Ex Vivo Venetoclax Sensitivity Predicts Clinical Response in Acute Myeloid Leukemia in the Prospective VenEx Trial.

The BCL2 inhibitor venetoclax has shown promise for treating acute myeloid leukemia (AML). However, identifying patients likely to respond remains a challenge, especially for those with relapsed/refractory (R/R) disease. We evaluated the utility of ex vivo venetoclax sensitivity testing to predict treatment responses to venetoclax-azacitidine in a prospective, multicenter, phase 2 trial conducted by the Finnish AML Group (VenEx, NCT04267081). The trial recruited 104 participants with previously untreated (n=48), R/R (n=39) or previously treated secondary AML (sAML) (n=17). The primary endpoint was complete remission or complete remission with incomplete hematologic recovery (CR/CRi) rate in ex vivo sensitive trial participants during the first three therapy cycles. The key secondary endpoints included the correlations between ex vivo drug sensitivity, responses, and survival. Venetoclax sensitivity was successfully assessed in 102/104 participants, with results available within a median of three days from sampling. In previously untreated AML, ex vivo sensitivity corresponded to an 85% (34/40) CR/CRi rate, with a median overall survival (OS) of 28.7 months, compared to 5.5 months for ex vivo resistant patients (p = 0.002). For R/R/sAML, ex vivo sensitivity resulted in a 62% CR/CRi rate (21/34) and median OS of 9.7 versus 3.3 months for ex vivo resistant patients (p < 0.001). In univariate and multivariate analysis, ex vivo venetoclax sensitivity was the strongest predictor for a favorable treatment response and survival. The VenEx trial demonstrates the feasibility of integrating ex vivo drug testing into clinical practice to identify AML patients, particularly in the R/R setting, who benefit from venetoclax.

Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones.

Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success.

Genetic and functional analysis of Raynaud's syndrome implicates loci in vasculature and immunity.

Raynaud's syndrome is a dysautonomia where exposure to cold causes vasoconstriction and hypoxia, particularly in the extremities. We performed meta-analysis in four cohorts and discovered eight loci (ADRA2A, IRX1, NOS3, ACVR2A, TMEM51, PCDH10-DT, HLA, and RAB6C) where ADRA2A, ACVR2A, NOS3, TMEM51, and IRX1 co-localized with expression quantitative trait loci (eQTLs), particularly in distal arteries. CRISPR gene editing further showed that ADRA2A and NOS3 loci modified gene expression and in situ RNAscope clarified the specificity of ADRA2A in small vessels and IRX1 around small capillaries in the skin. A functional contraction assay in the cold showed lower contraction in ADRA2A-deficient and higher contraction in ADRA2A-overexpressing smooth muscle cells. Overall, our study highlights the power of genome-wide association testing with functional follow-up as a method to understand complex diseases. The results indicate temperature-dependent adrenergic signaling through ADRA2A, effects at the microvasculature by IRX1, endothelial signaling by NOS3, and immune mechanisms by the HLA locus in Raynaud's syndrome.

Targeting CD33+ Acute Myeloid Leukemia with GLK-33, a Lintuzumab-Auristatin Conjugate with a Wide Therapeutic Window.

CD33 (Siglec-3) is a cell surface receptor expressed in approximately 90% of acute myeloid leukemia (AML) blasts, making it an attractive target for therapy of AML. Although previous CD33-targeting antibody-drug conjugates (ADC) like gemtuzumab ozogamicin (GO, Mylotarg) have shown efficacy in AML treatment, they have suffered from toxicity and narrow therapeutic window. This study aimed to develop a novelADCwith improved tolerability and a wider therapeutic window. GLK-33 consists of the anti-CD33 antibody lintuzumab and eight mavg-MMAU auristatin linkerpayloads per antibody. The experimental methods included testing in cell cultures, patient-derived samples, mouse xenograft models, and rat toxicology studies. GLK-33 exhibited remarkable efficacy in reducing cell viability within CD33-positive leukemia cell lines and primary AML samples. Notably, GLK-33 demonstrated antitumor activity at single dose as low as 300 mg/kg in mice, while maintaining tolerability at single dose of 20 to 30 mg/kg in rats. In contrast with both GO and lintuzumab vedotin, GLK-33 exhibited a wide therapeutic window and activity against multidrug-resistant cells. The development of GLK-33 addresses the limitations of previous ADCs, offering a wider therapeutic window, improved tolerability, and activity against drug-resistant leukemia cells. These findings encourage further exploration of GLK-33 in AML through clinical trials.

Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling.

Acute myeloid leukemia (AML), a heterogeneous and aggressive blood cancer, does not respond well to single-drug therapy. A combination of drugs is required to effectively treat this disease. Computational models are critical for combination therapy discovery due to the tens of thousands of two-drug combinations, even with approved drugs. While predicting synergistic drugs is the focus of current methods, few consider drug efficacy and potential toxicity, which are crucial for treatment success. To find effective new drug candidates, we constructed a bipartite network using patient-derived tumor samples and drugs. The network is based on drug-response screening and summarizes all treatment response heterogeneity as drug response weights. This bipartite network is then projected onto the drug part, resulting in the drug similarity network. Distinct drug clusters were identified using community detection methods, each targeting different biological processes and pathways as revealed by enrichment and pathway analysis of the drugs' protein targets. Four drugs with the highest efficacy and lowest toxicity from each cluster were selected and tested for drug sensitivity using cell viability assays on various samples. Results show that ruxolitinib-ulixertinib and sapanisertib-LY3009120 are the most effective combinations with the least toxicity and the best synergistic effect on blast cells. These findings lay the foundation for personalized and successful AML therapies, ultimately leading to the development of drug combinations that can be used alongside standard first-line AML treatment.

