Clinical Outcome of t-AML with Respect to Genetic Abnormalities and Treatment Intensity - Single Center Analysis over the Past Two Decades

Background: Therapy-related acute myeloid leukemia (t-AML) represents emerging challenge of the modern oncology as a life-threatening complication of cytotoxic therapy. Disease characterises poor prognosis and presence of adverse cytogenetic and genetic abnormalities. The goal of the study: Clinical outcome of t-AML patients with respect to genetic changes and treatment intensity. Patients and methods: Retrospective analysis of all consecutive AML patients treated in years 2000-2021 in one hematological center was performed. Diagnosis of t-AML was established according to WHO 2016 criteria. Overall survival (OS) and progression free survival (PFS) was defined to evaluate treatment outcomes only within t-AML patients undergoing intensive treatment (standard induction/consolidation; allogeneic cell transplantation (alloHCT) if eligible). Results: Among 743 AML patients 60 (8.1%) were diagnosed as t-AML (38 woman) with median age 57 years. Solid tumors (ST) preceded t-AML in 63.3%, hematological neoplasms (HN) in 36.7%. Majority of t-AML was preceded by breast cancer (30.0%), Hodgkin Lymphoma (11.7%), non-Hodgkin Lymphoma (10.0%) and ovarian cancer (10.0%). Median latency time for ST and HN subgroups was 5 vs 7 years respectively (P = .036). Previous cytotoxic therapy consisted of chemotherapy, radiotherapy or combination in 56.6%, 18.3% and 25.0% (autologous cell transplantation was performed in 54.5% of HN). Cytogenetic and molecular biology analysis was performed in 44 and 27 of t-AML respectively. Cytogenetic abnormalities, complex karyotype and normal karyotype occurred in 78.9%, 28.9% and 15.8% patients. KMT2A, RUNX1-RUNX1T1 and PML-RARA rearrangement was found in 21.1%, 18.4% and 7.9% of t-AML. FLT3-ITD, FLT-TKD, NPM1 and C-KIT DNA sequence variant occurred as follows: 14.8%, 7.4%, 3.7% and 3.7% correspondingly. Three pathogenic TP53 DNA sequence variants were detected in t-AML patients: c.711G>A, c.704A>G and c.989T>C (analysis performed on 20 t-AML patients). According ELN2017 genetic risk stratification patients were classified as adverse, intermediate and favorable in 51.4%, 35.1% and 13.5% respectively. Intensive treatment was implemented in 48 patients including alloHCT in 23 of them. Median OS and PFS was 15 and 8 months respectively for whole treated group. Median OS in t-AML undergoing intensive chemotherapy only vs alloHCT was 7 vs 47 months (P = .0025) with 12-year OS after alloHCT- 21.1% (Fig.1A). Among therapy-related acute promyelocytic leukemia (t-APL) patients median OS was not reached, without alloHCT. Median OS was higher for t-AML patients younger than 65 years than older ones: 20 vs 13 months respectively (P = .048) (Fig.1B). Among t-AML median OS in subgroup with adverse ELN 2017 vs intermediate and favorable ELN 2017 was 15 vs 40 months (P = .037) with 5-years OS 8.2% vs 41.0% (Fig.1C). In multivariate Cox proportional hazard regression model alloHCT was the only factor significantly influencing OS (HR = 0.16, 95% CI = 0.05-0.56, P = .004). All patients with TP53 mutations were intensively treated, one patient underwent alloHCT. 66.6% of patients had complex karyotype and any co-occuring DNA sequence variant was detected. Importantly, c.704A>G and c.989T>C TP53 DNA sequence variants were not previously described in AML according Catalogue of Somatic Mutations In Cancer database. Median OS in t-AML with TP53 mutation vs without was 4 vs 7 months (P = .398) (Fig.1D). Conslusions: Our study brings detailed analysis of clinical outcome of t-AML. Patients with t-AML undergoing intensive treatment, younger than 65 years and with t-APL have significantly higher OS rates. On the contrary t-AML patients classified as adverse genetic ELN2017 subgroup have poorer OS rates. Treatment strategy in t-AML should rely on performing alloHCT possibly soon. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Effects of Selection at Linked Sites on Patterns of Genetic Variability

Patterns of variation and evolution at a given site in a genome can be strongly influenced by the effects of selection at genetically linked sites. In particular, the recombination rates of genomic regions correlate with their amount of within-population genetic variability, the degree to which the frequency distributions of DNA sequence variants differ from their neutral expectations, and the levels of adaptation of their functional components. We review the major population genetic processes that are thought to lead to these patterns, focusing on their effects on patterns of variability: selective sweeps, background selection, associative overdominance, and Hill–Robertson interference among deleterious mutations. We emphasize the difficulties in distinguishing among the footprints of these processes and disentangling them from the effects of purely demographic factors such as population size changes. We also discuss how interactions between selective and demographic processes can significantly affect patterns of variability within genomes.

