CONTEXTS.py (CS.py): A supervised contextual post-classification method to access multiple dimensions of complex geospatial objects.

The qualitative dimensions of visible features in space can be captured by connecting spatial configurations arranged in a variety of different ways to diverse conceptual spaces. By conceptual spaces, we intend mental concepts describing specific spatial configurations present in a geographical area, defined by the contextual relationships among their constitutive elements. This paper presents a new supervised post-classification method allowing the extraction of semantically complex spatial objects from a single image of the Earth as, for instance, diverse conceptual spaces referring to multiple dimensions of land use (temporal, cultural, social, etc.). Computationally, our method is operationalised by CONTEXTS.py (CS.py), a plugin written in Python for QGIS. CS.py relies on training areas, defined by the user at diverse scales, to identify and extract in the input image conceptual spaces whose spatial contexts have the same spatial features present in the training areas. Applied to a case study on the island of Sicily, where millennial land use dynamics have resulted in a mosaic landscape, CS.py could detect from an orthophoto diverse conceptual spaces of land use in an area ordinarily classified as one land cover, thus expanding the capabilities of geospatial analysis to reach additional qualitative dimensions of information from image data.•CS.py simplifies a supervised contextual post-classification routine in an easy-to-use, practical and accessible QGIS plugin;•CS.py joins a family of tools for supervised object-based classification (e.g. OTB, GRASS), providing, additionally, the possibility to include contextual information as spatial criteria to train the classification routine.•CS.py has broad applications in different disciplines investigating landscape from quantitative and qualitative perspectives, allowing both, as in multiple environments.

Comprehensive Genomic Profiling Alters Clinical Diagnoses in a Significant Fraction of Tumors Suspicious of Sarcoma.

PURPOSE: Tumor classification is a key component in personalized cancer care. For soft tissue and bone tumors, this classification is currently based primarily on morphology assessment and immunohistochemical staining. However, these standard-of-care methods can pose challenges for pathologists. We therefore assessed how whole-genome and whole-transcriptome sequencing (WGTS) impacted tumor classification and clinical management when interpreted together with histomorphology. EXPERIMENTAL DESIGN: We prospectively evaluated WGTS in routine diagnostics of 200 soft tissue and bone tumors suspicious for malignancy, including DNA and RNA isolation from the tumor, and DNA isolation from a peripheral blood sample or any non-tumor tissue. RESULTS: Based on specific genomic alterations or absence of presumed findings, WGTS resulted in reclassification of 7% (13/197) of the histopathological diagnoses. Four cases were downgraded from low-grade sarcomas to benign lesions, and two cases were reclassified as metastatic malignant melanomas. Fusion genes associated with specific tumor entities were found in 30 samples. For malignant soft tissue and bone tumors, we identified treatment relevant variants in 15% of cases. Germline pathogenic variants associated to a hereditary cancer syndrome were found in 22 participants (11%). CONCLUSION: We conclude that WGTS provides an important dimension of data which aids in the classification of soft tissue and bone tumors, correcting a significant fraction of clinical diagnoses, and identifies molecular targets relevant for precision medicine. However, genetic findings need to be evaluated in their morphopathological context, just as germline findings need to be evaluated in the context of patient phenotype and family history.

Plasma bile acids in association with Crohn's disease.

BACKGROUND: In addition to facilitating lipid digestions, bile acids (BA) are signalling molecules acting on receptors on immune cells and along the gastrointestinal (GI) tract. The aim of this study was to assess if altered bile acid profiles in plasma are associated with Crohn's disease (CD). METHOD: This cross-sectional study included individuals (aged ≥18 years) referred for colonoscopy at a tertiary centre in Stockholm between 2016 and 2019. All participants received bowel preparation, completed a lifestyle questionnaire and provided blood samples for analysis. During colonoscopy, severity of disease was graded, and biopsies were taken from colonic mucosa. In the current substudy, 88 individuals with CD and 88 age-matched controls were selected for analysis of BA in plasma with ultra performance liquid chromatography (UPLC). Linear regression models were then used to compare mean bile acid concentrations and concentration ratios between CD and controls. RESULTS: Individuals with CD had lower plasma concentrations of the majority of secondary BA compared to controls, in total CD/CC ratio 0.60 (SE 0.12), p = 0.001. The most prominent observations were lower levels of deoxycolic acid derivates and lithocolic acid derivates among participants with CD. Moreover, plasma concentration for secondary BA among participants with active CD was significantly lower compared to those with CD in remission, CD active/CD remission ratio 0.65 (SE 0.11), p < 0.002. CONCLUSION: Crohn's disease may be associated with altered plasma bile acid composition. The significance of colonic bacterial diversity in this context needs to be investigated in further studies.

