Socioeconomic Status and Overall Survival Among Patients With Hematological Malignant Neoplasms.

Importance: In recent years, there has been a focus on reducing the socioeconomic gap in survival for hematological malignant neoplasms. Understanding recent developments is important to develop further intervention to improve care. Objective: To investigate the temporal trend in associations of socioeconomic status (SES) with survival among 3 aggressive hematological malignant neoplasms: multiple myeloma (MM), acute myeloid leukemia (AML), and diffuse large B-cell lymphoma (DLBCL). Design, Setting, and Participants: This nationwide, population-based cohort study used retrospectively collected data from 3 clinical registries of patients diagnosed in Denmark between January 1, 2005, and December 31, 2020, with follow-up until December 31, 2021. Analyses were stratified by diagnosis year (2005-2009, 2010-2014, and 2015-2020). Participants were patients aged 25 to 65 years with hematological malignant neoplasms. Patients with missing data on education were excluded. Data were analyzed from October 14, 2022, to January 2, 2024. Exposure: Education was used as a proxy for SES and defined low- and high-SES groups based on the completion of tertiary education. Main Outcomes and Measures: The main outcome was overall survival (OS), analyzed using Kaplan-Meier (log rank) method and Cox proportional hazards regression adjusted for age, sex, performance status, comorbidities, and disease-specific prognostic indices. Two-year OS through time and survival difference were estimated using flexible parametric survival models. Results: A total of 5677 patients (median [IQR] age, 58 [51-62] years; 3177 [57.0%] male) were assessed, including 1826 patients with MM, 1236 patients with AML, and 2509 patients with DLBCL. The 2-year OS increased over time for patients with MM (78.8% [95% CI, 75.4%-82.3%] to 91.4% [95% CI, 89.3%-93.5%]), AML (42.2% [95% CI, 37.8%-47.1%] to 52.7% [95% CI, 48.0%-57.9%]), and DLBCL (80.1% [95% CI, 77.4%-82.8%] to 88.1% [95% CI, 86.0%-90.3%]). For MM and DLBCL, no association of SES with survival was observed after adjustment (MM: hazard ratio [HR], 0.99 [95% CI, 0.85-1.15]; DLBCL: HR, 1.08 [95% CI, 0.91-1.29]). For AML, a negative association was observed between low SES and survival (HR, 1.49 [95% CI, 1.25-1.76]), but the association was attenuated in recent years. The difference in hazard for patients with low SES and AML was observed in the first 2 years after diagnosis. Conclusions and Relevance: These findings suggest that survival has improved among patients with these hematological malignant neoplasms. While patients with MM and DLBCL had increased survival in all groups, disparities were observed in AML outcomes, primarily in the first years after diagnosis. These results suggest that differences originate in factors specific to AML.

A clinical proteomics study of exhaled breath condensate and biomarkers for pulmonary embolism.

Pulmonary embolism (PE) can be a diagnostic challenge. Current diagnostic markers for PE are unspecific and new diagnostic tools are needed. The air we exhale is a possible new source for biomarkers which can be tapped into by analysing the exhaled breath condensate (EBC). We analysed the EBC from patients with PE and controls to investigate if the EBC is a useful source for new diagnostic biomarkers of PE. We collected and analysed EBC samples from patients with suspected PE and controls matched on age and sex. Patients in whom PE was ruled out after diagnostic work-up were included in the control group to increase the sensitivity and generalizability of the identified markers. EBC samples were collected using an RTube™. The protein composition of the EBCs were analysed using data dependent label-free quantitative nano liquid chromatography-tandem mass spectrometry. EBC samples from 28 patients with confirmed PE, and 49 controls were analysed. A total of 928 EBC proteins were identified in the 77 EBC samples. As expected, a low protein concentration was determined which resulted in many proteins with unmeasurable levels in several samples. The levels of HSPA5, PEBP1 and SFTPA2 were higher and levels of POF1B, EPPK1, PSMA4, ALDOA, and CFL1 were lower in PE compared with controls. In conclusion, the human EBC contained a variety of endogenous proteins and may be a source for new diagnostic markers of PE and other diseases.

