Composite proteomic and metabolomic plasma biomarkers for detection of colorectal, lung and ovarian cancers

Composite proteomic and metabolomic plasma biomarkers for detection of colorectal, lung and ovarian cancers | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Composite proteomic and metabolomic plasma biomarkers for detection of colorectal, lung and ovarian cancers Jim Åkerrén Ögren , Joakim Ekström , Natallia Rameika , Emma Torell , Chatarina Larsson , Gunilla Enblad , View ORCID Profile Ivaylo Stoimenov , View ORCID Profile Patrick Micke , Ulf Gyllensten , Mats Hellström , Bengt Glimelius , Karin Stålberg , View ORCID Profile Tobias Sjöblom doi: https://doi.org/10.1101/2025.05.19.25326282 Jim Åkerrén Ögren 1 Department of Immunology, Genetics and Pathology, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joakim Ekström 1 Department of Immunology, Genetics and Pathology, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Natallia Rameika 1 Department of Immunology, Genetics and Pathology, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emma Torell 1 Department of Immunology, Genetics and Pathology, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chatarina Larsson 1 Department of Immunology, Genetics and Pathology, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gunilla Enblad 1 Department of Immunology, Genetics and Pathology, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ivaylo Stoimenov 1 Department of Immunology, Genetics and Pathology, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ivaylo Stoimenov Patrick Micke 1 Department of Immunology, Genetics and Pathology, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Patrick Micke Ulf Gyllensten 1 Department of Immunology, Genetics and Pathology, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mats Hellström 1 Department of Immunology, Genetics and Pathology, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bengt Glimelius 1 Department of Immunology, Genetics and Pathology, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karin Stålberg 2 Department of Women’s and Children’s Health, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tobias Sjöblom 1 Department of Immunology, Genetics and Pathology, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tobias Sjöblom For correspondence: tobias.sjoblom{at}igp.uu.se Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Sensitive and specific blood biomarkers for detection of cancer are highly warranted. To discover such biomarkers, we measured plasma levels of 165 proteins and 244 metabolites in 818 patients with colorectal, lung or ovarian carcinoma at diagnosis, 119 patients with non-malignant conditions of the corresponding organs, and 1,129 healthy individuals. We performed exhaustive search over all cut-off values of the ROC statistic and identified composite biomarkers with diagnostic performance significantly superior to benchmark FDA approved blood tests in clinical use for detection of cancer. We found biomarkers composed of 2-4 proteins separating cases of each tumor type from healthy controls with ROC AUC in the range 0.89 to 0.98. These biomarkers also separated cases of each tumor type from the other two (ROC AUC 0.82-0.88). For lung and ovarian cancers, we identified biomarkers distinguishing cases with intermediate and high from those with low tumor stages. These biomarkers for cancer detection and stage can find use in early detection, staging and differential diagnosis of common tumor types. Introduction Colorectal cancer (CRC), lung cancer (LuCa) and ovarian cancer (OvCa) are among the cancers with the highest global incidence, with LuCa and CRC placing 1st and 3 rd for all cancers, and OvCa being the 8 th most diagnosed cancer in women 1 . Lung cancer and CRC are also the two leading causes of cancer-related death, accounting for 18.7% and 9.3% of all deaths from cancer, respectively, and in females OvCa places 8 th with 4.8% of deaths 1 . In most common cancer types, stage at diagnosis is an important predictor of survival 2 , 3 , 4 . Diagnosing the tumor at an early stage is generally perceived to increase access to curative surgery for the primary tumor, but in e.g. the US, nearly one-quarter of CRC, half of LuCa and up to 70% of OvCa cases are currently diagnosed at a late stage with distant metastases present 3 . Screening programs may contribute to early detection and diagnosis at earlier stage 5 by pinpointing tumors before development of symptoms, and are shown to reduce the mortality in CRC 6 and LuCa 7 , 8 . In combination with ultrasound, CA125 as a single biomarker was recently found insufficient to support OvCa screening in an asymptomatic population as it did not significantly lower the mortality although more cancers were diagnosed at lower stages 9 . The symptoms associated with CRC, LuCa, OvCa and several other malignancies are often non-specific and partially overlapping at earlier stages, including fatigue, weight loss, abdominal pain, constipation or diarrhea. There is therefore a need for sensitive and specific blood tests that detect these tumor types at early stages and also discriminate them from each other. Circulating biomarkers offer potential for non-invasive diagnostic and screening tools in cancer. Blood testing is safe and associated with high patient adherence compared to other methods such as radiology, endoscopy or stool-based tests. Current large-scale molecular analysis techniques enable parallel measurement of thousands of analytes in small volumes of blood, including e.g. levels, variants and epigenetic or post-translational modifications of proteins, nucleic acids and metabolites. Recent advances in pan-cancer blood biomarker tests include combined detection of proteins with mutational analysis of ctDNA 10 and analysis of cfDNA methylation with/without fragmentation 11 , 12 , 13 , 14 . These efforts have led to ongoing prospective clinical trials that compare cancer incidence rates in conventional screening methods with blood-based screening tests, exemplified by the NHS-Galleri Trial 15 , PATHFINDER 2 ( NCT05155605 ), STRIVE ( NCT03085888 ), and SUMMIT ( NCT03934866 ) studies. While the specificity of methylation-based tests is high, sensitivity is lower particularly in early tumor stages for several common tumor