It is known that Crohn's disease is associated with dysbiosis in the gut microbiota and that primary bile acids are transformed to secondary bile acids by bacterial enzymes in the gut before reabsorbed and transported back to the liver.In this cross-sectional study, Crohn's disease was associated with lower concentrations of secondary bile acids in blood plasmaThe findings should encourage further studies the role of the gut microbiome and bile acid metabolism in development of Crohn's disease and bile acid profile as a biomarker for bowel inflammation.

Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer.

No consensus strategies exist for prognosticating metastatic castration-resistant prostate cancer (mCRPC). Circulating tumor DNA fraction (ctDNA%) is increasingly reported by commercial and laboratory tests but its utility for risk stratification is unclear. Here, we intersect ctDNA%, treatment outcomes, and clinical characteristics across 738 plasma samples from 491 male mCRPC patients from two randomized multicentre phase II trials and a prospective province-wide blood biobanking program. ctDNA% correlates with serum and radiographic metrics of disease burden and is highest in patients with liver metastases. ctDNA% strongly predicts overall survival, progression-free survival, and treatment response independent of therapeutic context and outperformed established prognostic clinical factors. Recognizing that ctDNA-based biomarker genotyping is limited by low ctDNA% in some patients, we leverage the relationship between clinical prognostic factors and ctDNA% to develop a clinically-interpretable machine-learning tool that predicts whether a patient has sufficient ctDNA% for informative ctDNA genotyping (available online: https://www.ctDNA.org ). Our results affirm ctDNA% as an actionable tool for patient risk stratification and provide a practical framework for optimized biomarker testing.

AR and PI3K Genomic Profiling of Cell-free DNA Can Identify Poor Responders to Lutetium-177-PSMA Among Patients with Metastatic Castration-resistant Prostate Cancer.

Lutetium-177 prostate-specific membrane antigen radioligands (177Lu-PSMA) are new therapeutic agents for the treatment of metastatic castration-resistant prostate cancer (mCRPC). We evaluated the prognostic value of circulating tumour DNA (ctDNA) profiling in patients with mCRPC starting treatment with 177Lu-PSMA I&T. Between January 2020 and October 2022, patients with late-stage mCRPC (n = 57) were enrolled in a single-centre observational cohort study. Genomic alterations in the AR gene, PI3K signalling pathway, TP53, and TMPRSS2-ERG were associated with progression-free survival (PFS) on Kaplan-Meier and multivariable Cox regression analyses. Median PFS of 3.84 mo (95% confidence interval [CI] 3.3-5.4) was observed, and 21/56 (37.5%) evaluable patients experienced a prostate-specific antigen response of ≥50% during treatment. Among 46 patients who provided a blood sample for profiling before 177Lu-PSMA treatment. ctDNA was detected in 39 (84.8%); higher ctDNA was correlated with shorter PFS. Genomic structural rearrangements in the AR gene (hazard ratio [HR] 9.74, 95% confidence interval [CI] 2.4-39.5; p = 0.001) and alterations in the PI3K signalling pathway (HR 3.58, 95% CI 1.41-9.08; p = 0.007) were independently associated with poor 177Lu-PSMA prognosis on multivariable Cox regression. Prospective evaluation of these associations in biomarker-driven trials is warranted. Patient summary: We examined cell-free DNA in blood samples from patients with advanced metastatic prostate cancer who started treatment with lutetium-177-PSMA, a new radioligand therapy. We found that patients with genetic alterations in the androgen receptor gene or PI3K pathway genes did not experience a lasting benefit from lutetium-177-PSMA.