Venetoclax-based therapy for relapsed or refractory acute myeloid leukaemia following intensive induction chemotherapy.

BACKGROUND: The treatment of relapsed or refractory (R/R) acute myeloid leukaemia (AML) remains challenging and outcomes extremely poor. The introduction of venetoclax has transformed the treatment of AML and emerging data suggest that venetoclax-based therapy may enforce salvage treatment. MATERIALS AND METHODS: In this nationwide Danish retrospective study, we analysed treatment outcomes of venetoclax-based salvage treatment for R/R AML between 2019 and 2022. Only venetoclax-naive patients who had previously received treatment with intensive chemotherapy therapy were included. RESULTS: The cohort consisted of 43 R/R patients with a median age of 57 years. Nine (20.9%) were primary refractory and 34 (79.1%) patients had relapsed, including 21 after previous allogeneic stem cell transplantation. The overall response rate was 76.2% including 61.9% with composite complete remission (CRc: CR + CRi). Among CRc-responders with information on measurable residual disease (MRD), 8/13 (61.5%) obtained an MRD-negativity response. The overall survival was 9.3 months for all patients with an estimated 1-year overall survival of 34%. For CRc-responders the median overall survival was 13.3 months, and the median relapse-free survival was 12.8 months. CONCLUSION: Venetoclax-based salvage treatment for R/R AML produced high response rates; however, for most patients the response was of limited duration. This study is limited by an observational design and prone to selection bias.

Longitudinal minimal residual disease assessment in multiple myeloma patients in complete remission - results from the NMSG flow-MRD substudy within the EMN02/HO95 MM trial.

BACKGROUND: Multiple myeloma remains an incurable disease with multiple relapses due to residual myeloma cells in the bone marrow of patients after therapy. Presence of small number of cancer cells in the body after cancer treatment, called minimal residual disease, has been shown to be prognostic for progression-free and overall survival. However, for multiple myeloma, it is unclear whether patients attaining minimal residual disease negativity may be candidates for treatment discontinuation. We investigated, if longitudinal flow cytometry-based monitoring of minimal residual disease (flow-MRD) may predict disease progression earlier and with higher sensitivity compared to biochemical assessments. METHODS: Patients from the Nordic countries with newly diagnosed multiple myeloma enrolled in the European-Myeloma-Network-02/Hovon-95 (EMN02/HO95) trial and undergoing bone marrow aspiration confirmation of complete response, were eligible for this Nordic Myeloma Study Group (NMSG) substudy. Longitdudinal flow-MRD assessment of bone marrow samples was performed to identify and enumerate residual malignant plasma cells until observed clinical progression. RESULTS: Minimal residual disease dynamics were compared to biochemically assessed changes in serum free light chain and M-component. Among 20 patients, reaching complete response or stringent complete response during the observation period, and with ≥3 sequential flow-MRD assessments analysed over time, increasing levels of minimal residual disease in the bone marrow were observed in six cases, preceding biochemically assessed disease and clinical progression by 5.5 months and 12.6 months (mean values), respectively. Mean malignant plasma cells doubling time for the six patients was 1.8 months (95% CI, 1.4-2.3 months). Minimal malignant plasma cells detection limit was 4 × 10-5. CONCLUSIONS: Flow-MRD is a sensitive method for longitudinal monitoring of minimal residual disease dynamics in multiple myeloma patients in complete response. Increasing minimal residual disease levels precedes biochemically assessed changes and is an early indicator of subsequent clinical progression. TRIAL REGISTRATION: NCT01208766.

High Validity of the Danish National Patient Registry for Systemic Anticancer Treatment Registration from 2009 to 2019.