types, potentially because of low levels of released ctDNA. To ensure efficient translation, biomarker discovery should ideally align with the efficacy requirements from regulatory authorities that govern their use as diagnostic tests, such as the US Food and Drug Administration (FDA). In practice, for a novel biomarker to gain regulatory approval as a medical device for clinical use, non-inferior ROC AUC to an existing approved test for the same purpose must be demonstrated. Diagnostic blood biomarkers of cancer currently in clinical use generally suffer from low sensitivity and/or specificity, which limits their utility. In CRC, blood tests could improve participation rates in screening programs that are currently based on fecal samples 16 , 17 , however the current best FDA approved blood test to discover CRC, Epi proColon (ROC AUC 0.82) 18 , 19 , does not reach the performance of either the fecal occult blood test (FIT) alone (ROC AUC 0.88) 20 , 21 , or the multi-target stool DNA test Cologuard (ROC AUC 0.93) 22 , 23 . Notably, across all other cancer types, no blood test with performance on par with Epi proColon for CRC is currently FDA approved for early detection. Taken together, there is a need for robust blood biomarker tests for cancer with diagnostic performance exceeding the state-of-the-art. Here, we analyze plasma proteome and metabolome data using a ROC-based biomarker discovery approach with benchmarking to regulatory approved early detection tests for cancers to find composite diagnostic plasma biomarkers that have suitable properties for further development. Results Plasma proteomic and metabolomic analyses of cases and controls To discover diagnostic biomarkers, we measured the levels of 165 proteins and 244 metabolite parameters in plasma samples drawn at diagnosis before any treatment from 330 CRC, 304 LuCa, and 184 OvCa (including 24 borderline tumor) patients with tumor stages I-IV from the prospective population-based cancer biobank U-CAN 24 ( Figure 1A , Supplementary Figure 1). We also analyzed blood plasma from 119 (50 for CRC, 16 for LuCa and 53 for OvCa) patients with non-malignant diagnosis from the same biobank, here termed Non-cancer controls, to enable selection of biomarkers that discriminate cancer from pre-malignant or non-malignant diseases of the same organs ( Figure 1B , Supplementary Figure 2). As healthy population controls, we analyzed plasma samples from 1,129 individuals of the EpiHealth cohort 25 that were in the same age span as the cancer cases and living in the same area ( Figure 1B , Supplementary Figure 1). To ensure a relevant age interval for cancer screening, the cases and controls were of age 45-75 in CRC and LuCa and 40-80 in OvCa. The age and sex distributions of cases and population controls were similar ( Table 1 , Figure 1B ). Comparing the study cohort to the incident population as recorded in the national quality registries for the respective cancers, we found that for CRC, the distributions of patient sex, tumor location, T and N stage was similar to the incident population (Supplementary Figure 3). In LuCa, the proportions of smokers, sex and histological subtype were similar, whereas stage I was overrepresented and stage IV underrepresented compared to the incident population (Supplementary Figure 4). For OvCa, the relative proportion of sub-diagnoses differed, and stage I tumors were underrepresented compared to the population, but there was a similar ratio of stages I-III to IV/X0 (Supplementary Figure 5). Together, the population-based study cohorts are representative of the incident cancer cases and the background population. Download figure Open in new tab Figure 1. Discovery of tumor-type specific and pan-cancer biomarkers in blood plasma. A. Plasma samples from patients with cancer or non-malignant conditions at diagnosis and healthy population controls were analyzed by affinity proteomics and NMR metabolomics. The ROC was computed for individual analytes and for composite biomarkers of 1-4 analytes within and across the study cohorts. The ROC AUC distributions for biomarkers with performance similar to a predicate device already in clinical use were used for statistical testing of putative biomarkers. B. Selection of cases and controls and characteristics of the selected study populations. From 3,821 cases being investigated for colorectal, lung and ovarian cancer, sampled for blood at time of diagnosis in the population-based prospective study U-CAN, 818 with confirmed cancer and 119 with pre-malignant tumors or other disease of the respective organs fulfilled the inclusion criteria. From 23,670 healthy individuals in the population-based cohort EpiHealth, 748, and 771 controls were selected for the CRC and LuCa, and the OvCa cohorts, respectively. View this table: View inline View popup Download powerpoint Table 1. Characteristics of cancer cases and controls. Protein and metabolite levels altered in cancer patients versus non-cancer controls Since the cancer cases and healthy population controls were from different biobank cohorts, we expected that there could be pre-analytical effects that could affect biomarker identification. Indeed, we observed that the vast majority of NMR analytes discriminated between non-cancer controls and healthy controls, whereas only a subset of proteins did. In an attempt to minimize the impact of pre-analytical variation, we first analyzed the cancer cases versus the non-cancer controls from the same cohort, before proceeding to comparing cancer cases to the population controls. First, we computed the ROC AUC for each measured protein as single biomarker in discriminating cancer cases from the 119 non-cancer controls drawn from the U-CAN cohort, and tested for statistical significance ( Figure 2A ). From the 165 proteins measured, 22 (CRC), 31 (LuCa), 12 (OvCa) and 14 (pan-cancer) were up- or downregulated in plasma from the cancer patients at diagnosis (p < 0.05, 2-sided Mann-Whitney U test under Bonferroni correction; Supplementary Tables 1-5). Several established biomarkers for each tumor type were corroborated, including (1) ferritin down-regulation in CRC (AUC 0.66) 26 , 27 , (2) carcinoembryonic antigen (CEA/CEACAM5) up-regulation with a ROC AUC of 0.68 and 0.69 for CRC and LuCa, respectively, congruent with AUC 