Prognostic Value of Low-Pass Whole Genome Sequencing of Circulating Tumor DNA in Metastatic Castration-Resistant Prostate Cancer.

BACKGROUND: Multiple treatments are available for metastatic castration-resistant prostate cancer (mCRPC), including androgen receptor signaling inhibitors (ARSI) enzalutamide and abiraterone, but therapy resistance remains a major clinical obstacle. We examined the clinical utility of low-pass whole-genome sequencing (LPWGS) of circulating tumor DNA (ctDNA) for prognostication in mCRPC. METHODS: A total of 200 plasma samples from 143 mCRPC patients collected at the start of first-line ARSI treatment (baseline) and at treatment termination (n = 57, matched) were analyzed by LPWGS (median: 0.50X) to access ctDNA% and copy number alteration (CNA) patterns. The best confirmed prostate specific antigen (PSA) response (≥50% decline [PSA50]), PSA progression-free survival (PFS), and overall survival (OS) were used as endpoints. For external validation, we used plasma LPWGS data from an independent cohort of 70 mCRPC patients receiving first-line ARSI. RESULTS: Baseline ctDNA% ranged from ≤3.0% to 73% (median: 6.6%) and CNA burden from 0% to 82% (median: 13.1%) in the discovery cohort. High ctDNA% and high CNA burden at baseline was associated with poor PSA50 response (P = 0.0123/0.0081), poor PFS (P < 0.0001), and poor OS (P < 0.0001). ctDNA% and CNA burden was higher at PSA progression than at baseline in 32.7% and 42.3% of the patients. High ctDNA% and high CNA burden at baseline was also associated with poor PFS and OS (P ≤ 0.0272) in the validation cohort. CONCLUSIONS: LPWGS of ctDNA provides clinically relevant information about the tumor genome in mCRPC patients. Using LPWGS data, we show that high ctDNA% and CNA burden at baseline is associated with short PFS and OS in 2 independent cohorts.

Unsupervised detection of fragment length signatures of circulating tumor DNA using non-negative matrix factorization.

Sequencing of cell-free DNA (cfDNA) is currently being used to detect cancer by searching both for mutational and non-mutational alterations. Recent work has shown that the length distribution of cfDNA fragments from a cancer patient can inform tumor load and type. Here, we propose non-negative matrix factorization (NMF) of fragment length distributions as a novel and completely unsupervised method for studying fragment length patterns in cfDNA. Using shallow whole-genome sequencing (sWGS) of cfDNA from a cohort of patients with metastatic castration-resistant prostate cancer (mCRPC), we demonstrate how NMF accurately infers the true tumor fragment length distribution as an NMF component - and that the sample weights of this component correlate with ctDNA levels (r=0.75). We further demonstrate how using several NMF components enables accurate cancer detection on data from various early stage cancers (AUC = 0.96). Finally, we show that NMF, when applied across genomic regions, can be used to discover fragment length signatures associated with open chromatin.

Population pharmacokinetics of apramycin from first-in-human plasma and urine data to support prediction of efficacious dose.

BACKGROUND: Apramycin is under development for human use as EBL-1003, a crystalline free base of apramycin, in face of increasing incidence of multidrug-resistant bacteria. Both toxicity and cross-resistance, commonly seen for other aminoglycosides, appear relatively low owing to its distinct chemical structure. OBJECTIVES: To perform a population pharmacokinetic (PPK) analysis and predict an efficacious dose based on data from a first-in-human Phase I trial. METHODS: The drug was administered intravenously over 30 min in five ascending-dose groups ranging from 0.3 to 30 mg/kg. Plasma and urine samples were collected from 30 healthy volunteers. PPK model development was performed stepwise and the final model was used for PTA analysis. RESULTS: A mammillary four-compartment PPK model, with linear elimination and a renal fractional excretion of 90%, described the data. Apramycin clearance was proportional to the absolute estimated glomerular filtration rate (eGFR). All fixed effect parameters were allometrically scaled to total body weight (TBW). Clearance and steady-state volume of distribution were estimated to 5.5 L/h and 16 L, respectively, for a typical individual with absolute eGFR of 124 mL/min and TBW of 70 kg. PTA analyses demonstrated that the anticipated efficacious dose (30 mg/kg daily, 30 min intravenous infusion) reaches a probability of 96.4% for a free AUC/MIC target of 40, given an MIC of 8 mg/L, in a virtual Phase II patient population with an absolute eGFR extrapolated to 80 mL/min. CONCLUSIONS: The results support further Phase II clinical trials with apramycin at an anticipated efficacious dose of 30 mg/kg once daily.