Single Tube qPCR detection and quantitation of hotspot mutations down to 0.01% VAF

Clinically and biologically, rare DNA sequence variants are significant and informative. However, existing detection technologies are either complex in workflow, or restricted in the limit of detection (LoD), or do not allow for multiplexing. Blocker displacement amplification (BDA) method can stably and effectively detect and enrich multiple rare variants with LoD around 0.1% variant allele fraction (VAF). Nonetheless, the detailed mutation information has to be identified by additional sequencing technologies. Here, we present allele-specific BDA (As-BDA), a method combining BDA with allele-specific TaqMan (As-TaqMan) probes for effective variant enrichment and simultaneous SNV profiling. We demonstrated that As-BDA could detect mutations down to 0.01% VAF. Further, As-BDA could detect up to four mutations with low to 0.1% VAF per reaction using only 15 ng DNA input. The median error of As-BDA in VAF determination is approximately 9.1%. Comparison experiments using As-BDA and droplet digital PCR (ddPCR) on peripheral blood mononuclear cell (PBMC) clinical samples showed 100% concordance for samples with mutations at 3 0.1% VAF. Hence, we have shown that As-BDA can achieve simultaneous enrichment and identification of multiple targeted mutations within the same reaction with high clinical sensitivity and specificity, thus helpful for clinical diagnosis.

Variants in ACE2 and TMPRSS2 Genes Are Not Major Determinants of COVID-19 Severity in UK Biobank Subjects

It is plausible that variants in the ACE2 and TMPRSS2 genes might contribute to variation in COVID-19 severity and that these could explain why some people become very unwell whereas most do not. Exome sequence data was obtained for 49,953 UK Biobank subjects, of whom 82 had tested positive for SARS-CoV-2 and could be presumed to have severe disease. A weighted burden analysis was carried out using SCOREASSOC to determine whether there were differences between these cases and the other sequenced subjects in the overall burden of rare, damaging variants in ACE2 or TMPRSS2. There were no statistically significant differences in weighted burden scores between cases and controls for either gene. There were no individual DNA sequence variants with a markedly different frequency between cases and controls. Whether there are small effects on severity, or whether there might be rare variants with major effect sizes, would require studies in much larger samples. Genetic variants affecting the structure and function of the ACE2 and TMPRSS2 proteins are not the main explanation for why some people develop severe symptoms in response to infection with SARS-CoV-2. This research was conducted using the UK Biobank Resource.

Validation of lipid-related therapeutic targets for coronary heart disease prevention using human genetics

Drug target Mendelian randomization (MR) studies use DNA sequence variants in or near a gene encoding a drug target, that alter its expression or function, as a tool to anticipate the effect of drug action on the same target. Here, we applied MR to prioritize drug targets for their causal relevance for coronary heart disease (CHD). The targets were further prioritized using genetic co-localization, protein expression profiles from the Human Protein Atlas and, for targets with a licensed drug or an agent in clinical development, by sourcing data from the British National Formulary and clinicaltrials.gov. Out of the 341 drug targets identified through their association with circulating blood lipids (HDL-C, LDL-C and triglycerides), we were able to robustly prioritize 30 targets that might elicit beneficial treatment effects in the prevention or treatment of CHD. The prioritized list included NPC1L1 and PCSK9, the targets of licensed drugs whose efficacy has been already proven in clinical trials. To conclude, we discuss how this approach can be generalized to other targets, disease biomarkers and clinical end-points to help prioritize and validate targets during the drug development process.

Genetic architecture of host proteins involved in SARS-CoV-2 infection

Understanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver (https://omicscience.org/apps/covidpgwas/).

Genetic laboratory techniques

A modern genomic laboratory will offer a range of diagnostic techniques. A biochemical genetic laboratory may also be based in selected centres. In this chapter, a brief account of diagnostic techniques that are useful for inherited cardiac diseases is provided. It covers variant classification and techniques that can detect DNA sequence variants, next generation sequencing, Sanger sequencing, copy number analysis (including whole genome copy number analysis), targeted dosage techniques, and known variant detection. It then gives examples of genetic laboratory reports in the context of investigating potential inherited cardiac diseases, with subsequent familial tests if required.