0.67-0.80 for CRC 10 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 and 0.62-0.82 for LuCa 10 , 36 , 37 , (3) HMGB1 up-regulation in LuCa (AUC 0.76) 38 , (4) SPP1/OPN up-regulation in all three tumor types 10 , and (5) Mucin 16 (CA125) with ROC AUC 0.91 for OvCa, in the reported range 0.77-0.97 10 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ( Figure 2B ). Further, novel potential biomarkers were discovered, including PECAM1/CD31 up-regulation in CRC (AUC 0.69) and LuCa (AUC 0.68). Investigating the overlap between diagnoses, we found that seven significantly regulated proteins connected CRC and LuCa, 3 connected LuCa and OvCa, but none connected CRC and OvCa ( Figure 2C ). Download figure Open in new tab Figure 2. Pan-cancer and tumor-type-specific protein and metabolite biomarkers. For each cancer type alone and together (“pan-cancer”), ROC AUC values were computed for all 165 proteins and 244 metabolites comparing cancer cases against 119 non-cancer controls within the U-CAN cohort. A. ROC AUC values for proteins significantly up- or down-regulated for at least one cancer type. * denotes significant analytes at the 95% level (p < 0.05). B. ROC and Box plots for down-regulated Ferritin in CRC, up-regulated CEACAM5 (CEA) in CRC and LuCa, up-regulated HMBG1 in LuCa, up-regulated PECAM1 in both CRC and LuCa, up-regulated SPP1 (Osteopontin) in LuCa and OvCa, and up-regulated MUC16 (CA-125) in OvCa. All p values adjusted using two-sided Mann-Whitney U test under Bonferroni correction for multiple hypothesis testing. Confidence bands in ROC plots contain 95% of ROC curves from 1,000 bootstrap iterations. C. Graph visualization of proteins regulated at 95% significance. D. ROC AUC values for metabolites significantly up- or down-regulated for at least one cancer type. E. ROC and Box plots for pan-cancer markers up-regulated GlycA and down-regulated albumin. Next, we computed the ROC AUC for each NMR determined analyte in discriminating cancer cases from non-cancer controls from the same cohort and tested for statistical significance. From the 244 determined metabolites, 18 (CRC), 21 (LuCa), 21 (OvCa) and 20 (pan-cancer) were significantly up- or downregulated in cancers (p < 0.05, 2-sided Mann-Whitney U test under Bonferroni correction; Figure 2D , Supplementary Tables 1-5). Several of these were supported by previous studies. Reduced albumin 47 , increased levels of the systemic inflammation marker glycoprotein acetyls (GlycA) 48 , 49 along with reduced S-HDL-L and S-HDL-P were potential biomarkers of all three tumor types ( Figure 2E ). The CRC and OvCa groups had reduced lipoprotein particles and ApoA1 50 as well as reduced concentration and altered composition of HDL particles, along with reduced total concentrations of lipoprotein particles (Total-P), phospholipids (Total PL), and free cholesterol (Total FC). The LuCa cases had altered LDL composition with reduced cholesteryl esters and cholesterol fraction and increased phospholipids fraction. Composite biomarkers for separation of cancer patients at diagnosis from healthy population controls We next proceeded to identify composite biomarkers that could discriminate cancer cases from healthy population controls. To remove false positive biomarkers arising from pre-analytical differences between the cohorts, we included only the proteins and metabolites that by themselves could discriminate the cancer cases from the non-cancer controls ( Figure 2A and D ). We here required these individual analytes to also have significant difference versus the population controls. Finally, to further ensure that we filtered analytes with large pre-analytical or cohort effects, we retained only those where ROC AUC for cancer vs non-cancer controls was greater than ROC AUC for non-cancer controls vs healthy population controls. This resulted in 10 analytes (10 proteins, 0 metabolites) for CRC, 22 (21 proteins and 1 metabolite) for LuCa, and 13 (12 proteins and 1 metabolite) for OvCa identified as suitable for being part of composite biomarkers for the respective cancer type ( Table 2 , Supplementary Figure 6, Supplementary Tables 6-9). View this table: View inline View popup Download powerpoint Table 2. Protein and metabolite analytes with statistically significantly altered levels in blood plasma from cancer cases compared to controls. When forming composite biomarkers for each tumor type, we evaluated each analyte up- or downregulated and all analytes combined using logical AND or logical OR resulting in 2 k+1 classification scenarios where k denotes the number of analytes combined (Supplementary Figure 7). For each classification scenario we performed exhaustive search on all possible combinations of cutoff values to obtain the optimum ROC performance, i.e. finding the best true positive rate for each false positive rate. It can be argued that only biomarkers that supersede existing regulatory authority approved and clinically used diagnostic tests are meaningful to discover. We therefore evaluated all combinations of 2-4 up- or downregulated analytes, computed the ROC AUC for different classification scenarios and performed statistical hypothesis testing for superiority versus biomarkers with performance on par with regulatory approved diagnostic tests. For benchmarking purposes, we used the CRC tests Epi proColon/ColoHealth (AUC 0.82), FIT (AUC 0.88) and Cologuard (AUC 0.93) 51 throughout, arguing that performance of AUC 0.82 is a minimal performance target also for early detection of OvCa or LuCa. The FIT and Cologuard tests are feces-based, but they represent state-of-the-art tests for early detection of cancer with performance levels that new biomarkers will need to match or supersede for regulatory approval and adoption in clinical use. For each tumor type we computed all possible composite biomarkers combining 2, 3 and 4 analytes and checked for superiority against these three predicate device tests. For CRC, there were only 2 composite biomarkers of 4 analytes that were significantly superior to a test on par with Epi proColon, while for LuCa, there were 3 composite biomarkers of 2 analytes, 60 of 3 analytes, and 783 of 4 analytes that met the benchmark. For OvCa there were many composite biomarkers significantly better than a test with performance on par with