Using deep learning to detect patients at risk for prostate cancer despite benign biopsies.

Routine transrectal ultrasound-guided systematic prostate biopsy only samples a small volume of the prostate and tumors between biopsy cores can be missed, leading to low sensitivity to detect clinically relevant prostate cancers (PCa). Deep learning may enable detection of PCa despite benign biopsies. We included 14,354 hematoxylin-eosin stained benign prostate biopsies from 1,508 men in two groups: men without established PCa diagnosis and men with at least one core biopsy diagnosed with PCa. A 10-Convolutional Neural Network ensemble was optimized to distinguish benign biopsies from benign men or patients with PCa. Area under the receiver operating characteristic curve was estimated at 0.739 (bootstrap 95% CI:0.682-0.796) on man level in the held-out test set. At the specificity of 0.90, the model sensitivity was 0.348. The proposed model can detect men with risk of missed PCa and has the potential to reduce false negatives and to indicate men who could benefit from rebiopsies.

Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks.

MOTIVATION: Molecular phenotyping by gene expression profiling is central in contemporary cancer research and in molecular diagnostics but remains resource intense to implement. Changes in gene expression occurring in tumours cause morphological changes in tissue, which can be observed on the microscopic level. The relationship between morphological patterns and some of the molecular phenotypes can be exploited to predict molecular phenotypes from routine haematoxylin and eosin-stained whole slide images (WSIs) using convolutional neural networks (CNNs). In this study, we propose a new, computationally efficient approach to model relationships between morphology and gene expression. RESULTS: We conducted the first transcriptome-wide analysis in prostate cancer, using CNNs to predict bulk RNA-sequencing estimates from WSIs for 370 patients from the TCGA PRAD study. Out of 15 586 protein coding transcripts, 6618 had predicted expression significantly associated with RNA-seq estimates (FDR-adjusted P-value <1×10-4) in a cross-validation and 5419 (81.9%) of these associations were subsequently validated in a held-out test set. We furthermore predicted the prognostic cell-cycle progression score directly from WSIs. These findings suggest that contemporary computer vision models offer an inexpensive and scalable solution for prediction of gene expression phenotypes directly from WSIs, providing opportunity for cost-effective large-scale research studies and molecular diagnostics. AVAILABILITY AND IMPLEMENTATION: A self-contained example is available from http://github.com/phiwei/prostate_coexpression. Model predictions and metrics are available from doi.org/10.5281/zenodo.4739097. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Translational in vitro and in vivo PKPD modelling for apramycin against Gram-negative lung pathogens to facilitate prediction of human efficacious dose in pneumonia.

OBJECTIVES: New drugs and methods to efficiently fight carbapenem-resistant gram-negative pathogens are sorely needed. In this study, we characterized the preclinical pharmacokinetics (PK) and pharmacodynamics of the clinical stage drug candidate apramycin in time kill and mouse lung infection models. Based on in vitro and in vivo data, we developed a mathematical model to predict human efficacy. METHODS: Three pneumonia-inducing gram-negative species Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae were studied. Bactericidal kinetics were evaluated with time-kill curves; in vivo PK were studied in healthy and infected mice, with sampling in plasma and epithelial lining fluid after subcutaneous administration; in vivo efficacy was measured in a neutropenic mouse pneumonia model. A pharmacokinetic-pharmacodynamic model, integrating all the data, was developed and simulations were performed. RESULTS: Good lung penetration of apramycin in epithelial lining fluid (ELF) was shown (area under the curve (AUC)ELF/AUCplasma = 88%). Plasma clearance was 48% lower in lung infected mice compared to healthy mice. For two out of five strains studied, a delay in growth (∼5 h) was observed in vivo but not in vitro. The mathematical model enabled integration of lung PK to drive mouse PK and pharmacodynamics. Simulations predicted that 30 mg/kg of apramycin once daily would result in bacteriostasis in patients. DISCUSSION: Apramycin is a candidate for treatment of carbapenem-resistant gram-negative pneumonia as demonstrated in an integrated modeling framework for three bacterial species. We show that mathematical modelling is a useful tool for simultaneous inclusion of multiple data sources, notably plasma and lung in vivo PK and simulation of expected scenarios in a clinical setting, notably lung infections.

