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Gemcitabine/carboplatin-induced myelosuppressive adverse drug reactions (ADRs) are clinical problems leading to patient suffering and dose alterations. There is a need for personalised medicine to improve treatment effects and patients' well-being. We tested four genetic variants, rs11141915, rs1901440, rs12046844 and rs11719165, previously suggested as potential biomarkers for gemcitabine-induced leukopenia/neutropenia in Japanese patients, in 213 Swedish gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients. DNA was genotyped using TaqMan probes and real-time PCR. The relationships between the risk alleles and low toxicity (non-ADR: Common Terminology Criteria for Adverse Events [CTCAE] grades 0) or high toxicity (ADR: CTCAE grades 3-4) of platelets, leukocytes and neutrophils were evaluated using Fisher's exact test. The risk alleles did not correlate with myelosuppression, and the strongest borderline significance (not withstanding adjustment for multiple testing) was for rs1901440 (neutropenia, p = 0.043) and rs11719165 (leukopenia, p = 0.049) where the risk alleles trended towards lower toxicity, contrasting with previous study findings. Risk alleles and higher risk scores were more common among our patients. We conclude that the genetic variants do not apply to Swedish patients treated with gemcitabine/carboplatin. However, they can still be important in other populations and cohorts, especially in a gemcitabine monotherapy setting, where the causal genetic variation might influence myelosuppressive ADRs.

Small extracellular vesicles (sEVs) have in recent years evolved as a source of biomarkers for disease diagnosis and therapeutic follow up. sEV samples derived from multicellular organisms exhibit a high heterogeneous repertoire of vesicles which current methods based on ensemble measurements cannot capture. In this work we present droplet barcode sequencing for protein analysis (DBS-Pro) to profile surface proteins on individual sEVs, facilitating identification of sEV-subtypes within and between samples. The method allows for analysis of multiple proteins through use of DNA barcoded affinity reagents and sequencing as readout. High throughput single vesicle profiling is enabled through compartmentalization of individual sEVs in emulsion droplets followed by droplet barcoding through PCR. In this proof-of-concept study we demonstrate that DBS-Pro allows for analysis of single sEVs, with a mixing rate below 2%. A total of over 120,000 individual sEVs obtained from a NSCLC cell line and from malignant pleural effusion (MPE) fluid of NSCLC patients have been analyzed based on their surface proteins. We also show that the method enables single vesicle surface protein profiling and by extension characterization of sEV-subtypes, which is essential to identify the cellular origin of vesicles in heterogenous samples., QC 20221128

In recent studies, small cell lung cancer (SCLC) treatment guidelines based on Veterans’ Administration Lung Study Group limited/extensive disease staging and resulted in broad and inseparable prognostic subgroups. Evidence suggests that the eight versions of tumor, node, and metastasis (TNM) staging can play an important role to address this issue. The aim of the present study was to improve the detection of prognostic subgroups from a real-word data (RWD) cohort of patients and analyze their patterns using a development pipeline with thoracic oncologists and machine learning methods. The method detected subgroups of patients informing unsupervised learning (partition around medoids) including the impact of covariates on prognosis (Cox regression and random survival forest). An analysis was carried out using patients with SCLC (n = 636) with stage IIIA–IVB according to TNM classification. The analysis yielded k = 7 compacted and well-separated clusters of patients. Performance status (Eastern Cooperative Oncology Group-Performance Status), lactate dehydrogenase, spreading of metastasis, cancer stage, and CRP were the baselines that characterized the subgroups. The selected clustering method outperformed standard clustering techniques, which were not capable of detecting meaningful subgroups. From the analysis of cluster treatment decisions, we showed the potential of future RWD applications to understand disease, develop individualized therapies, and improve healthcare decision making., QC 20221115

