Extracting Systemic Anticancer Treatment Lines from the Danish National Patient Registry for Solid Tumour Patients Treated in the North Denmark Region Between 2009 and 2019.

Background: Reconstructing patient treatment trajectories is important to generate real-world evidence for epidemiological studies. The Danish National Patient Registry (DNPR) contains information about drug prescriptions and could therefore be used to reconstruct treatment trajectories. We aimed to evaluate and enhance two existing methods to reconstruct systemic anticancer treatment trajectories. Methods: This study was based on data from 8738 consecutive patients with solid tumors treated in the North Denmark Region between 2009 and 2019. Two approaches found in the literature as well as two new approaches were applied to the DNPR data. All methods relied on time intervals between two consecutive drug administrations to determine if they belonged to the same treatment line. MedOnc, a local dataset from the Department of Oncology, Aalborg University Hospital was used as a reference. To evaluate the performance of each method, F1-scores were calculated after matching the lines identified in both datasets. We used three different matching strategies: stringent matching, loose matching, and matching based on line numbers, controlling for overfitting. Results: Overall, the two new approaches outperformed the simpler and best performing of the two existing methods, with F1-scores of 0.47 and 0.45 vs 0.44 for stringent matching and 0.84 and 0.83 vs 0.82 for loose matching. Nevertheless, only one of the new methods outperformed the existing simpler method when matching on the number of lines (0.73 vs 0.72). Large differences were seen by cancer site, especially for the stringent and line number matchings. Performances were relatively stable by calendar year. Conclusion: The high F1-scores for the new methods confirm that they should be generally preferred to reconstruct systemic anticancer treatment trajectories using the DNPR.

Trends and drivers of pharmaceutical expenditures from systemic anti-cancer therapy.

Expenditures on medicine for systemic anti-cancer therapy (SACT) have seen large increases in recent years. The characterization of patients with high SACT costs is crucial to identify cost-driving factors, but little is known about the distribution of expenditures at the patient-level. We priced 260,834 registrations of SACT for 12,589 patients from 2008 to 2019 by combining them with product-level billings of EUR 142.1 million. Based on this, we defined high-cost patients as the 2.5% most expensive by accumulated SACT expenditures. We found that high-cost patients accounted for 28.8% of the total SACT expenditures and were observed across all major cancer groups except for pancreatic cancer. The risk of becoming a high-cost patient was increased for younger age groups, i.e., 18-44 and 45-64 years, for patients with BMI ≥ 25, and for patients with multiple cancer diagnoses, while no alteration of risk was observed due to comorbidities or sex. Changes in the characteristics of high-cost patients during the study period were found with an increased risk of becoming high-cost in later years for elderly patients and patients with lung cancer and a decreased risk for breast cancer patients.

Dynamic Risk Prediction of 30-Day Mortality in Patients With Advanced Lung Cancer: Comparing Five Machine Learning Approaches.

PURPOSE: Administering systemic anticancer treatment (SACT) to patients near death can negatively affect their health-related quality of life. Late SACT administrations should be avoided in these cases. Machine learning techniques could be used to build decision support tools leveraging registry data for clinicians to limit late SACT administration. MATERIALS AND METHODS: Patients with advanced lung cancer who were treated at the Department of Oncology, Aalborg University Hospital and died between 2010 and 2019 were included (N = 2,368). Diagnoses, treatments, biochemical data, and histopathologic results were used to train predictive models of 30-day mortality using logistic regression with elastic net penalty, random forest, gradient tree boosting, multilayer perceptron, and long short-term memory network. The importance of the variables and the clinical utility of the models were evaluated. RESULTS: The random forest and gradient tree boosting models outperformed other models, whereas the artificial neural network-based models underperformed. Adding summary variables had a modest effect on performance with an increase in average precision from 0.500 to 0.505 and from 0.498 to 0.509 for the gradient tree boosting and random forest models, respectively. Biochemical results alone contained most of the information with a limited degradation of the performances when fitting models with only these variables. The utility analysis showed that by applying a simple threshold to the predicted risk of 30-day mortality, 40% of late SACT administrations could have been prevented at the cost of 2% of patients stopping their treatment 90 days before death. CONCLUSION: This study demonstrates the potential of a decision support tool to limit late SACT administration in patients with cancer. Further work is warranted to refine the model, build an easy-to-use prototype, and conduct a prospective validation study.

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.

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.

Nationwide Survival Benefit after Implementation of First-Line Immunotherapy for Patients with Advanced NSCLC-Real World Efficacy.