Fusion Gene Detection Using Whole-Exome Sequencing Data in Cancer Patients.

Several fusion genes are directly involved in the initiation and progression of cancers. Numerous bioinformatics tools have been developed to detect fusion events, but they are mainly based on RNA-seq data. The whole-exome sequencing (WES) represents a powerful technology that is widely used for disease-related DNA variant detection. In this study, we build a novel analysis pipeline called Fuseq-WES to detect fusion genes at DNA level based on the WES data. The same method applies also for targeted panel sequencing data. We assess the method to real datasets of acute myeloid leukemia (AML) and prostate cancer patients. The result shows that two of the main AML fusion genes discovered in RNA-seq data, PML-RARA and CBFB-MYH11, are detected in the WES data in 36 and 63% of the available samples, respectively. For the targeted deep-sequencing of prostate cancer patients, detection of the TMPRSS2-ERG fusion, which is the most frequent chimeric alteration in prostate cancer, is 91% concordant with a manually curated procedure based on four other methods. In summary, the overall results indicate that it is challenging to detect fusion genes in WES data with a standard coverage of ∼ 15-30x, where fusion candidates discovered in the RNA-seq data are often not detected in the WES data and vice versa. A subsampling study of the prostate data suggests that a coverage of at least 75x is necessary to achieve high accuracy.

Clinical Trial Protocol for ProBio: An Outcome-adaptive and Randomised Multiarm Biomarker-driven Study in Patients with Metastatic Prostate Cancer.

ProBio is an outcome-adaptive, multiarm, multiple-assignment randomised, biomarker-driven platform trial in men with metastatic castration-resistant prostate cancer. Here we describe the amended clinical protocol, focusing on expansion of the trial to include patients with de novo metastatic hormone-sensitive prostate cancer.

Circulating tumor DNA (ctDNA) in adjuvant therapy of early stage colon cancer: current status and future perspectives.

BACKGROUND: This article reviews the current knowledge on circulating tumor DNA (ctDNA) in early stage colon cancer and ongoing trials on ctDNA-guided treatment in the adjuvant setting. METHODS: A literature search of Pubmed was performed to identify studies on ctDNA in early stage colon cancer and neoadjuvant or adjuvant treatment. For ongoing trials, we searched clinicaltrials.gov and the Australian New Zealand Clinical Trials Registry (ANZCTR). RESULTS: Several studies show that ctDNA is a strong predictor for recurrence and survival after surgery and adjuvant chemotherapy. The specificity of this marker is extremely high, and the sensitivity is increasing with the development of technology. Recurrences can be detected very early and the analysis can potentially be used to guide neoadjuvant and adjuvant treatment. Ongoing and planned studies are now looking into escalation and de-escalation of therapy according to ctDNA-status after surgery. CONCLUSION: Serial measurement of ctDNA shows great promise as a marker for both prognosis and response to treatment in early colon cancer. Future studies will show whether we can use this analysis for tailoring treatment for patients in the adjuvant and neoadjuvant setting. With improved technology, ctDNA has the potential of becoming a 'game-changer' in the treatment of early stage colon cancers.

Ductal and acinar components of mixed prostatic adenocarcinoma frequently have a common clonal origin.

