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Background: Sensitive and specific blood biomarkers for early detection of colorectal (CRC), lung (LuCa) and ovarian (OvCa) cancers are highly warranted. The current blood tests often need to be complemented with other clinical methods to achieve adequate diagnostic performance. Recent progress in the molecular profiling of plasma from cancer patients holds potential for improved non-invasive screening. Here, we aimed to identify composite proteomic and metabolomic plasma biomarkers for early cancer detection with performances that exceed those of existing FDA-approved blood and stool-based diagnostic tests for CRC, LuCa and OvCa.

Methods: In a case-control study using samples from the U-CAN and EpiHealth biobanks, we measured plasma levels of 165 proteins and 244 metabolites in 818 patients with CRC, LuCa and OvCa at diagnosis, 119 patients with non-malignant conditions of the corresponding organs, and 1,129 healthy individuals. We performed an exhaustive search over all cut-off values of the ROC for all combinations of up to 4 proteins and metabolites, implementing measures to minimize the impact of cross-cohort comparisons. Ultimately, we benchmarked candidate biomarkers performance to FDA approved blood tests in clinical use for detection of cancer. External validation was performed using publicly available datasets.

Results: We found biomarkers composed of 2-4 proteins separating cases of each tumor type from healthy controls with ROC AUC, respectively for CRC: CEACAM5, FLT1, IL19, Ferritin (AUC 0.89), LuCa: FNDC5, MDK, PLAUR, CEACAM5 (AUC 0.91) and OvCa: MUC16/CA125, PLG (AUC 0.97). The diagnostic performance of these biomarkers was comparable to, and in some instances surpassed, the performance of established tests, such as Epi proColon (AUC 0.82) and Cologuard (AUC 0.93). Metabolites were informative for tumor stage discrimination, especially in LuCa and OvCa. External validation in the CancerSeek dataset showed strong agreement in biomarker performance.

Conclusions: The composite protein biomarkers identified in this study represent a novel opportunity for detection, staging and differential diagnosis of common tumor types. Metabolites are more relevant for tumor staging than early detection. A limited number of well-performing analytes enables cost-effective implementation in clinical diagnostics and merits further evaluation.

High-grade serous ovarian carcinoma (HGSC) is the most lethal form of ovarian carcinoma, often diagnosed at advanced stages due to non-specific symptoms and the lack of reliable screening methods. This proof-of-concept study aimed to develop a robust TP53 mutation panel for detecting HGSC through targeted DNA sequencing in both liquid and solid biopsies. We constructed a custom TP53 gene panel and utilized a PCR-based unique molecular identifier approach for next-generation sequencing to analyze 94 samples from 11 patients diagnosed with HGSC, including primary tumor, plasma, ascites, ovarian cyst fluid, vaginal, endocervical and endometrial samples. Detected TP53 mutations were analyzed, categorized, and their frequencies calculated. Pathogenic TP53 mutations were identified in all patients, with 91% of the patients exhibiting one unique paired mutation across three or more sample types. The panel demonstrated high sensitivity and technical reproducibility, successfully detecting TP53 mutations across all sample types, with as little as 2.6 ng of DNA. TP53 mutations were consistently found in ascites, ovarian cyst fluid, and plasma samples, confirming the presence of pathogenic mutations in each sample type across all patients. This study underscores the potential of liquid biopsies in clinical molecular diagnostics. The TP53 mutation panel presented in this proof-of-concept study offers a promising approach for differential diagnostics and detection of HGSC, informative data prior to extended investigation and first-line treatment guidance.

Background: Ovarian cancer has the highest mortality of all gynecological cancers and surgery is commonly used as final diagnostic. Available literature indicates that women with benign tumors could often be conservatively managed, but accurate molecular tests are needed for triaging when gold-standard imaging techniques are inconclusive or lacking.