Monosomy 7/del(7q) cause sensitivity to inhibitors of nicotinamide phosphoribosyltransferase in acute myeloid leukemia.

ABSTRACT: Monosomy 7 and del(7q) (-7/-7q) are frequent chromosomal abnormalities detected in up to 10% of patients with acute myeloid leukemia (AML). Despite unfavorable treatment outcomes, no approved targeted therapies exist for patients with -7/-7q. Therefore, we aimed to identify novel vulnerabilities. Through an analysis of data from ex vivo drug screens of 114 primary AML samples, we discovered that -7/-7q AML cells are highly sensitive to the inhibition of nicotinamide phosphoribosyltransferase (NAMPT). NAMPT is the rate-limiting enzyme in the nicotinamide adenine dinucleotide salvage pathway. Mechanistically, the NAMPT gene is located at 7q22.3, and deletion of 1 copy due to -7/-7q results in NAMPT haploinsufficiency, leading to reduced expression and a therapeutically targetable vulnerability to the inhibition of NAMPT. Our results show that in -7/-7q AML, differentiated CD34+CD38+ myeloblasts are more sensitive to the inhibition of NAMPT than less differentiated CD34+CD38- myeloblasts. Furthermore, the combination of the BCL2 inhibitor venetoclax and the NAMPT inhibitor KPT-9274 resulted in the death of significantly more leukemic blasts in AML samples with -7/-7q than the NAMPT inhibitor alone. In conclusion, our findings demonstrate that AML with -7/-7q is highly sensitive to NAMPT inhibition, suggesting that NAMPT inhibitors have the potential to be an effective targeted therapy for patients with monosomy 7 or del(7q).

Robust scoring of selective drug responses for patient-tailored therapy selection.

Most patients with advanced malignancies are treated with severely toxic, first-line chemotherapies. Personalized treatment strategies have led to improved patient outcomes and could replace one-size-fits-all therapies, yet they need to be tailored by testing of a range of targeted drugs in primary patient cells. Most functional precision medicine studies use simple drug-response metrics, which cannot quantify the selective effects of drugs (i.e., the differential responses of cancer cells and normal cells). We developed a computational method for selective drug-sensitivity scoring (DSS), which enables normalization of the individual patient's responses against normal cell responses. The selective response scoring uses the inhibition of noncancerous cells as a proxy for potential drug toxicity, which can in turn be used to identify effective and safer treatment options. Here, we explain how to apply the selective DSS calculation for guiding precision medicine in patients with leukemia treated across three cancer centers in Europe and the USA; the generic methods are also widely applicable to other malignancies that are amenable to drug testing. The open-source and extendable R-codes provide a robust means to tailor personalized treatment strategies on the basis of increasingly available ex vivo drug-testing data from patients in real-world and clinical trial settings. We also make available drug-response profiles to 527 anticancer compounds tested in 10 healthy bone marrow samples as reference data for selective scoring and de-prioritization of drugs that show broadly toxic effects. The procedure takes <60 min and requires basic skills in R.

Using Proteomics Data to Identify Personalized Treatments in Multiple Myeloma: A Machine Learning Approach.

This paper describes a machine learning (ML) decision support system to provide a list of chemotherapeutics that individual multiple myeloma (MM) patients are sensitive/resistant to, based on their proteomic profile. The methodology used in this study involved understanding the parameter space and selecting the dominant features (proteomics data), identifying patterns of proteomic profiles and their association to the recommended treatments, and defining the decision support system of personalized treatment as a classification problem. During the data analysis, we compared several ML algorithms, such as linear regression, Random Forest, and support vector machines, to classify patients as sensitive/resistant to therapeutics. A further analysis examined data-balancing techniques that emerged due to the small cohort size. The results suggest that utilizing proteomics data is a promising approach for identifying effective treatment options for patients with MM (reaching on average an accuracy of 81%). Although this pilot study was limited by the small patient cohort (39 patients), which restricted the training and validation of the explored ML solutions to identify complex associations between proteins, it holds great promise for developing personalized anti-MM treatments using ML approaches.

An ischemic myelopathy case series: Flaccid paraplegia following a spike ball save and numbness while walking normally.

Spinal cord infarctions in children are rare and early magnetic resonance imaging studies are often negative. A high clinical suspicion must be maintained to identify stroke and initiate workup for underlying etiology to suggest appropriate treatment. We present two cases of spinal cord infarction without major preceding trauma. The first was caused by disc herniation and external impingement of a radiculomedullary artery and the second was due to fibrocartilaginous embolism with classic imaging findings of ventral and dorsal cord infarctions, respectively. These cases were treated conservatively with diagnostic workup and aspirin, though additional treatments which can be considered with prompt diagnosis are also explored in our discussion. Both cases recovered the ability to ambulate independently within months. Case 1 is attending college and ambulates campus with a single-point cane. Case 2 ambulates independently, though has some difficulty with proprioception of the feet so uses wheelchairs for long-distance ambulation.