BACKGROUND: The Danish National Patient Registry is a major resource for Danish epidemiology. Only a few studies have been conducted to check the validity of the reporting of systemic anticancer treatments. In this study, we assessed this validity for a range of cancer types over a long period of time. PATIENTS AND METHODS: We extracted systemic anticancer treatment procedures from the Danish National Patient Registry for patients with solid malignant tumors treated at the Department of Oncology at Aalborg University Hospital between 2009 and 2019 (12,014 patients with 215,293 drug records). These data were compared to records obtained from the antineoplastic prescription database used at the department. We estimated the sensitivity, positive predictive value (PPV), and F1-score defined as the harmonic mean of the sensitivity and the PPV. RESULTS: There was an overall high concordance between the two datasets with a sensitivity and a PPV >92%. Treatments for brain, ovarian and endometrial cancers displayed lower concordance (81-89%). The validity was stable over the study period, with a slight drop during 2016-2017. Most drugs had a high validity with F1-scores above 90%. Fluorouracil, gemcitabine, pemetrexed, pembrolizumab, and nivolumab had F1-scores above 97%. Drugs that were introduced in the study period, such as lapatinib, palbociclib, erlotinib, pertuzumab, and panitumumab, yielded lower F1-scores due to the absence of specific registry codes early after introduction. CONCLUSION: The Danish National Patient Registry can be used to reliably obtain information about systemic anticancer treatments, keeping in mind limitations for recently introduced drugs and for some types of cancer.

Mathematical modelling of SARS-CoV-2 variant outbreaks reveals their probability of extinction.

When a virus spreads, it may mutate into, e.g., vaccine resistant or fast spreading lineages, as was the case for the Danish Cluster-5 mink variant (belonging to the B.1.1.298 lineage), the British B.1.1.7 lineage, and the South African B.1.351 lineage of the SARS-CoV-2 virus. A way to handle such spreads is through a containment strategy, where the population in the affected area is isolated until the spread has been stopped. Under such circumstances, it is important to monitor whether the mutated virus is extinct via massive testing for the virus sub-type. If successful, the strategy will lead to lower and lower numbers of the sub-type, and it will eventually die out. An important question is, for how long time one should wait to be sure the sub-type is extinct? We use a hidden Markov model for infection spread and an approximation of a two stage sampling scheme to infer the probability of extinction. The potential of the method is illustrated via a simulation study. Finally, the model is used to assess the Danish containment strategy when SARS-CoV-2 spread from mink to man during the summer of 2020, including the Cluster-5 sub-type. In order to avoid further spread and mink being a large animal virus reservoir, this situation led to the isolation of seven municipalities in the Northern part of the country, the culling of the entire Danish 17 million large mink population, and a bill to interim ban Danish mink production until the end of 2021.

Putative Biomarkers for Acute Pulmonary Embolism in Exhaled Breath Condensate.

Current diagnostic markers for pulmonary embolism (PE) are unspecific. We investigated the proteome of the exhaled breath condensate (EBC) in a porcine model of acute PE in order to identify putative diagnostic markers for PE. EBC was collected at baseline and after the induction of autologous intermediate-risk PE in 14 pigs, plus four negative control pigs. The protein profiles of the EBC were analyzed using label-free quantitative nano liquid chromatography-tandem mass spectrometry. A total of 897 proteins were identified in the EBCs from the pigs. Alterations were found in the levels of 145 different proteins after PE compared with the baseline and negative controls: albumin was among the most upregulated proteins, with 14-fold higher levels 2.5 h after PE (p-value: 0.02). The levels of 49 other proteins were between 1.3- and 17.1-fold higher after PE. The levels of 95 proteins were lower after PE. Neutrophil gelatinase-associated lipocalin (fold change 0.3, p-value < 0.01) was among the most reduced proteins 2.5 h after PE. A prediction model based on penalized regression identified five proteins including albumin and neutrophil gelatinase-associated lipocalin. The model was capable of discriminating baseline samples from EBC samples collected 2.5 h after PE correctly in 22 out of 27 samples. In conclusion, the EBC from pigs with acute PE contained several putative diagnostic markers of PE.