Methods: Here, we analyzed 5416 plasma proteins in two independent cohorts (N₁=171, N₂=233) with women surgically diagnosed with benign or malignant tumors. Using one cohort as discovery, we compared protein levels of benign tumors with early stage (I-II), late stage (III-IV) or any stage (I-IV) ovarian cancer and trained risk-score reporting multivariate models including a fixed cut-off for malignancy. Associations and model performance was then evaluated in the replication cohort.

Results: We identify 327 biomarker associations, corresponding to 191 unique proteins, and replicate 326 (99.7%). By comparing the 191 proteins with their corresponding tumor gene expression we find that only 11% (21/191) have significant correlation. Through analyzes of protein-protein correlation networks, we find that 62 of the 191 proteins have high correlation with at least one other protein, suggesting that many of the associations are secondary effects. In the replication cohort, our model has areas under the curve (AUC = 0.96) corresponding to 97% sensitivity at 68% specificity. For early-stage tumors, we estimate the sensitivity to 91% at a specificity of 68% as compared to 85% and 54% for CA-125 alone.

Conclusions: Our results indicates that up to one third of benign cases can be identified by molecular measures thereby reducing the need for diagnostic surgery.

This paper summarizes the efforts to develop novel biomarkers for diagnosis and screening of the three main gynecological cancers, cervical, endometrial, and ovarian cancer, with an emphasis on research performed during the last 20 years in Uppsala. A cervical cancer screening program has existed in Sweden since 1966 using cytology as the primary test. Over the last two decades, research has provided the scientific base for a transition to self-sampling to improve convenience of the woman and achieve higher population coverage, and use of human papillomavirus as the primary test. Also, efficient prophylactic vaccines and more efficient treatment strategies of women with cervical dysplasia have been introduced. Together, these medical tools have the potential to eradicate cervical cancer by 2120, as envisaged by WHO. By contrast, efficient biomarkers for endometrial and ovarian cancer are still lacking. Through the use of high-throughput proteomics, we have identified novel plasma protein biomarkers to be used in the diagnosis of women with adnexal ovarian mass upon transvaginal ultrasound, and possibly also for early detection in population screening. Similarly, novel biomarkers for the diagnosis of endometrial cancer are being evaluated. To establish a population-based screening program requires careful cost-benefit analyses. One alternative would be to broaden the focus of the current cervical cancer screening program to include also the novel biomarkers for ovarian and endometrial cancer, and thereby achieve screening for all three gynecological cancers. A program that screens for all three diseases could increase motivation to participate and thereby population coverage.

Hypertension affects more than one billion people worldwide. Here we identify 113 novel loci, reporting a total of 2,103 independent genetic signals (P < 5 × 10^-8) from the largest single-stage blood pressure (BP) genome-wide association study to date (n = 1,028,980 European individuals). These associations explain more than 60% of single nucleotide polymorphism-based BP heritability. Comparing top versus bottom deciles of polygenic risk scores (PRSs) reveals clinically meaningful differences in BP (16.9 mmHg systolic BP, 95% CI, 15.5-18.2 mmHg, P = 2.22 × 10^-126) and more than a sevenfold higher odds of hypertension risk (odds ratio, 7.33; 95% CI, 5.54-9.70; P = 4.13 × 10^-44) in an independent dataset. Adding PRS into hypertension-prediction models increased the area under the receiver operating characteristic curve (AUROC) from 0.791 (95% CI, 0.781-0.801) to 0.826 (95% CI, 0.817-0.836, ∆AUROC, 0.035, P = 1.98 × 10^-34). We compare the 2,103 loci results in non-European ancestries and show significant PRS associations in a large African-American sample. Secondary analyses implicate 500 genes previously unreported for BP. Our study highlights the role of increasingly large genomic studies for precision health research.

