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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.

Background: This study aims to explore patients' with acute myeloid leukemia perceptions about precision medicine and their preferences for involvement in this new area of shared decision-making. Methods: Individual semi-structured interviews were conducted in Finland, Italy and Germany (n = 16). The study population included patients aged 24-79 years. Interviews were analyzed with thematic content analysis. Results: Patient's perceived lack of knowledge as a barrier for their involvement in decision-making. Treatment decisions were often made rapidly based on the patient's intuition and trust for the physician rather than on information, in situations that decrease the patient's decision capacity. The patients emphasized that they are in a desperate situation that makes them willing to accept treatment with low probabilities of being cured. Conclusions: The study raised important issues regarding patients' understanding of precision medicine and challenges concerning how to involve patients in medical decision-making. Although technical advances were viewed positively, the role of the physician as an expert and person-of-trust cannot be replaced. Practice implications: Regardless of patients' preferences for involvement in decision-making, information plays a crucial role for patients' perceived involvement in their care. The concepts related to precision medicine are complex and will imply challenges to patient education.

Aminopeptidases, which catalyze the cleavage of amino acids from the amino terminus of proteins, are widely distributed in the natural world and play a crucial role in cellular processes and functions, including metabolism, signaling, angiogenesis, and immunology. They are also involved in the homeostasis of amino acids and proteins that are required for cellular proliferation. Tumor cells are highly dependent on the exogenous supply of amino acids for their survival, and overexpression of aminopeptidase facilitates rapid tumor cell proliferation. In addition, clinical studies have demonstrated that patients with cancers with high aminopeptidase expression often have poorer outcomes. Emerging evidence supports the rationale of inhibiting aminopeptidase activity as a targeted approach for novel treatment options, as limiting the availability of amino acids can be selectively lethal to tumor cells. While there are agents that directly target aminopeptidases that demonstrate potential as cancer therapies, such as bestatin and tosedostat, more selective and more targeted therapeutic approaches are needed. This article specifically looks at the biological role of aminopeptidases in both normal and cancer processes, and their potential as a biological target for future therapeutic strategies. When examining previous publications, most do not cover aminopeptidases and their role in cancer processes. Aminopeptidases play a vital role in cell processes and functions; however, their overexpression may lead to a rapid proliferation of tumor cells. Emerging evidence supports the rationale of leveraging aminopeptidase activity as a targeted approach for new oncological treatments. This article specifically looks at the biological role of aminopeptidases in both normal and cancer processes, and their potential as a biological target for future therapeutic strategies.

Abstract Tuberculosis is a significant public health concern resulting in the death of over 1 million individuals each year worldwide. While treatment options and vaccines exist, a substantial number of infections still remain untreated or are caused by treatment resistant strains. Therefore, it is important to identify mechanisms that contribute to risk and prognosis of tuberculosis as this may provide tools to understand disease mechanisms and provide novel treatment options for those with severe infection. Our goal was to identify genetic risk factors that contribute to the risk of tuberculosis and to understand biological mechanisms and causality behind the risk of tuberculosis. A total of 1895 individuals in the FinnGen study had International Classification of Diseases-based tuberculosis diagnosis. Genome-wide association study analysis identified genetic variants with statistically significant association with tuberculosis at the human leukocyte antigen (HLA) region (P < 5e−8). Fine mapping of the HLA association provided evidence for one protective haplotype tagged by HLA DQB1*05:01 (P = 1.82E−06, OR = 0.81 [CI 95% 0.74–0.88]), and predisposing alleles tagged by HLA DRB1*13:02 (P = 0.00011, OR = 1.35 [CI 95% 1.16–1.57]). Furthermore, genetic correlation analysis showed association with earlier reported risk factors including smoking (P < 0.05). Mendelian randomization supported smoking as a risk factor for tuberculosis (inverse-variance weighted P < 0.05, OR = 1.83 [CI 95% 1.15–2.93]) with no significant evidence of pleiotropy. Our findings indicate that specific HLA alleles associate with the risk of tuberculosis. In addition, lifestyle risk factors such as smoking contribute to the risk of developing tuberculosis.

The BCL-2 inhibitor venetoclax has revolutionized the treatment of acute myeloid leukemia (AML) in patients not benefiting from intensive chemotherapy. Nevertheless, treatment failure remains a challenge, and predictive markers are needed, particularly for relapsed or refractory AML. Ex vivo drug sensitivity testing may correlate with outcomes, but its prospective predictive value remains unexplored. Here we report the results of the first stage of the prospective phase II VenEx trial evaluating the utility and predictiveness of venetoclax sensitivity testing using different cell culture conditions and cell viability assays in patients receiving venetoclax-azacitidine. Participants with de novo AML ineligible for intensive chemotherapy, relapsed or refractory AML, or secondary AML were included. The primary endpoint was the treatment response in participants showing ex vivo sensitivity and the key secondary endpoints were the correlation of sensitivity with responses and survival. Venetoclax sensitivity testing was successful in 38/39 participants. Experimental conditions significantly influenced the predictive accuracy. Blast-specific venetoclax sensitivity measured in conditioned medium most accurately correlated with treatment outcomes; 88% of sensitive participants achieved a treatment response. The median survival was significantly longer for participants who were ex vivo-sensitive to venetoclax (14.6 months for venetoclax-sensitive patients vs. 3.5 for venetoclax-insensitive patients, P<0.001). This analysis illustrates the feasibility of integrating drug-response profiling into clinical practice and demonstrates excellent predictivity.