Identification of targetable EML4-ALK fusion proteins has revolutionized the treatment of a minor subgroup of non-small cell lung cancer (NSCLC) patients. Although fluorescence in situ hybridization (FISH) is regarded as the gold standard for detection of ALK rearrangements, ALK immunohistochemistry (IHC) is often used as screening tool in clinical practice. In order to unbiasedly analyze the diagnostic impact of such a screening strategy, we compared ALK IHC with ALK FISH in three large representative Swedish NSCLC cohorts incorporating clinical parameters and gene expression data. ALK rearrangements were detected using FISH on tissue microarrays (TMAs), including tissue from 851 NSCLC patients. In parallel, ALK protein expression was detected using IHC, applying the antibody clone D5F3 with two different protocols (the FDA approved Ventana CDx assay and our in house Dako IHC protocol). Gene expression microarray data (Affymetrix) was available for 194 patients. ALK rearrangements were detected in 1.7 % in the complete cohort and 2.0 % in the non-squamous cell carcinoma subgroup. ALK protein expression was observed in 1.8 and 1.4 % when applying the Ventana assay or the in house Dako protocol, respectively. The specificity and accuracy of IHC was high (> 98 %), while the sensitivity was between 69 % (Ventana) and 62 % (in house Dako protocol). Furthermore, only 67 % of the ALK IHC positive cases were positive with both IHC assays. Gene expression analysis revealed that 6/194 (3 %) tumors showed high ALK gene expression (≥ 6 AU) and of them only three were positive by either FISH or IHC. The overall frequency of ALK rearrangements based on FISH was lower than previously reported. The sensitivity of both IHC assays was low, and the concordance between the FISH and the IHC assays poor, questioning current strategies to screen with IHC prior to FISH or completely replace FISH by IHC.

The droplet digital PCR (ddPCR) system enables high-sensitivity detection of nucleic acids and direct absolute quantification of the targets. The aim of this research was to evaluate this system for viral load (VL) analysis of the human papillomavirus (HPV) genotypes HPV31, 35, 39, 51 and 56 measured in number of viral particles per cell. The sample types used for the optimization of the ddPCR assay were formalin-fixed paraffin-embedded (FFPE) tissues and cervical liquid cytology samples. The presently optimized ddPCR assays, together with assays optimized previously for HPV16, 18, 33 and 45, with the same ddPCR method, were used for the VL analysis of cervical tumor samples. Results published previously on the present study cohort showed that women with a cervical tumor containing multiple high-risk HPV genotypes had a worse prognosis compared to women with single-genotype-infected tumors. The VL was therefore analyzed in this study for the same cohort, as a possible explanatory factor to the prognostic differences. The results of the optimization part of the study, with analysis of VL using ddPCR in DNA from varying sample types (FFPE and liquid cytology samples), showed that each of the five assays demonstrated good inter- and intra-assay means with a coefficient of variation (CV) under 8% and 6% respectably. The cohort results showed no difference in VL between tumors with multiple and single HPV infections, and therefore did most likely not constitute a contributing factor for prognostic differences observed previously. However, tumors from women aged 60 years or older or containing certain HPV genotypes and genotype genera were associated with a higher VL.

