV viremia following ACV prophylaxis is associated with NUDT15 genotype ( P  = 0.015). Donor NUDT15 deficiency is linked to graft failure in patients receiving CMV-seropositive stem cells ( P  = 0.047). In conclusion, NUDT15 is an important metabolizing enzyme for ACV and GCV, and NUDT15 variation contributes to inter-patient variability in their therapeutic effects. Nucleoside analogs (NNA), such as acyclovir (ACV) and ganciclovir (GCV), are widely used as anti-virals to treat herpes virus infection. Here, Nishii et al. show that diphosphatase NUDT15 hydrolyzes ACV and GCV, therewith reducing NNA activity in vitro and link NUDT15 variation to inter-patient variability in ACV and GCV therapeutic effects.

BACKGROUNDInterpatient differences in the accumulation of methotrexate's active polyglutamylated metabolites (MTXPGs) in leukemia cells influence its antileukemic effects.METHODSTo identify genomic and epigenomic and patient variables determining the intracellular accumulation of MTXPGs, we measured intracellular MTXPG levels in acute lymphoblastic leukemia (ALL) cells from 388 newly diagnosed patients after in vivo high-dose methotrexate (HDMTX) (1 g/m2) treatment, defined ALL subtypes, and assessed genomic and epigenomic variants influencing folate pathway genes (mRNA, miRNA, copy number alterations [CNAs], SNPs, single nucleotide variants [SNVs], CpG methylation).RESULTSWe documented greater than 100-fold differences in MTXPG levels, which influenced its antileukemic effects (P = 4 × 10-5). Three ALL subtypes had lower MTXPG levels (T cell ALL [T-ALL] and B cell ALL [B-ALL] with the TCF3-PBX1 or ETV6-RUNX1 fusions), and 2 subtypes had higher MTXPG levels (hyperdiploid and BCR-ABL like). The folate pathway genes SLC19A1, ABCC1, ABCC4, FPGS, and MTHFD1 significantly influenced intracellular MTXPG levels (P = 2.9 × 10-3 to 3.7 × 10-8). A multivariable model including the ALL subtype (P = 1.1 × 10-14), the SLC19A1/(ABCC1 + ABCC4) transporter ratio (P = 3.6 × 10-4), the MTX infusion time (P = 1.5 × 10-3), FPGS mRNA expression (P = 2.1 × 10-3), and MTX systemic clearance (P = 4.4 × 10-2) explained 42% of the variation in MTXPG accumulation (P = 1.1 × 10-38). Model simulations indicated that a longer infusion time (24 h vs. 4 h) was superior in achieving higher intracellular MTXPG levels across all subtypes if ALL.CONCLUSIONSThese findings provide insights into mechanisms underlying interpatient differences in intracellular accumulation of MTXPG in leukemia cells and its antileukemic effectsFUNDINGTHE National Cancer Institute (NCI) and the Institute of General Medical Sciences of the NIH, the Basque Government Programa Posdoctoral de Perfeccionamiento de Personal Investigador doctor, and the American Lebanese Syrian Associated Charities (ALSAC).

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy, and novel therapeutics are much needed. Profiling patient leukemia' drug sensitivities , we discovered that 44.4% of childhood and 16.7% of adult T-ALL cases exquisitely respond to dasatinib. Applying network-based systems pharmacology analyses to examine signal circuitry, we identified preTCR-LCK activation as the driver of dasatinib sensitivity, and T-ALL-specific LCK dependency was confirmed in genome-wide CRISPR-Cas9 screens. Dasatinib-sensitive T-ALLs exhibited high BCL-XL and low BCL2 activity and venetoclax resistance. Discordant sensitivity of T-ALL to dasatinib and venetoclax is strongly correlated with T-cell differentiation, particularly with the dynamic shift in LCK vs. BCL2 activation. Finally, single-cell analysis identified leukemia heterogeneity in LCK and BCL2 signaling and T-cell maturation stage, consistent with dasatinib response. In conclusion, our results indicate that developmental arrest in T-ALL drives differential activation of preTCR-LCK and BCL2 signaling in this leukemia, providing unique opportunities for targeted therapy.

Many state-of-the-art noisy-label learning methods rely on learning mechanisms that estimate the samples' clean labels during training and discard their original noisy labels. However, this approach prevents the learning of the relationship between images, noisy labels and clean labels, which has been shown to be useful when dealing with instance-dependent label noise problems. Furthermore, methods that do aim to learn this relationship require cleanly annotated subsets of data, as well as distillation or multi-faceted models for training. In this paper, we propose a new training algorithm that relies on a simple model to learn the relationship between clean and noisy labels without the need for a cleanly labelled subset of data. Our algorithm follows a 3-stage process, namely: 1) self-supervised pre-training followed by an early-stopping training of the classifier to confidently predict clean labels for a subset of the training set; 2) use the clean set from stage (1) to bootstrap the relationship between images, noisy labels and clean labels, which we exploit for effective relabelling of the remaining training set using semi-supervised learning; and 3) supervised training of the classifier with all relabelled samples from stage (2). By learning this relationship, we achieve state-of-the-art performance in asymmetric and instance-dependent label noise problems.

