Feasibility and early clinical impact of precision medicine for late-stage cancer patients in a regional public academic hospital.

AIM: Our goal was to describe a precision medicine program in a regional academic hospital, characterize features of included patients and present early data on clinical impact. MATERIALS AND METHODS: We prospectively included 163 eligible patients with late-stage cancer of any diagnosis from June 2020 to May 2022 in the Proseq Cancer trial. Molecular profiling of new or fresh frozen tumor biopsies was done by WES and RNAseq with parallel sequencing of non-tumoral DNA as individual reference. Cases were presented at a National Molecular Tumor Board (NMTB) for discussion of targeted treatment. Subsequently, patients were followed for at least 7 months. RESULTS: 80% (N = 131) of patients had a successful analysis done, disclosing at least one pathogenic or likely pathogenic variant in 96%. A strongly or potentially druggable variant was found in 19% and 73% of patients, respectively. A germline variant was identified in 2.5%. Median time from trial inclusion to NMTB decision was one month. One third (N = 44) of patients who underwent molecularly profiling were matched with a targeted treatment, however, only 16% were either treated (N = 16) or are waiting for treatment (N = 5), deteriorating performance status being the primary cause of failure. A history of cancer among 1st degree relatives, and a diagnosis of lung or prostate cancer correlated with greater chance of targeted treatment being available. The response rate of targeted treatments was 40%, the clinical benefit rate 53%, and the median time on treatment was 3.8 months. 23% of patients presented at NMTB were recommended clinical trial participation, not dependent on biomarkers. CONCLUSIONS: Precision medicine in end-stage cancer patients is feasible in a regional academic hospital but should continue within the frame of clinical protocols as few patients benefit. Close collaboration with comprehensive cancer centers ensures expert evaluations and equality in access to early clinical trials and modern treatment.

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.

Feasibility of a multimodal intervention on malnutrition in patients with lung cancer during primary anti-neoplastic treatment.

BACKGROUND: Wasting of body mass and skeletal muscle frequently develops in patients with cancer and is associated with impaired functional ability and poor clinical outcome and quality of life. This study aimed to evaluate the feasibility and explore the effect of a multimodal intervention targeting nutritional status in patients with non-small cell lung cancer receiving primary anti-neoplastic treatment. Additionally, predictive and prognostic factors of gaining skeletal muscle were explored. METHODS: This was a single-centre multimodal intervention trial using a historical control group. The multimodal intervention involved fish oil intake (2 g of eicosapentaenoic acid or docosahexaenoic acid daily), regular dietary counselling and unsupervised physical exercise twice weekly during the first three cycles of primary anti-neoplastic treatment. Feasibility was assessed through recruitment rate, completion rate and compliance rate with the intervention. Differences in skeletal muscle, body weight, and physical function between the intervention and historical control groups were analysed. Factors contributing to increased skeletal muscle were explored using univariate and multivariate ordinal logistic regression analyses. RESULTS: The recruitment and completion rates were 0.48 (n = 59/123) and 0.80 (n = 46/59), respectively. The overall compliance rate with all five individual interventions was 0.60 (n = 28/47). The individual compliance rates were 0.81 (n = 38/47) with fish oil intake, 0.94 (n = 44/47) with energy intake, 0.98 (n = 46/47) with protein intake, 0.51 (n = 24/47) with resistance exercise and 0.57 (n = 27/47) with aerobic exercise. No mean differences in skeletal muscle, body weight, or physical function were found between the intervention and control groups. However, a larger proportion of patients in the intervention group gained skeletal muscle (p < 0.02). The identified contributing factors of muscle gain were weight gain (OR, 1.3; p = 0.01), adherence to treatment plan (OR, 4.6; p = 0.02), stable/partial response (OR, 3.3; p = 0.04) and compliance to the intervention (OR, 7.4; p = 0.01). Age, sex, tumour stage, performance status, treatment type and baseline cachexia did not predict muscle gain. CONCLUSION: This three-dimensional intervention in patients with lung cancer undergoing primary anti-neoplastic treatment was feasible and increased the proportion of patients gaining skeletal muscle. Dietary counselling and fish oil use were useful strategies. The motivation for conducting unsupervised physical intervention was low. Clinical trials.gov identifier: NCT04161794.

Pre-treatment serum vitamin D deficiency is associated with increased inflammatory biomarkers and short overall survival in patients with pancreatic cancer.

BACKGROUND: Vitamin D deficiency and inflammation are associated with increased mortality. We investigated the relationship between pre-treatment serum vitamin D levels, inflammatory biomarkers (IL-6, YKL-40 and CRP) and overall survival (OS) in pancreatic ductal adenocarcinoma (PDAC) patients. METHODS: Pre-treatment serum vitamin D, IL-6, YKL-40 and CRP levels were determined in 1,267 patients with PDAC enrolled from July 2008 to September 2018 in the prospective BIOPAC study (NCT03311776). The patients were grouped according to vitamin D levels: sufficient >50 nmol/L, insufficient 25-50 nmol/L and deficient <25 nmol/L. RESULTS: Across all tumour stages, vitamin D-deficient patients had the highest median levels of IL-6 (8.3 pg/mL, range 0.7-91), YKL-40 (177 ng/ml, range 25-5279) and CRP (15.5 mg/L, range 0.8-384). The resected stage I and II patients with vitamin D deficiencies had a shorter median OS, 18.3 months (95% CI, 12.1-31.5 months) than those with sufficient levels, 29.7 months (95% CI, 22.3-36.1 months), and the hazard ratio for death was 1.55 (95% CI, 1.04-2.31; p = 0.03). In advanced PDAC, there was no significant difference in OS between the vitamin D groups. CONCLUSIONS: Vitamin D deficiency was associated with increased inflammatory biomarkers in all PDAC stages. The resected stage I and II patients with sufficient vitamin D levels had a higher OS than those with a vitamin D deficiency. However, there was no correlation between vitamin D levels and survival in advanced PDAC. Future studies need to investigate vitamin D supplementation effects on survival in PDAC.

