Robin Mjelle

The CT26 colorectal cancer and KPC pancreatic cancer models are widely used syngeneic murine tumor models with different characteristics which influence their response to cancer treatments. Despite their extensive use, their proteomic landscapes remain insufficiently characterized. We applied label-free quantitative proteomics to KPC and CT26 tumors across three replicate experiments to identify biological differences between the models, explore immune cell deconvolution from bulk proteomic data, and assess reproducibility across experiments and preprocessing strategies. CT26 and KPC tumors showed distinct global proteomic profiles as KPC tumors were enriched in proteins associated with extracellular matrix remodeling, cytoskeletal organization, adhesion, and metabolic adaptation, whereas CT26 tumors were enriched in proteins related to proliferation, RNA processing, and translation. Immune cell deconvolution indicated qualitative differences in immune cell composition, where CT26 tumors had a higher level of CD4 T cells and bone marrow-derived macrophages and lower level of bone marrow-derived dendritic cells compared with KPC tumors. However, results were sensitive to missing-value handling and limited by availability of reference samples and lack of orthogonal validation. Across experiments and analytical workflows, the main differences between CT26 and KPC were reproducible, whereas experiment-specific effects were more variable. While further research is needed to assess the clinical relevance of our findings, they provide a proteomic framework for understanding biological differences between CT26 and KPC tumors and highlight the importance of reproducibility in proteomics-based tumor profiling.

Cancer cachexia is a complex syndrome marked by involuntary weight and muscle loss, often driven by systemic inflammation. This multicenter, longitudinal observational study investigated circulating microRNA (miRNA) profiles in patients with unresectable locally advanced or metastatic colorectal cancer, comparing those with and without cachexia and inflammation. A total of 168 patients were categorized into four groups based on cachexia and C-reactive protein (CRP) levels. Cachexia was defined using the 2011 consensus criteria, incorporating weight loss, low BMI, and sarcopenia. Patients with both cachexia and systemic inflammation exhibited significantly distinct miRNA profiles as well as poorer overall survival (HR 2.10, p < 0.001) compared to patients with neither condition. No significant differences were observed in patients lacking either cachexia or inflammation or both. Inflammatory cachexia emerged as a biologically distinct entity, with 82 differentially expressed miRNAs. The miR-320-family, miR-6087, miR-4488, miR-29a-3p, miR-194-5p, and miR-10a-5p were most altered, several of which are linked to muscle mass, metabolism, lipid, and protein synthesis. These findings highlight the pivotal role of systemic inflammation in cancer cachexia and support its inclusion in diagnostic criteria. Moreover, circulating miRNAs may serve as promising biomarkers for identifying cachexia in cancer patients.

Background: This study aimed to investigate the potential of circulating small RNAs (sRNAs) as predictive biomarkers for future colorectal cancer (CRC). The study analyzed serum samples from pre-diagnostic CRC patients in two prospective biobanks. Methods: Serum samples from 142 pre-diagnostic CRC patients, from the Finnish Maternity Cohort (FMC) and The HUNT Study (HUNT2), were subjected to small RNA sequencing. The study compared sRNA expression in CRC cases with controls, considering diverse sRNA classes. Results: Analysis revealed diverse miRNA expression patterns with notable variations in future metastatic cases. Specifically, miR-223-3p and miR-21-5p showed significant up-regulation in future metastatic cases in the FMC cohort. Consistent changes were observed across cohorts, with miR-584-5p, miR-30c-5p, miR-146a-5p, miR-10a-5p, and miR-1306-5p showing up-regulation in future metastatic cases. Conclusions: The study identified potential serum miRNA biomarkers associated with metastatic CRC, though statistical significance varied. These findings contribute to the understanding of miRNA profiles in pre-diagnostic CRC patients, emphasizing the need for further exploration of non-invasive biomarkers in large prospective studies.

Metastatic colorectal cancer (mCRC) presents significant challenges in clinical management due to its heterogeneity and variable response to treatment. In this study, we conducted comprehensive small RNA (sRNA) sequencing analyses to identify sRNA biomarkers associated with survival and treatment response in mCRC patients. We measured serum sRNAs before and after chemotherapy treatment in a discovery cohort of 189 mCRC patients. Our analysis revealed 25 microRNAs (miRNA) as significantly associated with overall survival at baseline. We found that 11 of the 25 significant miRNAs were also significant in an independent validation cohort of 20 mCRC patients, including the top five miRNAs from the discovery cohort. Importantly, all but four of the 25 significant miRNAs from the discovery cohort had hazard ratios in the same direction in the validation cohort. Among the 25 significant miRNAs, we identified the miR-320 family of miRNAs as the strongest independent prognostic marker, with high baseline levels correlating with poor survival outcomes. Furthermore, post-treatment levels of the same miRNAs were even more predictive of overall survival, emphasizing the prognostic value of serum changes in miRNA levels before and after treatment. Moreover, we observed significant changes in serum miRNAs and other sRNAs when comparing samples before and after chemotherapy, with distinct expression patterns between responders and non-responders. Leveraging these differential expression patterns, we established a serum sRNA signature that accurately predicts response to chemotherapy with an area under the curve (AUC) of 0.8. In summary, our study highlights the prognostic and predictive potential of sRNA biomarkers in mCRC, offering valuable insights into patient stratification and personalized treatment approaches.