Cologuard ( Table 3 , Supplementary Tables 10-17). For the best biomarkers per tumor type, a combination of upregulated CEACAM5 (CEA), FLT1 and IL19, and downregulated Ferritin separated CRC from healthy population controls (AUC 0.89), and a combination of downregulated FNDC5 and upregulated MDK, PLAUR and CEACAM5 separated LuCa from healthy population controls (AUC 0.91) ( Figure 3B ). Both these biomarkers were statistically significantly superior to a biomarker on par with the CRC blood test Epi proColon but not to a biomarker on par with FIT. A combination of upregulated MUC16 (CA125) and PLG separated OvCa from healthy population controls (AUC 0.97) with performance significantly superior to a test with performance on par with Cologuard. Excluding CA125, which performed exceptionally well here as compared to the literature, a combination of upregulated PLG, KLK6, MDK and CCL2 separated OvCa from healthy population controls with AUC 0.92, which was significantly superior to a biomarker with performance on par with Epi proColon. This performance is comparable to that of the OvCa test OVA1, albeit OVA1 is not approved for screening. The best biomarkers for each tumor type could also separate cancer cases from the non-cancer controls with AUC 0.81-0.92 and discriminate the target tumor type from the two other tumor types with AUC 0.82-0.88 ( Figure 3A ). Closer analysis of the best biomarkers per tumor type revealed that all the individual analytes contributed to the performance of the respective composite biomarker ( Figure 3B ). Further, the composite biomarkers discriminated later stage tumors from healthy controls better than they discriminated earlier stages from healthy controls ( Figure 3C ), which was also the case for the individual analytes included in the combinations (Supplementary Figure 8). All of the top-performing composite biomarkers were determined from plasma protein levels only ( Figure 3D ), none included metabolites. Replacing upregulated MDK protein with upregulated metabolite S-LDL-PL % in the four-combination for LuCa resulted in slightly worse but comparable ROC AUC (Supplemental Table S13) but overall the benefit of adding a fourth analyte to the three-protein core of PLAUR, FNDC5 and CEACAM5 was small. Similarly, the metabolite analyte GlycA was included in significant OvCa biomarkers but only as marginal improvements to a strong core based on protein combinations. Taken together, we discovered two-protein biomarkers for OvCa and four-protein biomarkers for CRC and LuCa that were superior to relevant benchmark FDA approved blood and/or stool tests for early diagnosis of cancer. Download figure Open in new tab Figure 3. Composite biomarkers distinguish patients with either CRC, LuCa or OvCa from healthy individuals and from patients with the two other forms of cancer. The ROC was computed for all combinations of 1-4 of the selected analytes for each cancer type. For each combination evaluated, we optimized the ROC curve by exhaustive search on all possible cutoff values combined either by logical OR (|) or logical AND (&). The resulting composite biomarker was evaluated for superiority with respect to Epi proColon (AUC 0.82), FIT (AUC 0.88) and Cologuard (AUC 0.93) at the 99% significance level using Bonferroni correction for multiple hypothesis testing. A. ROC plots with AUC values for the top composite biomarkers for colorectal cancer (CRC), lung cancer (LuCa), ovarian cancer (OvCa), and OvCa when CA125/MUC16 was excluded from computation of combinations. B. Sequential addition of analytes reveals their contribution to the ROC performance of the composite biomarkers. C. Stage-dependent performance of the composite biomarkers over stages I-IV. D. Boxplots of measured plasma concentrations for the analytes used in the composite biomarkers. View this table: View inline View popup Download powerpoint Table 3. Single analyte and composite plasma biomarkers for diagnosis of cancer compared to the ROC AUC of different benchmarks. Tumor-stage-dependent biomarkers In addition to early cancer detection, we also wanted to identify biomarkers that can discriminate cancer stage. We therefore evaluated correlations between protein and metabolite concentrations and stage for CRC, LuCa and OvCa respectively. We analyzed the 818 cancer cases and 119 non-cancer controls by Spearman rank correlation test, including all measured proteins and metabolites since the pre-analytical effects from the healthy control cohort comparisons are not an issue here. We found 4 (2 proteins, 2 metabolites) analytes for CRC, 111 (27 proteins, 84 metabolites) analytes for LuCa and 23 (12 proteins, 11 metabolites) analytes for OvCa that significantly correlated with stage (95% significance level, Bonferroni correction), with the MDK protein, and the two metabolites XS-VLDL-PL % and L-LDL-C % overlapping for all the three tumor types ( Figure 4A-B , Supplementary Table 18). Next, we searched for single analytes that could discriminate stages III-IV from stages I-II tumors with statistical significance by computing ROC AUC, and found 0 analytes for CRC, 110 analytes (27 proteins, 83 metabolites) for LuCa and 8 analytes (4 proteins, 4 metabolites) for OvCa (2-sided Mann-Whitney U test under Bonferroni correction; Figure 4C-D ; Supplementary Table 19). In the same way, we also searched for single analytes that could distinguish stage IV from stages I-III and found 22 (1 protein, 21 metabolites) for CRC, 78 (17 proteins, 61 metabolites) for LuCa, and 8 (4 proteins, 4 metabolites) for OvCa. From the single analytes that could separate stage IV cancers from lower stages, we computed all possible 4-combination composite biomarkers and the top combinations included upregulated CEACAM5, XL-VLDL-PL %, GlycA and downregulated L-LDL-CE % for CRC (ROC AUC 0.87), upregulated MUC16, DKK, KRT19 and downregulated Albumin for LuCa (ROC AUC 0.92), and upregulated MUC16, XXL-VLDL-PL %, CHI3L1 and downregulated Albumin for OvCa (ROC AUC 0.88) ( Figure 4E , Supplementary Tables 20-22). Downregulated albumin in LuCa and OvCa is consistent with the hypoalbuminemia commonly observed in wide-spread carcinosis. Notably, the discrimination of stage IV CRC from stage I-III CRC was much better compared to the discrimination of stages III-IV from stage I-II. This is