Optimizing Outcome Prediction in Diffuse Large B-Cell Lymphoma by Use of Machine Learning and Nationwide Lymphoma Registries: A Nordic Lymphoma Group Study.

PURPOSE: Prognostic models for diffuse large B-cell lymphoma (DLBCL), such as the International Prognostic Index (IPI) are widely used in clinical practice. The models are typically developed with simplicity in mind and thus do not exploit the full potential of detailed clinical data. This study investigated whether nationwide lymphoma registries containing clinical data and machine learning techniques could prove to be useful for building modern prognostic tools. PATIENTS AND METHODS: This study was based on nationwide lymphoma registries from Denmark and Sweden, which include large amounts of clinicopathologic data. Using the Danish DLBCL cohort, a stacking approach was used to build a new prognostic model that leverages the strengths of different survival models. To compare the performance of the stacking approach with established prognostic models, cross-validation was used to estimate the concordance index (C-index), time-varying area under the curve, and integrated Brier score. Finally, the generalizability was tested by applying the new model to the Swedish cohort. RESULTS: In total, 2,759 and 2,414 patients were included from the Danish and Swedish cohorts, respectively. In the Danish cohort, the stacking approach led to the lowest integrated Brier score, indicating that the survival curves obtained from the stacking model fitted the observed survival the best. The C-index and time-varying area under the curve indicated that the stacked model (C-index: Denmark [DK], 0.756; Sweden [SE], 0.744) had good discriminative capabilities compared with the other considered prognostic models (IPI: DK, 0.662; SE, 0.661; and National Comprehensive Cancer Network-IPI: DK, 0.681; SE, 0.681). Furthermore, these results were reproducible in the independent Swedish cohort. CONCLUSION: A new prognostic model based on machine learning techniques was developed and was shown to significantly outperform established prognostic indices for DLBCL. The model is available at https://lymphomapredictor.org .

Human B-cell cancer cell lines as a preclinical model for studies of drug effect in diffuse large B-cell lymphoma and multiple myeloma.

Drug resistance in cancer refers to recurrent or primary refractory disease following drug therapy. At the cellular level, it is a consequence of molecular functions that ultimately enable the cell to resist cell death-one of the classical hallmarks of cancer. Thus, drug resistance is a fundamental aspect of the cancer cell phenotype, in parallel with sustained proliferation, immortality, angiogenesis, invasion, and metastasis. Here we present a preclinical model of human B-cell cancer cell lines used to identify genes involved in specific drug resistance. This process includes a standardized technical setup for specific drug screening, analysis of global gene expression, and the statistical considerations required to develop resistance gene signatures. The state of the art is illustrated by the first-step classical drug screen (including the CD20 antibody rituximab, the DNA intercalating topoisomerase II inhibitor doxorubicin, the mitotic inhibitor vincristine, and the alkylating agents cyclophosphamide and melphalan) along with the generation of gene lists predicting the chemotherapeutic outcome as validated retrospectively in clinical trial datasets. This B-cell lineage-specific preclinical model will allow us to initiate a range of laboratory studies, with focus on specific gene functions involved in molecular resistance mechanisms.

A model system for assessing and comparing the ability of exon microarray and tag sequencing to detect genes specific for malignant B-cells.

BACKGROUND: Malignant cells in tumours of B-cell origin account for 0.1% to 98% of the total cell content, depending on disease entity. Recently, gene expression profiles (GEPs) of B-cell lymphomas based on microarray technologies have contributed significantly to improved sub-classification and diagnostics. However, the varying degrees of malignant B-cell frequencies in analysed samples influence the interpretation of the GEPs. Based on emerging next-generation sequencing technologies (NGS) like tag sequencing (tag-seq) for GEP, it is expected that the detection of mRNA transcripts from malignant B-cells can be supplemented. This study provides a quantitative assessment and comparison of the ability of microarrays and tag-seq to detect mRNA transcripts from malignant B-cells. A model system was established by eight serial dilutions of the malignant B-cell lymphoma cell line, OCI-Ly8, into the embryonic kidney cell line, HEK293, prior to parallel analysis by exon microarrays and tag-seq. RESULTS: We identified 123 and 117 differentially expressed genes between pure OCI-Ly8 and HEK293 cells by exon microarray and tag-seq, respectively. There were thirty genes in common, and of those, most were B-cell specific. Hierarchical clustering from all dilutions based on the differentially expressed genes showed that neither technology could distinguish between samples with less than 1% malignant B-cells from non-B-cells. A novel statistical concept was developed to assess the ability to detect single genes for both technologies, and used to demonstrate an inverse proportional relationship with the sample purity. Of the 30 common genes, the detection capability of a representative set of three B-cell specific genes--CD74, HLA-DRA, and BCL6 - was analysed. It was noticed that at least 5%, 13% and 22% sample purity respectively was required for detection of the three genes by exon microarray whereas at least 2%, 4% and 51% percent sample purity of malignant B-cells were required for tag-seq detection. CONCLUSION: A sample purity-dependent loss of the ability to detect genes for both technologies was demonstrated. Taq-seq, in comparison to exon microarray, required slightly less malignant B-cells in the samples analysed in order to detect the two most abundantly expressed of the selected genes. The results show that malignant cell frequency is an important variable, with fundamental impact when interpreting GEPs from both technologies.