(1) Background: Analysis of tumor DNA by next-generation sequencing (NGS) plays various roles in the classification and management of cancer. This study aimed to assess the performance of two similar and large, comprehensive gene panels with a focus on clinically relevant variant detection and tumor mutation burden (TMB) assessment; (2) Methods: DNA from 19 diagnostic small cell lung cancer biopsies and an AcroMetrix™ assessment sample with >500 mutations were sequenced using Oncomine^™ Comprehensive Assay Plus (OCAP) on the Ion Torrent platform and TruSight Oncology 500 Assay (TSO500) on the Illumina platform; (3) Results: OCAP and TSO500 achieved comparable NGS quality, such as mean read coverage and mean coverage uniformity. A total of 100% of the variants in the diagnostic samples and 80% of the variants in the AcroMetrix™ assessment sample were detected by both panels, and the panels reported highly similar variant allele frequency. A proportion of 14/19 (74%) samples were classified in the same TMB category; (4) Conclusions: Comparable results were obtained using OCAP and TSO500, suggesting that both panels could be applied to screen patients for enrolment in personalized cancer treatment trials.

Background: Small RNAs (sRNAs), a heterogenous group of non-coding RNAs, are emerging as promising molecules for cancer patient risk stratification and as players in tumour pathogenesis. Here, we have studied microRNAs (miRNAs) and other sRNAs in relation to survival and disease severity in multiple myeloma. Methods: We comprehensively characterised sRNA expression in multiple myeloma patients by performing sRNA-sequencing on myeloma cells isolated from bone marrow aspirates of 86 myeloma patients. The sRNA expression profiles were correlated with the patients’ clinical data to investigate associations with survival and disease subgroups, by using cox proportional hazards (coxph) -models and limma-voom, respectively. A publicly available sRNA dataset was used as external validation (n = 151). Results: We show that multiple miRNAs are differentially expressed between ISS Stage I and III. Interestingly, we observed the downregulation of seven different U2 spliceosomal RNAs, a type of small nuclear RNAs in severe disease stages. Further, by a discovery-based approach, we identified miRNA miR-105-5p as a predictor of poor overall survival (OS) in multiple myeloma. Multivariate analysis showed that miR-105-5p predict OS independently of established disease markers. Conclusions: Overexpression of miR-105-5p in myeloma cells correlates with reduced OS, potentially improving prognostic risk stratification in multiple myeloma.

Multiple myeloma (MM) is an incurable cancer of terminally differentiated plasma cells that proliferate in the bone marrow. miRNAs are promising biomarkers for risk stratification in MM and several miRNAs are shown to have a function in disease pathogenesis. However, to date, surprisingly few miRNA-mRNA interactions have been described for and functionally validated in MM. In this study, we performed miRNA-seq and mRNA-seq on CD138 + cells isolated from bone marrow aspirates of 86 MM patients to identify novel interactions between sRNAs and mRNAs. We detected 9.8% significantly correlated miRNA-mRNA pairs of which 5.17% were positively correlated and 4.65% were negatively correlated. We found that miRNA-mRNA pairs that were predicted by in silico target-prediction algorithms were more negatively correlated than non-target pairs, indicating functional miRNA targeting and that correlation between miRNAs and mRNAs from patients can be used to identify miRNA-targets. mRNAs for negatively correlated miRNA-mRNA target pairs were associated with gene ontology terms such as autophagy, protein degradation and endoplasmic stress response, reflecting important processes in MM. Targets for two specific miRNAs, miR-125b-5p and miR-365b-3p, were functionally validated in MM cell line transfection experiments followed by RNA-sequencing and qPCR. In summary, we identified functional miRNA-mRNA target pairs by correlating miRNA and mRNA data from primary MM cells. We identified several target pairs that are of potential interest for further studies. The data presented here may serve as a hypothesis-generating knowledge base for other researchers in the miRNA/MM field. We also provide an interactive web application that can be used to exploit the miRNA-target interactions as well as clinical parameters associated to these target-pairs.