BACKGROUND: Ductal adenocarcinoma (DA) is an aggressive subtype of prostate cancer. It is most commonly seen in mixed tumors together with conventional acinar adenocarcinoma (AA). The genetic profile of DA and its clonal origin is not fully characterized. OBJECTIVE: To investigate whether DA represents a distinct genetic subtype and to investigate the somatic relationship between the ductal and acinar components of mixed cancers. DESIGN, SETTING, AND PARTICIPANTS: In 17 radical prostatectomy specimens ductal and acinar tumor components from the same tumor foci were dissected. DNA was extracted and genomic sequencing performed. After exclusion of two cases with low cell yield, 15 paired samples remained for analysis. RESULTS: In 12 of 15 cases a common somatic denominator was identified, while three cases had clonally separate components. In DA, TMPRSS2-ERG gene fusions were detected in 47% (7/15), clonal FOXA1 alterations in 33% (5/15) and SPOP alterations in 27% (4/15) of cases. In one case KIAA1549-BRAF fusion was identified. Genome doubling events, resulting in an increased ploidy, were identified in the DA in 53% (8/15) of cases, but not seen in any AA. PTEN and CTNNB1 alterations were enriched in DA (6/15) but not seen in any AA. No cancers showed microsatellite instability or high tumor mutation burden. CONCLUSIONS: Ductal and acinar prostate adenocarcinoma components of mixed tumors most often share the same origin and are clonally related. DA components in mixed tumor often exhibit genome doubling events resulting in aneuploidy, consistent with the aggressive nature of high grade prostate cancer.

Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies-Current Status and Next Steps.

Diagnosis and Gleason grading of prostate cancer in biopsies are critical for the clinical management of men with prostate cancer. Despite this, the high grading variability among pathologists leads to the potential for under- and overtreatment. Artificial intelligence (AI) systems have shown promise in assisting pathologists to perform Gleason grading, which could help address this problem. In this mini-review, we highlight studies reporting on the development of AI systems for cancer detection and Gleason grading, and discuss the progress needed for widespread clinical implementation, as well as anticipated future developments. PATIENT SUMMARY: This mini-review summarizes the evidence relating to the validation of artificial intelligence (AI)-assisted cancer detection and Gleason grading of prostate cancer in biopsies, and highlights the remaining steps required prior to its widespread clinical implementation. We found that, although there is strong evidence to show that AI is able to perform Gleason grading on par with experienced uropathologists, more work is needed to ensure the accuracy of results from AI systems in diverse settings across different patient populations, digitization platforms, and pathology laboratories.

Increased Pathway Complexity Is a Prognostic Biomarker in Metastatic Castration-Resistant Prostate Cancer.

Metastatic castration-resistant prostate cancer (mCRPC) is a heterogeneous disease, characterized by common and rare driver gene alterations that provide a selective growth advantage for progressing tumour cells. We hypothesized that the number of distinct gene driver alteration-affected pathways or gene classes was associated with poor prognosis in patients initiating androgen receptor signalling inhibitors (ARSi). We performed a post hoc analysis of an amalgamated baseline circulating tumour DNA (ctDNA) mutational landscape dataset of ARSi-treated men with mCRPC (n = 342). We associated the detected hotspot, pathogenic, and/or high impact protein function-affecting perturbations in 39 genes into 13 pathways. Progression-free (PFS) and overall survival (OS) were analysed using Kaplan-Meier curves and multivariate Cox regression models. Driver gene alterations were detected in 192/342 (56.1%) evaluable patients. An increased number of affected pathways, coined pathway complexity index (PCI), resulted in a decremental PFS and OS, and was independently associated with prognosis once ≥3 pathway or gene classes were affected (PFS HR (95%CI): 1.7 (1.02-2.84), p = 0.04, and OS HR (95%CI): 2.5 (1.06-5.71), p = 0.04). Additionally, visceral disease and baseline PSA and plasma ctDNA levels were independently associated with poor prognosis. Elevated PCI is associated with poor ARSi outcome and supports comprehensive genomic profiling to better infer mCRPC prognosis.

Challenging conventional karyotyping by next-generation karyotyping in 281 intensively treated patients with AML.