in contrast to LuCa and OvCa, where there was similar performance for stage IV vs I-III and stage III-IV vs I-II ( Figure 4D ). This is however consistent with our earlier observation that no single analytes were statistically significant for separation of CRC stage III+IV from stage I+II. Taken together, blood-based resolution of tumor stage based on the proteins and metabolites measured was more effective for LuCa and OvCa than for CRC, and metabolites contributed to the performance of several of the best composite biomarkers. Download figure Open in new tab Figure 4. Tumor-stage-dependent proteomic and metabolomic signatures. A. Clustered heatmap of Spearman rank correlations between analytes and stages for CRC, LuCa and OvCa, respectively. Lower heatmap displays for which type of cancer the analytes had significant correlation with stage. Significant correlations with stage were observed for 4 proteins in CRC, 111 in LuCa and 23 in OvCa (adjusted p < 0.05). B. Boxplots of analyte concentrations for different tumor types and stages. C. Identification of analytes of all 409 (165 proteins and 244 metabolites) measured that could separate stage IV from stages I-III using the Mann-Whitney U test as well as analytes that could separate stages III-IV from stages I-II. D. ROC AUC values for separating stage IV from stages I-III, plotted against ROC AUC values for separating stage III-IV from stages I-II for all 409 measured single analytes. E. The ROC for the top combinations of composite biomarkers separating stages III-IV from stages I-II as well as separating stage IV from stages I-III. For each combination of selected analytes evaluated, we optimized the ROC curve by exhaustive search on all possible cutoff values combined either by logical OR (|) or logical AND (&). External validation of proteomic biomarkers Finally, we performed external validation of individual proteins and selected composite biomarkers for detection of cancer vs healthy controls ( Figure 5 ). To this end, we compared the data from stage I-III cancers and healthy controls in this study to data from the CancerSeek study 10 where Luminex technology was used to measure one-quarter of the proteins analyzed here ( Figure 5A ). We found several single-protein biomarkers, including MUC-16/CA125 and GDF15, that had highly similar ROC AUC values between our study and the CancerSeek study, whereas some were slightly superior in CancerSeek (SPP1/OPN) or in this study (PECAM-1), respectively ( Figure 5B , Supplementary Table 23). In all, 32 proteins were measured in both studies and after filtering analytes with technical issues we selected proteins with consistent behavior for each diagnosis. When 4-protein composite biomarkers were generated using the 8 retained proteins for CRC, 7 retained proteins for LuCa and 13 retained proteins for OvCa -, similar ROC performance was achieved in both datasets ( Figure 5C , Supplementary Tables 24-26). In conclusion, we observed a high degree of similarity for biomarkers that we sought to validate in an independent data set, indicating that composite protein biomarkers can form the bases of robust and reproducible diagnostic tests for cancer detection. Download figure Open in new tab Figure 5. Validation of proteomic biomarkers for cancer detection in an external dataset. For a subset of proteins, external validation could be performed in CancerSEEK data. A. Cohort sizes and overlap in measured proteins in this study and in the CancerSEEK study. In total, 32 proteins were measured in both studies and 3 of these were excluded due to technical issues. Next, we enriched for proteins with consistent behavior, selecting for each tumor type those with ROC AUC > 0.5 for cancer stage I-III vs healthy controls in both studies. This resulted in 8 CRC, 7 LuCa and 13 OvCa proteins considered informative in both studies. B. The ROC AUC in this study (discovery) plotted against the ROC AUC in CancerSEEK study (validation) for all proteins regarded informative after filtering. C. The best 4-combination biomarkers of informative proteins found in this study for CRC, LuCa and OvCa, respectively, and the corresponding performance in CancerSEEK. For each biomarker, the ROC curves and AUC of the individual proteins and the composite biomarker are shown. Discussion Here, we analyzed a large dataset and identified combinations of 2-4 analytes/proteins that performed better than an FDA approved early cancer detection blood test. Based on FDA pre-approval decisions on biomarkers since 1981, the Epi proColon ROC performance of 0.82 can be considered as a bar to surpass for a new blood-based biomarker test for early detection of cancer to have potential for regulatory approval and wider clinical adoption. We here succeeded in identifying composite plasma all-protein biomarkers that exceed the selected benchmark for all three tumor types. These combinations merit further development and evaluation in cohorts collected in a healthcare triage setting as well as in a screening setting. In both CRC and LuCa we found biomarkers that were significantly superior to the performance of detection of CRC by Epi proColon. In OvCa, we identified biomarkers with ROC performance superior to the at the time of analyses most sensitive FDA approved early detection test for cancer currently available, which is the feces test Cologuard with ROC AUC 0.93 for detection of CRC. For OvCa, the OVA-1 test is an FDA-approved blood serum test for assessing risk for ovarian malignancy in women that have been previously diagnosed with an ovarian mass for which they are scheduled for surgery. However, OVA1 is not a test for early cancer detection in women that do not have an already detected ovarian mass, and is therefore not a suitable benchmark for this study. During 2024, past the analyses of this study, two new early detection tests for CRC obtained FDA premarket approval, the cfDNA blood-based Shield test, and the multitarget stool DNA test Cologuard Plus. Both these tests show higher sensitivity than the corresponding benchmark tests that we used here. In clinical evaluation, the Shield test had 83.1% sensitivity 52 , and Cologuard Plus 93.9% sensitivity 53 for detection of CRC. Although the blood-based Shield test outperforms the previous state-of-art, the Epi proColon