Patient-reported outcomes in patients with hematological relapse or progressive disease: a longitudinal observational study.

BACKGROUND: Patients with hematological cancer who experience relapse or progressive disease often face yet another line of treatment and continued mortality risk that could increase their physical and emotional trauma and worsen their health-related quality of life. Healthcare professionals who use patient-reported outcomes to identify who will have specific sensitivities in particular health-related quality of life domains may be able to individualize and target treatment and supportive care, both features of precision medicine. Here, in a cohort of patients with relapsed or progressive hematological cancer, we sought to identify health-related quality of life domains in which they experienced deterioration after relapse treatment and to investigate health-related quality of life patterns. METHOD: Patients were recruited in connection with a precision medicine study at the Department of Hematology, Aalborg University Hospital. They completed the European Organization for Research and Treatment of Cancer questionnaire and the Hospital Anxiety and Depression Scale at baseline and at 3, 6, 9, and 12 months after the relapse diagnosis or progressive cancer. Modes of completion were electronically or on paper. Clinically relevant changes from baseline to 12 months were interpreted according to Cocks' guidelines. We quantified the number of patients with moderate or severe symptoms and functional problems and the number who experienced improvements or deterioration from baseline to 12 months. RESULTS: A total of 104 patients were included, of whom 90 (87%) completed baseline questionnaires and 50 (56%) completed the 12-month assessments. The three symptoms that patients most often reported as deteriorating were fatigue (18%), insomnia (18%), and diarrhea (18%). The three functions that patients most often reported as deteriorating were role (16%) and emotional (16%) and cognitive (16%) functioning. CONCLUSION: In this study, patient-reported outcome data were useful for identifying negatively affected health-related quality of life domains in patients with relapsed or progressive hematological cancer. We identified patients experiencing deterioration in health-related quality of life during treatment and characterized a potential role for patient-reported outcomes in precision medicine to target treatment and supportive care in this patient group.

The mutational profile of immune surveillance genes in diagnostic and refractory/relapsed DLBCLs.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is the most frequent lymphoid neoplasm among adults,and approximately 30-40% of patients will experience relapse while 5-10% will suffer from primary refractory disease caused by different mechanisms, including treatment-induced resistance. For refractory and relapsed DLBCL (rrDLBCL) patients, early detection and understanding of the mechanisms controlling treatment resistance are of great importance to guide therapy decisions. Here, we have focused on genetic variations in immune surveillance genes in diagnostic DLBCL (dDLBCL) and rrDLBCL patients to elaborate on the suitability of new promising immunotherapies. METHODS: Biopsies from 30 dDLBCL patients who did not progress or relapse during follow up and 17 rrDLBCL patients with refractory disease or who relapsed during follow up were analyzed by whole-exome sequencing, including matched individual germline samples to include only somatic genetic variants in downstream analysis of a curated list of 58 genes involved in major immune surveillance pathways. RESULTS: More than 70% of both dDLBCLs and rrDLBCLs harbored alterations in immune surveillance genes, but rrDLBCL tumor samples have a lower number of genes affected compared to dDLBCL tumor samples. Increased gene mutation frequencies in rrDLBCLs were observed in more than half of the affected immune surveillance genes than dDLBCLs. CONCLUSION: Genetic variants in the antigen-presenting genes affect a higher number of rrDLBCL patients supporting an important role for these genes in tumor progression and development of refractory disease and relapse.

A bendamustine resistance gene signature in diffuse large B-cell lymphoma and multiple myeloma.