Although copy number alterations (CNAs) and translocations constitute the backbone of the diagnosis and prognostication of acute myeloid leukemia (AML), techniques used for their assessment in routine diagnostics have not been reconsidered for decades. We used a combination of 2 next-generation sequencing-based techniques to challenge the currently recommended conventional cytogenetic analysis (CCA), comparing the approaches in a series of 281 intensively treated patients with AML. Shallow whole-genome sequencing (sWGS) outperformed CCA in detecting European Leukemia Net (ELN)-defining CNAs and showed that CCA overestimated monosomies and suboptimally reported karyotype complexity. Still, the concordance between CCA and sWGS for all ELN CNA-related criteria was 94%. Moreover, using in silico dilution, we showed that 1 million reads per patient would be enough to accurately assess ELN-defining CNAs. Total genomic loss, defined as a total loss ≥200 Mb by sWGS, was found to be a better marker for genetic complexity and poor prognosis compared with the CCA-based definition of complex karyotype. For fusion detection, the concordance between CCA and whole-transcriptome sequencing (WTS) was 99%. WTS had better sensitivity in identifying inv(16) and KMT2A rearrangements while showing limitations in detecting lowly expressed PML-RARA fusions. Ligation-dependent reverse transcription polymerase chain reaction was used for validation and was shown to be a fast and reliable method for fusion detection. We conclude that a next-generation sequencing-based approach can replace conventional CCA for karyotyping, provided that efforts are made to cover lowly expressed fusion transcripts.

Integrated transcriptomic and genomic analysis improves prediction of complete remission and survival in elderly patients with acute myeloid leukemia.

Relevant molecular tools for treatment stratification of patients ≥65 years with acute myeloid leukemia (AML) are lacking. We combined clinical data with targeted DNA- and full RNA-sequencing of 182 intensively and palliatively treated patients to predict complete remission (CR) and survival in AML patients ≥65 years. Intensively treated patients with NPM1 and IDH2R172 mutations had longer overall survival (OS), whereas mutated TP53 conferred lower CR rates and shorter OS. FLT3-ITD and TP53 mutations predicted worse OS in palliatively treated patients. Gene expression levels most predictive of CR were combined with somatic mutations for an integrated risk stratification that we externally validated using the beatAML cohort. We defined a high-risk group with a CR rate of 20% in patients with mutated TP53, compared to 97% CR in low-risk patients defined by high expression of ZBTB7A and EEPD1 without TP53 mutations. Patients without these criteria had a CR rate of 54% (intermediate risk). The difference in CR rates translated into significant OS differences that outperformed ELN stratification for OS prediction. The results suggest that an integrated molecular risk stratification can improve prediction of CR and OS and could be used to guide treatment in elderly AML patients.

The ProBio trial: molecular biomarkers for advancing personalized treatment decision in patients with metastatic castration-resistant prostate cancer.

BACKGROUND: Multiple therapies exist for patients with metastatic castration-resistant prostate cancer (mCRPC). However, their improvement on progression-free survival (PFS) remains modest, potentially explained by tumor molecular heterogeneity. Several prognostic molecular biomarkers have been identified for mCRPC that may have predictive potential to guide treatment selection and prolong PFS. We designed a platform trial to test this hypothesis. METHODS: The Prostate-Biomarker (ProBio) study is a multi-center, outcome-adaptive, multi-arm, biomarker-driven platform trial for tailoring treatment decisions for men with mCRPC. Treatment decisions in the experimental arms are based on biomarker signatures defined as mutations in certain genes/pathways suggested in the scientific literature to be important for treatment response in mCRPC. The biomarker signatures are determined by targeted sequencing of circulating tumor and germline DNA using a panel specifically designed for mCRPC. DISCUSSION: Patients are stratified based on the sequencing results and randomized to either current clinical practice (control), where the treating physician decides treatment, or to molecularly driven treatment selection based on the biomarker profile. Outcome-adaptive randomization is implemented to early identify promising treatments for a biomarker signature. Biomarker signature-treatment combinations graduate from the platform when they demonstrate 85% probability of improving PFS compared to the control arm. Graduated combinations are further evaluated in a seamless confirmatory trial with fixed randomization. The platform design allows for new drugs and biomarkers to be introduced in the study. CONCLUSIONS: The ProBio design allows promising treatment-biomarker combinations to quickly graduate from the platform and be confirmed for rapid implementation in clinical care. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT03903835. Date of registration: April 4, 2019. Status: Recruiting.