In this paper, we introduce Splatt3R, a pose-free, feed-forward method for in-the-wild 3D reconstruction and novel view synthesis from stereo pairs. Given uncalibrated natural images, Splatt3R can predict 3D Gaussian Splats without requiring any camera parameters or depth information. For generalizability, we build Splatt3R upon a ``foundation'' 3D geometry reconstruction method, MASt3R, by extending it to deal with both 3D structure and appearance. Specifically, unlike the original MASt3R which reconstructs only 3D point clouds, we predict the additional Gaussian attributes required to construct a Gaussian primitive for each point. Hence, unlike other novel view synthesis methods, Splatt3R is first trained by optimizing the 3D point cloud's geometry loss, and then a novel view synthesis objective. By doing this, we avoid the local minima present in training 3D Gaussian Splats from stereo views. We also propose a novel loss masking strategy that we empirically find is critical for strong performance on extrapolated viewpoints. We train Splatt3R on the ScanNet++ dataset and demonstrate excellent generalisation to uncalibrated, in-the-wild images. Splatt3R can reconstruct scenes at 4FPS at 512 x 512 resolution, and the resultant splats can be rendered in real-time.

As large language models (LLMs) evolve, their integration with 3D spatial data (3D-LLMs) has seen rapid progress, offering unprecedented capabilities for understanding and interacting with physical spaces. This survey provides a comprehensive overview of the methodologies enabling LLMs to process, understand, and generate 3D data. Highlighting the unique advantages of LLMs, such as in-context learning, step-by-step reasoning, open-vocabulary capabilities, and extensive world knowledge, we underscore their potential to significantly advance spatial comprehension and interaction within embodied Artificial Intelligence (AI) systems. Our investigation spans various 3D data representations, from point clouds to Neural Radiance Fields (NeRFs). It examines their integration with LLMs for tasks such as 3D scene understanding, captioning, question-answering, and dialogue, as well as LLM-based agents for spatial reasoning, planning, and navigation. The paper also includes a brief review of other methods that integrate 3D and language. The meta-analysis presented in this paper reveals significant progress yet underscores the necessity for novel approaches to harness the full potential of 3D-LLMs. Hence, with this paper, we aim to chart a course for future research that explores and expands the capabilities of 3D-LLMs in understanding and interacting with the complex 3D world. To support this survey, we have established a project page where papers related to our topic are organized and listed: https://github.com/ActiveVisionLab/Awesome-LLM-3D.

As large language models (LLMs) evolve, their integration with 3D spatial data (3D-LLMs) has seen rapid progress, offering unprecedented capabilities for understanding and interacting with physical spaces. This survey provides a comprehensive overview of the methodologies enabling LLMs to process, understand, and generate 3D data. Highlighting the unique advantages of LLMs, such as in-context learning, step-by-step reasoning, open-vocabulary capabilities, and extensive world knowledge, we underscore their potential to significantly advance spatial comprehension and interaction within embodied Artificial Intelligence (AI) systems. Our investigation spans various 3D data representations, from point clouds to Neural Radiance Fields (NeRFs). It examines their integration with LLMs for tasks such as 3D scene understanding, captioning, question-answering, and dialogue, as well as LLM-based agents for spatial reasoning, planning, and navigation. The paper also includes a brief review of other methods that integrate 3D and language. The meta-analysis presented in this paper reveals significant progress yet underscores the necessity for novel approaches to harness the full potential of 3D-LLMs. Hence, with this paper, we aim to chart a course for future research that explores and expands the capabilities of 3D-LLMs in understanding and interacting with the complex 3D world. To support this survey, we have established a project page where papers related to our topic are organized and listed: https://github.com/ActiveVisionLab/Awesome-LLM-3D.

The use of the scrum framework is now taking root in units throughout the Navy and Marine Corps as a result of the efforts of Mr. J.J. Sutherland and U.S. Navy Commander Jon Haase.2 These efforts have ignited a spark at the Centers of Adaptive Warfighting that has resulted in the creation of the Military Scrum Master Course and the training of over 2,000 Sailors and Marines.3 The resounding positive result from units that have taken the risk to implement Scrum is that they will never return to the traditional battle rhythm. The problem was that, despite their hard work, they only focused on the \"closest alligator to the boat,\" and they never could seem to get ahead. Because the Marines were so busy, communication within the section and across the battalion often fell by the wayside-always busy, but not consistently productive. Once complete, the team can start the five essential elements of the Scrum Framework: The Sprint, Sprint Planning, the Daily \"Scrum\" Standup, Sprint Review, and Retrospective.^ Sprints are a fixed length of time that is between one to four weeks. Often senior military leaders do not believe that a team can increase productivity as much as 300 to 1,200 times without losing quality.6 However, success in both military and civilian organizations have demonstrated that exponential increases in productivity are routine results of Scrum teams.