Aim: Bendamustine is primarily used for treatment of indolent lymphomas but has shown efficacy in some patients with diffuse large B-cell lymphoma (DLBCL) and multiple myeloma (MM). Molecular-based patient stratification for identification of resistant patients, who will benefit from alternative treatments, is important. The aim of this study was to develop a resistance gene signature (REGS) from bendamustine dose-response assays in cultures of DLBCL and MM cell lines, enabling prediction of bendamustine response in DLBCL and MM patients. Methods: Bendamustine response was determined in 14 DLBCL and 11 MM cell lines. Using baseline gene expression profiles and degree of growth inhibition after bendamustine exposure, a bendamustine REGS was developed and examined for the risk stratification potential in DLBCL (n = 971) and MM (n = 1,126) patients divided into prognostic subtypes. Results: Bendamustine resistance significantly correlated with resistance to cyclophosphamide in DLBCL and melphalan in MM cell lines. The bendamustine REGS showed significantly lower bendamustine resistance probabilities in DLBCL patients with GCB subtype tumors and in tumors of the differentiation dependent centrocyte and plasmablast subtypes. In MM patients, pre-BII classified tumors displayed high bendamustine resistance probabilities and the plasma cell subtype had lower bendamustine resistance probability than memory cells. Furthermore, tumors belonging to the 4p14, MAF, and D2 TC subclasses consistently displayed high bendamustine resistance probabilities. Conclusion: Significant differences in predicted response to bendamustine were found in molecular subtypes of DLBCL and MM, encouraging validation in prospective bendamustine-treated cohorts with available gene expression profiles and follow-up data.

Regression on imperfect class labels derived by unsupervised clustering.

Outcome regressed on class labels identified by unsupervised clustering is custom in many applications. However, it is common to ignore the misclassification of class labels caused by the learning algorithm, which potentially leads to serious bias of the estimated effect parameters. Due to their generality we suggest to address the problem by use of regression calibration or the misclassification simulation and extrapolation method. Performance is illustrated by simulated data from Gaussian mixture models, documenting a reduced bias and improved coverage of confidence intervals when adjusting for misclassification with either method. Finally, we apply our method to data from a previous study, which regressed overall survival on class labels derived from unsupervised clustering of gene expression data from bone marrow samples of multiple myeloma patients.

Antigen Expression Varies Significantly between Molecular Subgroups of Acute Myeloid Leukemia Patients: Clinical Applicability Is Hampered by Establishment of Relevant Cutoffs.

INTRODUCTION: In this single-center study of 268 acute myeloid leukemia (AML) patients, we have tested if a subset of 4 routinely employed immunophenotypic stem cell-associated markers correlated with the presence of recurrently mutated genes and if the markers were predictive for mutational status. METHODS: Immunophenotypic data from 268 diagnostic AML samples obtained in 2009-2018 were analyzed retrospectively for the antigens CD34, CD117, CD123, and CLEC12A. Correlation between immunophenotypes and mutations was analyzed by Fischer's exact test. Clinical applicability of the markers for predicting mutational status was evaluated by receiver operating characteristics analyses, where an area under the curve (AUC) of at least 0.85 was accepted as clinically relevant. RESULTS: For a number of genes, the antigen expression differed significantly between mutated and wild-type gene expression. Despite low AUCs, CD123 and CLEC12A correlated with FLT3+NPM1- and FLT3+NPM1+. Three subsets met the AUC requirements (CD34+, CD34+CD117+, and CD34-CD117+) for predicting FLT3-NPM1+ or FLT3+NPM1+. CONCLUSION: The value of immunophenotypes as surrogate markers for mutational status in AML seems limited when employing CD123 and CLEC12A in combination with CD34 and CD117. Defining relevant cutoffs for given markers is challenging and hampered by variation between laboratories and patient groups.

Shared care follow-up of patients with B-cell neoplasms based on nurse-led telephone consultations and PRO-data: a feasibility study from the North Denmark Region.