Background The selection of patients with non-small cell lung cancer (NSCLC) for immune checkpoint inhibitor (ICI) treatment remains challenging. This real-world study aimed to compare the overall survival (OS) before and after the implementation of ICIs, to identify OS prognostic factors, and to assess treatment data in first-line (1L) ICI-treated patients without epidermal growth factor receptor mutation or anaplastic lymphoma kinase translocation. Methods Data from the Danish NSCLC population initiated with 1L palliative antineoplastic treatment from 1 January 2013 to 1 October 2018, were extracted from the Danish Lung Cancer Registry (DLCR). Long-term survival and median OS pre- and post-approval of 1L ICI were compared. From electronic health records, additional clinical and treatment data were obtained for ICI-treated patients from 1 March 2017 to 1 October 2018. Results The OS was significantly improved in the DLCR post-approval cohort (n = 2055) compared to the pre-approval cohort (n = 1658). The 3-year OS rates were 18% (95% CI 15.6-20.0) and 6% (95% CI 5.1-7.4), respectively. On multivariable Cox regression, bone (HR = 1.63) and liver metastases (HR = 1.47), performance status (PS) 1 (HR = 1.86), and PS ≥ 2 (HR = 2.19) were significantly associated with poor OS in ICI-treated patients. Conclusion OS significantly improved in patients with advanced NSCLC after ICI implementation in Denmark. In ICI-treated patients, PS ≥ 1, and bone and liver metastases were associated with a worse prognosis.

Direct costs of antineoplastic and supportive treatment for progressive multiple myeloma in a tax-based health system.

Aim: To estimate current real-world costs of drugs and supportive care for the treatment of multiple myeloma in a tax-based health system. Methods: Forty-one patients were included from a personalized medicine study (2016-2019). Detailed information was collected from patient journals and hospital registries to estimate the total and mean costs using inverse probability weighting of censored data. Results: Total observed (censored) costs for the 41 patients was €8.84 million during 125 treatment years, with antineoplastic drugs as the main cost driver (€5.6 million). Individual costs showed large variations. Mean 3-year cost per patient from first progression was €182,103 (€131,800-232,405). Conclusion: Prediction of real-world costs is hindered by the availability of detailed costing data. Micro-costing analyses are needed for budgeting and real-world evaluation of cost-effectiveness.

Lay abstract In recent years, there has been a dramatic improvement in the treatment of multiple myeloma due to the introduction of new drugs. These drugs have significantly increased survival but have also had an immense impact on healthcare budgets. In this study, we used detailed treatment information for multiple myeloma patients in combination with billing data from the hospital pharmacy at a Danish hospital to calculate individual cost histories for both drugs and supportive care. Using these data, we estimated the mean 3-year cost of a multiple myeloma patient to be €182.103, but we also found large variation between patients, causing an uncertainty of €50.000 in either direction. We believe that detailed costing studies, similar to the present one, are necessary for evaluation of cost-effectiveness of drugs in clinical practice.

Development of a Precision Medicine Workflow in Hematological Cancers, Aalborg University Hospital, Denmark.

Within recent years, many precision cancer medicine initiatives have been developed. Most of these have focused on solid cancers, while the potential of precision medicine for patients with hematological malignancies, especially in the relapse situation, are less elucidated. Here, we present a demographic unbiased and observational prospective study at Aalborg University Hospital Denmark, referral site for 10% of the Danish population. We developed a hematological precision medicine workflow based on sequencing analysis of whole exome tumor DNA and RNA. All steps involved are outlined in detail, illustrating how the developed workflow can provide relevant molecular information to multidisciplinary teams. A group of 174 hematological patients with progressive disease or relapse was included in a non-interventional and population-based study, of which 92 patient samples were sequenced. Based on analysis of small nucleotide variants, copy number variants, and fusion transcripts, we found variants with potential and strong clinical relevance in 62% and 9.5% of the patients, respectively. The most frequently mutated genes in individual disease entities were in concordance with previous studies. We did not find tumor mutational burden or micro satellite instability to be informative in our hematologic patient cohort.

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.

Oral mucosa tissue gene expression profiling before, during, and after radiation therapy for tonsil squamous cell carcinoma.

BACKGROUND: Radiation-therapy (RT) induces mucositis, a clinically challenging condition with limited prophylactic interventions and no predictive tests. In this pilot study, we applied global gene-expression analysis on serial human oral mucosa tissue and blood cells from patients with tonsil squamous cell cancer (TSCC) to identify genes involved in mucositis pathogenesis. METHODS AND FINDINGS: Eight patients with TSCC each provided consecutive buccal biopsies and blood cells before, after 7 days of RT treatment, and 20 days following RT. We monitored clinical mucositis and performed gene-expression analysis on tissue samples. We obtained control tissue from nine healthy individuals. After RT, expression was upregulated in apoptosis inducer and inhibitor genes, EDA2R and MDM2, and in POLH, a DNA-repair polymerase. Expression was downregulated in six members of the histone cluster family, e.g., HIST1H3B. Gene expression related to proliferation and differentiation was altered, including MKI67 (downregulated), which encodes the Ki-67-proliferation marker, and KRT16 (upregulated), which encodes keratin16. These alterations were not associated with the clinical mucositis grade. However, the expression of LY6G6C, which encodes a surface immunoregulatory protein, was upregulated before treatment in three cases of clinical none/mild mucositis, but not in four cases of ulcerative mucositis. CONCLUSION: RT caused molecular changes related to apoptosis, DNA-damage, DNA-repair, and proliferation without a correlation to the severity of clinical mucositis. LY6G6C may be a potential protective biomarker for ulcerative mucositis. Based on these results, our study model of consecutive human biopsies will be useful in designing a prospective clinical validation trial to characterize molecular mucositis and identify predictive biomarkers.