Ovarian cancer is the 8th most common cancer among women and has a 5-year survival of only 30-50%. While the survival is close to 90% for stage I tumours it is only 20% for stage IV. Current biomarkers are not sensitive nor specific enough, and novel biomarkers are urgently needed. We used the Explore PEA technology for large-scale analysis of 2943 plasma proteins to search for new biomarkers using two independent clinical cohorts. The discovery analysis using the first cohort identified 296 proteins that had significantly different levels in malign tumours as compared to benign and for 269 (91%) of these, the association was replicated in the second cohort. Multivariate modelling, including all proteins independent of their association in the univariate analysis, identified a model for separating benign conditions from malign tumours (stage I-IV) consisting of three proteins; WFDC2, KRT19 and RBFOX3. This model achieved an AUC of 0.92 in the replication cohort and a sensitivity and specificity of 0.93 and 0.77 at a cut-off developed in the discovery cohort. There was no statistical difference of the performance in the replication cohort compared to the discovery cohort. WFDC2 and KRT19 have previously been associated with ovarian cancer but RBFOX3 has not previously been identified as a potential biomarker. Our results demonstrate the ability of using high-throughput precision proteomics for identification of novel plasma protein biomarker for ovarian cancer detection.

INTRODUCTION: Blood pressure (BP) is a highly heritable trait with over 2000 underlying genomic loci identified to date. Although the kidney plays a key role, little is known about specific cell types involved in the genetic regulation of BP.

METHODS: Here, we applied stratified linkage disequilibrium score (LDSC) regression to connect BP genome-wide association studies (GWAS) results to specific cell types of the mature human kidney. We used the largest single-stage BP genome-wide analysis to date, including up to 1,028,980 adults of European ancestry, and single-cell transcriptomic data from 14 mature human kidneys, with mean age of 41 years.

RESULTS: Our analyses prioritized myofibroblasts and endothelial cells, among the total of 33 annotated cell type, as specifically involved in BP regulation (P < 0.05/33, i.e., 0.001515). Enrichment of heritability for systolic BP (SBP) was observed in myofibroblast cells in mature human kidney cortex, and enrichment of heritability for diastolic BP (DBP) was observed in descending vasa recta and peritubular capillary endothelial cells as well as stromal myofibroblast cells. The new finding of myofibroblast, the significant cell type for both BP traits, was consistent in 8 replication efforts using 7 sets of independent data, including in human fetal kidney, in East-Asian (EAS) ancestry, using mouse single-cell RNA sequencing (scRNA-seq) data, and when using another prioritization method.

CONCLUSION: Our findings provide a solid basis for follow-up studies to further identify genes and mechanisms in myofibroblast cells that underlie the regulation of BP.

Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioural traits and the disease aetiology of neuropsychiatric disorders. Here the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,000 individuals for 184 neuro-related proteins in human plasma. The analysis identified 125 cis-regulatory protein quantitative trait loci (cis-pQTL) and 164 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. At the cis-pQTL, multiple proteins shared a genetic basis with human behavioural traits such as alcohol and food intake, smoking and educational attainment, as well as neurological conditions and psychiatric disorders such as pain, neuroticism and schizophrenia. Integrating with established drug information, the causal inference analysis validated 52 out of 66 matched combinations of protein targets and diseases or side effects with available drugs while suggesting hundreds of repurposing and new therapeutic targets.

Early detection is key for increased survival in ovarian cancer, but no general screening program exists today due to lack of biomarkers and overall cost versus benefit over traditional clinical methods. Here, we used dried cervico-vaginal fluid (CVF) as sampling matrix coupled with mass spectrometry for detection of protein biomarkers. We find that self-collected CVF on paper cards yields robust results and is suitable for high-throughput proteomics. Artificial intelligence-based methods were used to identify an 11-protein panel that separates cases from controls. In validation data, the panel achieved a sensitivity of 0.97 (95% CI 0.91-1.00) at a specificity of 0.67 (0.40-0.87). Analyses of samples collected prior to development of symptoms indicate that the panel is informative also of future risk of disease. Dried CVF is used in cervical cancer screening, and our results opens the possibility for a screening program also for ovarian cancer, based on self-collected CVF samples.