BACKGROUND: Patients with B-cell neoplasms in remission are monitored with regular physician visits at the hospital. The current standard follow-up procedure is not evidence-based or individualized to patient needs. To improve and individualize the follow-up, we investigated the feasibility of a shared care follow-up initiative, with alternating physician visits and nurse-led telephone consultations and assessments based on patient-reported outcome (PRO) data. METHODS: Patients ≥18 years diagnosed with B-cell neoplasms were eligible for the study when they were in remission and stable without treatment for at least 6 months. Patients were assigned to alternating visits with physicians and nurse-led telephone consultations. The nurse-led telephone consultations were based on PROs, which were collected with the European Organization for Research and Treatment of Cancer questionnaire (EORTC-QLQ-C30), the Myeloproliferative Neoplasm - Symptom Assessment Form, and the Hospital Anxiety and Depression Scale. Patients completed questionnaires before every nurse-led consultation. We also applied the Patient Feedback Form to survey patient acceptance of the requirement of questionnaire completion. We applied descriptive statistics, in terms of counts (n) and proportions (%), to describe the study population and all endpoints. RESULTS: Between February 2017 and December 2018, 80 patients were enrolled. Adherence, measured as the recruitment rate, was 96% (80/83), and the drop-out rate was 6% (5/80). During the study period, 3/80 (4%) patients relapsed, and 5/80 (6%) patients returned to the standard follow-up, because they required closer medical observation. Relapses were diagnosed based on unscheduled visits requested by patients (n = 2) and patient-reported symptoms reviewed by the nurse (n = 1). The response rate to questionnaires was 98% (335/341). A total of 58/79 (74%) patients completed the Patient Feedback Form; 51/57 (89%) patients reported improved communication with health care professionals; and 50/57 (88%) patients reported improved recollection of symptoms as a result of completing questionnaires. CONCLUSION: Based on patient adherence, a low relapse rate, and positive patient attitudes towards completing questionnaires, we concluded that a shared care follow-up, supported by PROs, was a feasible alternative to the standard follow-up for patients with B-cell disease in remission.

Normal myeloid progenitor cell subset-associated gene signatures for acute myeloid leukaemia subtyping with prognostic impact.

Acute myeloid leukaemia (AML) is characterised by phenotypic heterogeneity, which we hypothesise is a consequence of deregulated differentiation with transcriptional reminiscence of the normal compartment or cell-of-origin. Here, we propose a classification system based on normal myeloid progenitor cell subset-associated gene signatures (MAGS) for individual assignments of AML subtypes. We generated a MAGS classifier including the progenitor compartments CD34+/CD38- for haematopoietic stem cells (HSCs), CD34+/CD38+/CD45RA- for megakaryocyte-erythroid progenitors (MEPs), and CD34+/CD38+/CD45RA+ for granulocytic-monocytic progenitors (GMPs) using regularised multinomial regression with three discrete outcomes and an elastic net penalty. The regularisation parameters were chosen by cross-validation, and MAGS assignment accuracy was validated in an independent data set (N = 38; accuracy = 0.79) of sorted normal myeloid subpopulations. The prognostic value of MAGS assignment was studied in two clinical cohorts (TCGA: N = 171; GSE6891: N = 520) and had a significant prognostic impact. Furthermore, multivariate Cox regression analysis using the MAGS subtype, FAB subtype, cytogenetics, molecular genetics, and age as explanatory variables showed independent prognostic value. Molecular characterisation of subtypes by differential gene expression analysis, gene set enrichment analysis, and mutation patterns indicated reduced proliferation and overrepresentation of RUNX1 and IDH2 mutations in the HSC subtype; increased proliferation and overrepresentation of CEBPA mutations in the MEP subtype; and innate immune activation and overrepresentation of WT1 mutations in the GMP subtype. We present a differentiation-dependent classification system for AML subtypes with distinct pathogenetic and prognostic importance that can help identify candidates poorly responding to combination chemotherapy and potentially guide alternative treatments.

MicroRNAs associated to single drug components of R-CHOP identifies diffuse large B-cell lymphoma patients with poor outcome and adds prognostic value to the international prognostic index.