Objectives: HPV‐positive oropharyngeal squamous cell carcinoma (OPSCC) and head and neck carcinoma of unknown primary (HNCUP) are increasing. Despite good prognosis, recurrence rates range from 10% to 25%. Surveillance with clinical controls and imaging is not always reliable. Circulating tumour human papillomavirus DNA (ctHPV‐DNA) has emerged as a potential biomarker for treatment evaluation and detection of recurrence. We aimed to investigate the correlation between ctHPV‐DNA in HPV+ OPSCC/HNCUP and radiologic tumour burden. Additionally, we sought to assess whether ctHPV‐DNA could serve as a tool in treatment evaluation.Design: A prospective observational cohort study.Setting: This multicenter study involved three otolaryngology units located in central Sweden. We utilised HPV genotype‐specific assays for droplet digital PCR (ddPCR) to detect ctHPV‐DNA in plasma at diagnosis and follow‐up. ctHPV‐DNA levels were correlated to radiological tumour burden and radiological response using the Kendall Rank correlation coefficient and the Kruskal–Wallis test.Participants: Patients with HPV+ OPSCC/HNCUP undergoing definitive (chemo)radiotherapy and enrolled in the CIRCOS study.Results: Out of 54 patients, 51 were eligible for analyses. At baseline, ctHPV‐DNA was detectable in 88%. A majority of patients with a favourable radiological evaluation according to RECIST had a corresponding undetectable ctHPV‐DNA at follow‐up. The levels of ctHPV‐DNA at baseline correlated with total tumour volume and nodal volume (rτ = 0.39, p < 0.01, respectively rτ = 0.26, p < 0.01).Conclusion: ctHPV‐DNA shows correlation with tumour burden. This study strengthens the role of ctHPV‐DNA as a promising biomarker for treatment evaluation in HPV‐related OPC/HNCUP. With further research on serial plasma sampling, ctHPV‐DNA could complement radiological treatment evaluation in HPV+ OPSCC/HNCUP. Trial Registration: NCT05904327 [ClinicalTrials.gov]

Introduction: The aim of the study was to investigate whether biobanked liquid-based cytology (LBC) vaginal samples could be reanalyzed for the biomarkers HPV DNA and mRNA without loss of sensitivity. Methods: One hundred LBC samples with ASCUS or CIN1 were tested for HPV DNA and mRNA before and after biobanking. DNA analysis targeted the viral genes E6 and E7, 12 high-risk and 2 low-risk HPV types together with the human control gene HBB, using real-time PCR. The Aptima HPV assay was used for mRNA analysis of 14 high-risk HPV types. Results: With Aptima there was 84% agreement between results before and after biobanking. The sensitivity and specificity were 0.79 (95% CI, 0.68-0.88) and 0.94 (95% CI, 0.80-0.99), respectively. With the DNA-based method, the agreement between results was 87%, the sensitivity 0.85 (95% CI, 0.75-0.92) and the specificity 0.95 (95% CI, 0.77-1.00). Both methods presented a significant difference between positive results before and after biobanking; McNemar test: p = 0.004, p = 0.003, Cohen's kappa: 0.67 (95% CI, 0.53-0.81), 0.68 (95% CI, 0.52-0.84). Cycle threshold values for the DNA method were higher for all genotypes after biobanking, except for HPV-59. Some loss of sensitivity was seen after biobanking but the concordance between HPV detection before and after biobanking was good for both evaluated methods. Conclusions: Biobanking of LBC vaginal samples offers a good platform for HPV testing and could be extended to further molecular analyses. However, in order to ensure a valid test result a larger portion needs to be analyzed from the biobanked sample.

We investigated differences in circulating cell-free DNA (cfDNA) methylation between patients with cancer and those presenting with severe, nonspecific symptoms. Plasma cfDNA from 229 patients was analyzed, of whom 37 were diagnosed with a wide spectrum of cancer types within 12 months. Samples underwent enzymatic conversion, library preparation, and enrichment using the NEBNext workflow and Twist pan-cancer methylation panel, followed by sequencing. Methylation analysis was performed with nf-core/methylseq. Differentially methylated regions (DMRs) were identified with DMRichR. Machine learning with cross-validation was used to classify cancer and controls. The classifier was applied to an external validation set of 144 controls previously unseen by the model. Cancer samples showed higher overall CpG methylation than controls (1.82% vs. 1.34%, p < 0.001). A total of 162 DMRs were detected, 95.7% being hypermethylated in cancer. Machine learning identified 20 key DMRs for classification between cancer and controls. The final model achieved an AUC of 0.88 (83.8% sensitivity, 83.8% specificity), while mean cross-validation performance reached an AUC of 0.73 (57.1% sensitivity, 77.5% specificity). The specificity of the classifier on unseen control samples was 79.2%. Distinct methylation differences and DMR-based classification support cfDNA methylation as a robust biomarker for cancer detection in patients with confounding conditions.