test, detection of early stage cancer and precancerous lesions remains a challenge, with sensitivity of the Shield test for clinical stage I cancer being 55% and for advanced precancerous lesions 13% 52 . To the best of our knowledge, this is the first study integrating affinity proteomics and metabolomics to develop composite biomarkers. The cores of the composite cancer detection biomarkers identified here contained well-established single protein biomarkers such as MUC16/CA125 or CEACAM5/CEA, effectively supporting their relevance. In CRC, while FLT1 has previously been found elevated 54 , IL-19 has not. In LuCa, the PLAUR/suPAR is previously reported elevated in non-small cell LuCa (NSCLC) but not in small cell LuCa (SCLC) 55 , whereas elevated FNDC5/irisin has not previously been linked to LuCa. Consistent with our data, Midkine (MDK) has previously been found elevated in LuCa 56 and OvCa 57 . An interesting observation is that, as compared to the literature, MUC16/CA125 performed extremely well for detection of OvCa in our data, and also in the external Luminex-based validation dataset from the CancerSeek study. While CA125 is in use in OvCa monitoring and prognostics, including as part of the OVA1 test 58 , 59 , it has some issues that limit its clinical value. One example is that it was previously found not to be elevated in ∼20% of women with epithelial OvCa 60 . One potential explanation is that the antibodies to CA125 used in the Luminex assays perform better than those used in other affinity proteomics assays, which could potentially be utilized to improve current CA125-containing biomarkers. Interestingly, plasminogen (PLG) elevation, which has not previously been reported in OvCa, was part of strong biomarkers in combinations both with and without CA125. In contrast, both of the two other elevated plasma proteins in the best OvCa biomarker without Ca125, KLK6 and CCL2, have prior association with OvCa 61 , 62 . For NMR metabolomics, this study encompasses twice the number of CRC cases 63 , 64 , twice the number of LuCa cases 65 and twice the number of OvCa cases 66 than prior art. Several NMR analytes of interest here, including albumin and glycoprotein acetyls (GlycA), have regulatory approval for clinical use, which could enable implementation of composite biomarkers incorporating metabolites. However, the cohort differences impacted variability for metabolites more than was the case for proteins in this study, potentially because of differences in fasting status before blood draw 67 between the two cohorts. While we did find metabolites potentially useful for stage determination here when we analyzed only the U-CAN cohort, the observation that only two metabolites passed filtering for pre-analytical differences between the two cohorts when U-CAN cases were compared to the healthy controls of the EpiHealth cohort suggests that NMR-based tests will require highly standardized sampling and handling for robustness of clinical implementations. Taken together, the best performing composite biomarkers for each tumor type combine known and novel biomarkers, supporting their relevance. Another potential use of cancer biomarkers is to separate early from late stage tumors. Interestingly, a larger number of metabolites compared to proteins had statistically significant differences between late stages and early stages among the cancer cases. Some analytes, such as Albumin and GlycA, are well-known general disease markers and are likely not tumor specific. However, in combination with more tumor-specific proteins this could still be useful in a composite biomarker. For LuCa and OvCa, many analytes showed consistent increase or decrease with increasing stage (correlation analysis and box plots in Figure 4 ), whereas for CRC most analytes displayed a large difference between stage IV and stages I-III. For LuCa and OvCa, composite biomarkers for stage separation could discriminate stages III-IV from stages I-II with similar ROC AUC as discriminating stage IV from stages I-III, which was not the case for CRC, suggesting that stage IV CRCs but not stages I-III show strong circulating proteomic and metabolomic signatures ( Figure 3C ). This is consistent with the generally lower performance for CRC biomarkers compared to LuCa and OvCa discovered in this study. Overall, metabolites proved more useful for tumor stage discrimination than early detection. The FDA approval process for a new biomarker is centered on comparing its sensitivity and specificity to that of biomarkers already approved by the regulator for the same purpose. Here, we used large numbers of samples and a novel computational approach 51 to evaluate combinations of analytes, for each combination identifying the optimal direction of change for each analyte that produced the best ROC AUC performance. While partitioning of data for training, test and validation is current convention, we reasoned that external validation by analysis of independently generated data is more meaningful given that study-specific biases caused by pre-analytical sample handling and cohort composition are not corrected for by partitioning. After removing analytes with inflated ROC AUC likely due to pre-analytical differences between study cohorts, we used hypothesis testing under Bonferroni correction to determine if the combinations superseded clinical benchmarks tests in diagnostic performance. While a potential drawback of the filtering approach applied is the loss of biomarkers that also recognize non-malignant pre-cancerous lesions, we reasoned that it would be more useful to unambiguously identify biomarkers of malignant disease. While the approach used here identifies the combinations of analytes giving the best ROC performance, the optimal cut-off values need to be determined through analysis of additional data. To provide true external validation, we will next analyze the identified composite biomarkers in samples collected before diagnosis at suspicion of cancer as well as in a population screening setting, which represents the main use cases for diagnostic blood biomarkers. A potential use of biomarkers with high tumor-type specificity, as the ones identified here, is to be able to discriminate abdominal tumor types when patients present with diffuse symptoms that are not