We present an approach to improve the detection sensitivity of a streaming current-based biosensor for membrane protein profiling of small extracellular vesicles (sEVs). The experimental approach, supported by theoretical investigation, exploits electrostatic charge contrast between the sensor surface and target analytes to enhance the detection sensitivity. We first demonstrate the feasibility of the approach using different chemical functionalization schemes to modulate the zeta potential of the sensor surface in a range -16.0 to -32.8 mV. Thereafter, we examine the sensitivity of the sensor surface across this range of zeta potential to determine the optimal functionalization scheme. The limit of detection (LOD) varied by 2 orders of magnitude across this range, reaching a value of 4.9 x 10(6) particles/mL for the best performing surface for CD9. We then used the optimized surface to profile CD9, EGFR, and PD-L1 surface proteins of sEVs derived from non-small cell lung cancer (NSCLC) cell-line H1975, before and after treatment with EGFR tyrosine kinase inhibitors, as well as sEVs derived from pleural effusion fluid of NSCLC adenocarcinoma patients. Our results show the feasibility to monitor CD9, EGFR, and PD-L1 expression on the sEV surface, illustrating a good prospect of the method for clinical application.

Background: Dissemination of non-small-cell lung cancer (NSCLC) in the central nervous system is a frequent and challenging clinical problem. Systemic or local therapies rarely prolong survival and have modest activity regarding local control. Alterations in gene expression in brain metastasis versus primary tumour may increase aggressiveness and impair therapeutic efforts. Methods: We identified 25 patients with surgically removed NSCLC brain metastases in two different patient cohorts. For 13 of these patients, primary tumour samples were available. Gene expression analysis using the nCounter (R) PanCancer Immune Profiling gene expression panel (nanoString technologies Inc.) was performed in brain metastases and primary tumour samples. Identification of differentially expressed genes was conducted on normalized data using the nSolver analysis software. Results: We compared gene expression patterns in brain metastases with primary tumours. Brain metastasis samples displayed a distinct clustering pattern compared to primary tumour samples with a statistically significant downregulation of genes related to immune response and immune cell activation. Results from KEGG term analysis on differentially expressed genes revealed a concomitant enrichment of multiple KEGG terms associated with the immune system. We identified a 12-gene immune signature that clearly separated brain metastases from primary tumours. Conclusions: We identified a unique gene downregulation pattern in brain metastases compared with primary tumours. This finding may explain the lower intracranial efficacy of systemic therapy, especially immunotherapy, in brain metastasis of patients with NSCLC.

Objectives: Gemcitabine/carboplatin treatment is known to cause severe adverse drug reactions which can lead to the need for reduction or cessation of chemotherapy. It would be beneficial to identify patients at risk of severe hematological toxicity in advance before treatment start. This study aims to identify genetic markers for gemcitabine/carboplatin-induced leukopenia and neutropenia in non-small cell lung cancer patients. Material and methods: Whole-exome sequencing was performed on 215 patients. Association analysis was performed on single-nucleotide variants (SNVs) and genes, and the validation was based on an independent genome-wide association study (GWAS). Based on the association and validation analyses the genetic variants were then selected for and used in weighted genetic risk score (wGRS) prediction models for leukopenia and neutropenia. Results: Association analysis identified 50 and 111 SNVs, and 12 and 20 genes, for leukopenia and neutropenia, respectively. Of these SNVS 20 and 19 were partially validated for leukopenia and neutropenia, respectively. The genes SVIL (p = 2.48E-06) and EFCAB2 (p = 4.63E-06) were significantly associated with leukopenia contain the partially validated SNVs rs3740003, rs10160013, rs1547169, rs10927386 and rs10927387. The wGRS prediction models showed significantly different risk scores for high and low toxicity patients. Conclusion: We have identified and partially validated genetic biomarkers in SNVs and genes correlated to gemcitabine/carboplatin-induced leukopenia and neutropenia and created wGRS models for predicting the risk of chemotherapy-induced hematological toxicity. These results provide a strong foundation for further studies of chemotherapy-induced toxicity.