BACKGROUND: Treatment resistance is a major clinical challenge of diffuse large B-cell lymphoma (DLBCL) where approximately 40% of the patients have refractory disease or relapse. Since DLBCL is characterized by great clinical and molecular heterogeneity, the purpose of the present study was to investigate whether miRNAs associated to single drug components of R-CHOP can improve robustness of individual markers and serve as a prognostic classifier. METHODS: Fifteen DLBCL cell lines were tested for sensitivity towards single drug compounds of the standard treatment R-CHOP: rituximab (R), cyclophosphamide (C), doxorubicin (H), and vincristine (O). For each drug, cell lines were ranked using the area under the dose-response curve and grouped as either sensitive, intermediate or resistant. Baseline miRNA expression data were obtained for each cell line in untreated condition, and differential miRNA expression analysis between sensitive and resistant cell lines identified 43 miRNAs associated to growth response after exposure towards single drugs of R-CHOP. Using the Affymetrix HG-U133 platform, expression levels of miRNA precursors were assessed in 701 diagnostic DLBCL biopsies, and miRNA-panel classifiers predicting disease progression were build using multiple Cox regression or random survival forest. Classifiers were validated and ranked by repeated cross-validation. RESULTS: Prognostic accuracies were assessed by Brier Scores and time-varying area under the ROC curves, which revealed better performance of multivariate Cox models compared to random survival forest models. The Cox model including miR-146a, miR-155, miR-21, miR-34a, and miR-23a~miR-27a~miR-24-2 cluster performed the best and successfully stratified GCB-DLBCL patients into high- and low-risk of disease progression. In addition, combination of the Cox miRNA-panel and IPI substantially increased prognostic performance in GCB classified patients. CONCLUSION: As a proof of concept, we found that expression data of drug associated miRNAs display prognostic utility and adding these to IPI improves prognostic stratification of GCB-DLBCL patients treated with R-CHOP.

Implementing the FAIR Data Principles in precision oncology: review of supporting initiatives.

Compelling research has recently shown that cancer is so heterogeneous that single research centres cannot produce enough data to fit prognostic and predictive models of sufficient accuracy. Data sharing in precision oncology is therefore of utmost importance. The Findable, Accessible, Interoperable and Reusable (FAIR) Data Principles have been developed to define good practices in data sharing. Motivated by the ambition of applying the FAIR Data Principles to our own clinical precision oncology implementations and research, we have performed a systematic literature review of potentially relevant initiatives. For clinical data, we suggest using the Genomic Data Commons model as a reference as it provides a field-tested and well-documented solution. Regarding classification of diagnosis, morphology and topography and drugs, we chose to follow the World Health Organization standards, i.e. ICD10, ICD-O-3 and Anatomical Therapeutic Chemical classifications, respectively. For the bioinformatics pipeline, the Genome Analysis ToolKit Best Practices using Docker containers offer a coherent solution and have therefore been selected. Regarding the naming of variants, we follow the Human Genome Variation Society's standard. For the IT infrastructure, we have built a centralized solution to participate in data sharing through federated solutions such as the Beacon Networks.

A multiple myeloma classification system that associates normal B-cell subset phenotypes with prognosis.

Despite the recent progress in treatment of multiple myeloma (MM), it is still an incurable malignant disease, and we are therefore in need of new risk stratification tools that can help us to understand the disease and optimize therapy. Here we propose a new subtyping of myeloma plasma cells (PCs) from diagnostic samples, assigned by normal B-cell subset associated gene signatures (BAGS). For this purpose, we combined fluorescence-activated cell sorting and gene expression profiles from normal bone marrow (BM) Pre-BI, Pre-BII, immature, naïve, memory, and PC subsets to generate BAGS for assignment of normal BM subtypes in diagnostic samples. The impact of the subtypes was analyzed in 8 available data sets from 1772 patients' myeloma PC samples. The resulting tumor assignments in available clinical data sets exhibited similar BAGS subtype frequencies in 4 cohorts from de novo MM patients across 1296 individual cases. The BAGS subtypes were significantly associated with progression-free and overall survival in a meta-analysis of 916 patients from 3 prospective clinical trials. The major impact was observed within the Pre-BII and memory subtypes, which had a significantly inferior prognosis compared with other subtypes. A multiple Cox proportional hazard analysis documented that BAGS subtypes added significant, independent prognostic information to the translocations and cyclin D classification. BAGS subtype analysis of patient cases identified transcriptional differences, including a number of differentially spliced genes. We identified subtype differences in myeloma at diagnosis, with prognostic impact and predictive potential, supporting an acquired B-cell trait and phenotypic plasticity as a pathogenetic hallmark of MM.