disease-specific. For example, patients with disseminated OvCa often present with similar symptoms and radiological findings as CRC patients. The discrimination of OvCa from CRC exemplifies such clinical needs, where a reliable blood test could be a supplement or even alternative for biopsy if validated clinically. Since infrastructure and workflows are straightforward, cost effective and well established in clinical laboratories, protein-based diagnostic biomarkers for cancer may be preferred as compared to analyses of circulating DNA or RNA. However, the requirements for sensitivity and tumor type specificity are rarely fulfilled by single-analyte biomarkers, but demands a combination of several proteins, metabolites or nucleic acid targets 68 . For reliability and cost reasons, a diagnostic test based on such combinations should incorporate as few analytes as possible. We therefore capped combination sizes at 4 analytes in this study. Although this is a low number considering other prominent proteomics-containing cancer signatures 10 , we identified hundreds of potential tumor-type-specific biomarkers that perform at least equally well as the prior art. While there are other affinity proteomics technologies that enable measurement of thousands of proteins, and can thus provide much larger datasets for de novo biomarker discovery, the one used here is implemented on an FDA approved instrument platform, which can greatly facilitate clinical translation. Analysis of differences in diagnostic performance within and between study cohorts allowed selection of a limited set of biomarkers. In this study these biomarkers could either be shown to behave similarly versus non-malignant controls from both study cohorts, or be validated in an external cohort. However, as we measured 4-fold more proteins than the external dataset from the CancerSeek study, only a subset of combinations could be externally validated at this point. In comparison to CancerSEEK, several of the proteins we could compare had remarkably similar ROC AUC for cancer versus population controls, while others were slightly different. These differences can indicate sensitivity to pre-analytical variation, but could also be caused by e.g. the different age distributions of cases and controls in CancerSEEK. Methods Ethical approval The study was approved by the Swedish Ethical Review Authority (EPM dnr 2019-00222) with amendments. The research was in line with informed consents obtained from patients included in U-CAN 24 (EPN Uppsala 2010-198 with amendments) and research participants in EpiHealth 25 . Cases and controls Study size was determined according to (Ekström et al, manuscript in preparation). Cancer cases were selected from the full population of individuals included 2010-2020 in the longitudinal prospective cohort U-CAN 24 at Uppsala University Hospital. Inclusion criteria for this study for cancer cases included sample freezing time ≤4 h after blood draw, registration in the national quality registry for the respective cancer diagnosis (the Swedish Colorectal Cancer Registry, the Swedish National Quality Registry for Lung Cancer, and the Swedish Quality register for Gynecologic Cancer), age at diagnosis range 45-75 (CRC and LuCa) or 40-80 (OvCa), no non-surgical or surgical cancer treatment before blood sampling, sampling within 90 d of the diagnosis date stated in the respective national quality registry, no known concurrent or prior malignancy reported in the Swedish Cancer Registry, and availability of sufficient plasma samples. Epithelial borderline ovarian tumors (n=24) were included among the OvCa cases. As technical controls for pre-analytical handling, we used plasma samples from patients in U-CAN with non-malignant or pre-malignant conditions in the respective organ sampled under identical handling conditions as the cancer cases. The most common benign conditions were colon polyps, adenomas, prolapse and incontinence; gynecological cyctoadenomas of various histology, endometriosis, endometrial hyperplasia and polyps, myomas and cervical intraepithelial neoplasia (CIN); chronical inflammatory conditions of the lung. The EDTA plasma samples from U-CAN participants had been drawn non-fasting at diagnosis before administration of any anti-cancer treatment, and subjected to central standardized handling at RT followed by freezing for biobanking at -80 °C. Population controls were selected from individuals included in the prospective study EpiHealth 25 in 2010-2015, that had not been registered in the Swedish Cancer Registry as having any type of cancer up to 5 years after sampling. The EpiHealth individuals had been sampled for blood at a test center at Uppsala University following 6 h fasting, with plasma centrifuged and stored for up to 6 h at +4 °C followed by freezing for biobanking at -80 °C. Measurements of proteins and metabolites Plasma levels of 165 different proteins (Supplementary Table 27) were measured using a suspension array ELISA system (BioPlex 200, Bio-Rad) and 10 antibody panels (Human Circulating Cancer Biomarker Panel 1, Human Circulating Cancer Biomarker Panel 3, Human Circulating Cancer Biomarker Panel 4, Human Cancer metastasis Biomarkers panel, Human Immuno-Oncology Checkpoint Protein Panel 1, Angiogenesis Panel 2, Human Cytokine/Chemokine Panel, Human Cytokine/Chemokine Panel II, Human Cytokine/Chemokine Panel IV, Human Myokine Panel) according to the manufacturer’s instructions. Using xPONENT® software (ThermoFisher), data was collected from 50 beads per bead set for all 10 kits. The OvCa marker WFDC2 (HE4) was measured but excluded for technical reasons (assay failure) after consulting the manufacturer. The plasma levels of 244 metabolite parameters (Supplementary Table 28) were determined by NMR using the Blood analysis service by Nightingale Health, Helsinki, Finland. Data analysis For all analytes we computed ROC AUC for distinguishing cancer from non-cancer controls and cancer from healthy controls. Based on extensive study design simulations, all data was used for validation. All data points were included in ROC computations and statistical analyses. In boxplot visualizations, the whiskers extend to 1.5 times the interquartile range, and outliers are not shown due to distortion of scales. Statistical significance at the 