A B-cell-associated gene signature classification of diffuse large B-cell lymphoma by NanoString technology.

Gene expression profiling (GEP) by microarrays of diffuse large B-cell lymphoma (DLBCL) has enabled the categorization of DLBCL into activated B-cell-like and germinal center B-cell-like subclasses. However, as this does not fully embrace the great diversity of B-cell subtypes, we recently developed a gene expression assay for B-cell-associated gene signature (BAGS) classification. To facilitate quick and easy-to-use BAGS profiling, we developed in this study the NanoString-based BAGS2Clinic assay. Microarray data from 4 different cohorts (n = 970) were used to select genes and train the assay. The locked assay was validated in an independent cohort of 88 sample biopsies. The assay showed good correspondence with the original BAGS classifier, with an overall accuracy of 84% (95% confidence interval, 72% to 93%) and a subtype-specific accuracy ranging between 80% and 99%. BAGS classification has the potential to provide valuable insight into tumor biology as well as differences in resistance to immuno- and chemotherapy that can lead to novel treatment strategies for DLBCL patients. BAGS2Clinic can facilitate this and the implementation of BAGS classification as a routine clinical tool to improve prognosis and treatment guidance for DLBCL patients.

Molecular classification of tissue from a transformed non-Hogkin's lymphoma case with unexpected long-time remission.

BACKGROUND: The concept of precision medicine in cancer includes individual molecular studies to predict clinical outcomes. In the present N = 1 case we retrospectively have analysed lymphoma tissue by exome sequencing and global gene expression in a patient with unexpected long-term remission following relaps. The goals were to phenotype the diagnostic and relapsed lymphoma tissue and evaluate its pattern. Furthermore, to identify mutations available for targeted therapy and expression of genes to predict specific drug effects by resistance gene signatures (REGS) for R-CHOP as described at http://www.hemaclass.org. We expected that such a study could generate therapeutic information and a frame for future individual evaluation of molecular resistance detected at clinical relapse. CASE PRESENTATION: The patient was diagnosed with a transformed high-grade non-Hodgkin lymphoma stage III and treated with conventional R-CHOP [rituximab (R), cyclophosphamide (C), doxorubicin (H), vincristine (O) and prednisone (P)]. Unfortunately, she suffered from severe toxicity but recovered during the following 6 months' remission until biopsy-verified relapse. The patient refused second-line combination chemotherapy, but accepted 3 months' palliation with R and chlorambucil. Unexpectedly, she obtained continuous complete remission and is at present >9 years after primary diagnosis. Molecular studies and data evaluation by principal component analysis, mutation screening and copy number variations of the primary and relapsed tumor, identified a pattern of branched lymphoma evolution, most likely diverging from an in situ follicular lymphoma. Accordingly, the primary diagnosed transformed lymphoma was classified as a diffuse large B cell lymphoma (DLBCL) of the GCB/centrocytic subtype by "cell of origin BAGS" assignment and R sensitive and C, H, O and P resistant by "drug specific REGS" assignment. The relapsed DLBCL was classified as NC/memory subtype and R, C, H sensitive but O and P resistant. CONCLUSIONS: Thorough analysis of the tumor DNA and RNA documented a branched evolution of the two clinical diagnosed tFL, most likely transformed from an unknown in situ lymphoma. Classification of the malignant tissue for drug-specific resistance did not explain the unexpected long-term remission and potential cure. However, it is tempting to consider the anti-CD20 immunotherapy as the curative intervention in the two independent tumors of this case.