95% level was tested using two-sided Mann-Whitney U test and Bonferroni correction for multiple hypothesis testing. The number of hypotheses tested and corrected for was 409*3 = 1227. Analytes that were significant both versus non-cancer controls and healthy controls were included in computation of combination biomarkers. Data analysis and visualization were done in Python and corresponding data science packages ( Python 3.9.18, numpy 1.21.4, pandas 1.3.4, matplotlib 3.5.0, seaborn 0.13.0, plotly 5.6.0, scikit-learn 1.2.1, networkx 3.1 ). Plots were generated using Python and figure panels composed with Inkscape and biorender.com. Composite biomarkers for cancer detection We used a novel algorithm to compute the ROC curves for combinations of up to 4 analytes while optimizing the cut-off values for each analyte 51 . For a combination of k analytes we computed 2 k+1 classification scenarios: each analyte can be up- or downregulated and combined using logical AND or logical OR. For each classification scenario exhaustive search was used to check all possible cutoff values and optimize the ROC curve, i.e. finding highest possible true positive rate for every false positive rate yielding the highest possible AUC. We designed the study according to results from prior simulations in this statistical framework that revealed that the most efficient use of cases and controls given the number of hypotheses tested was to assign all to a validation step (J. Ekström et al, Manuscript in preparation). We computed the ROC AUC for separating cancer cases of stages I-IV from the healthy controls of the population cohort given the over/under rules identified in the first step but allowing for optimization of new cut-off values. We then performed statistical hypothesis testing of whether the biomarker separated cases from controls better than a benchmark biomarker with the same properties as the currently best FDA approved diagnostic blood test for cancer, Epi proColon (ROC AUC 0.82), and the two best feces-based tests Fecal Immunochemical Test FIT (ROC AUC 0.88) or Cologuard (ROC AUC 0.93) at the 99% significance level given the same over/under rules that were generated in the comparison of cancers to non-malignant disease controls. This process was repeated for each tumor type. We used Bonferroni correction to adjust p-values and corrected for all computed combinations of respective order. Thus, we evaluated in total 708 2-combinations, 3,892 3-combinations and 16,480 4-combinations. Biomarkers for stage separation The correlation analysis was performed using two-sided Spearman rank correlation test and Bonferroni correction for multiple hypothesis testing (409 hypotheses tested) to find analytes that were statistically significantly correlated with stage. Two-sided Mann-Whitney U test and Bonferroni correction was used to find individual analytes that could discriminate later stages from earlier stages: stage IV vs stages I-III and stages III-IV vs stages I-II. From the significant analytes we computed composite biomarkers in the same way as described above. External validation of protein biomarkers For external validation of protein biomarkers, we computed ROC AUC for combinations using the same rule set as above and data from the CancerSeek study, which contains 388 CRC, 104 lung, and 54 OvCa of stages I-III along with 812 healthy controls sampled in the same pre-analytical handling 10 . For the proteins measured in both studies we used the same methodology and performed the same analysis as described above. First, we identified analytes that were statistically significant, computed all possible 4-combination composite biomarkers and compared performance in the two datasets. When computing the composite biomarkers, the cutoffs were optimized in the two datasets separately. This means that the same behavior (up/down-regulation) and combination of proteins were compared but not absolute cutoff values. This is to circumvent baseline differences due to differences in experimental protocol and sample handling. Data availability The datasets generated in the study for cases and non-cancer controls from the U-CAN cohort are available in the SciLifeLab Data Repository, with DOI: 10.17044/scilifelab.28351367. Datasets generated for EpiHealth healthy controls are not publicly available due to limitations with the informed consent, however, are available from the corresponding author on reasonable request. Code availability All code used to generate the figures can be obtained with a reasonable request to the corresponding author. Data Availability All data produced in the present study are available upon reasonable request to the authors. https://www.science.org/doi/suppl/10.1126/science.aar3247/suppl_file/aar3247_cohen_sm_tables-s1-s11.xlsx Competing interests The authors declare no competing interests. Acknowledgments The study was supported by grants to T.S. from the Swedish Cancer Society (CAN 2018/772, 21 1719 Pj, 2024 3831 Pj), the Sjöberg Foundation and Lena Wäppling’s foundation, and from the Swedish Cancer Society to J.Å.Ö. (CAN 21-0430-PT) and B.G. (190382PJ01H). We thank U-CAN for access to blood samples collected through Uppsala Biobank, Uppsala University Hospital and Uppsala University with support from the Swedish Government (SRA grant CancerUU), and EpiHealth for blood samples collected with support from the Swedish Government through the Swedish Research Council. References 1. ↵ Bray F , et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries . CA Cancer J Clin 74 , 229 – 263 ( 2024 ). OpenUrl CrossRef PubMed 2. ↵ Hawkes N . Cancer survival data emphasise importance of early diagnosis . BMJ 364 , l408 ( 2019 ). OpenUrl FREE Full Text 3. ↵ Siegel RL , Giaquinto AN , Jemal A . Cancer statistics, 2024 . CA Cancer J Clin 74 , 12 – 49 ( 2024 ). OpenUrl CrossRef PubMed 4. ↵ Siegel RL , Wagle NS , Cercek A , Smith RA , Jemal A . Colorectal cancer statistics, 2023 . CA Cancer J Clin 73 , 233 – 254 ( 2023 ). OpenUrl CrossRef PubMed 5. ↵ Vicentini M , et al. Impact of screening programme using the faecal immunochemical test on stage of colorectal cancer: Results from the IMPATTO study . Int J Cancer 145 , 110 – 121 ( 2019 ). OpenUrl PubMed 6. ↵ Jodal HC , Helsingen LM , Anderson JC , Lytvyn L , Vandvik PO , Emilsson L . Colorectal cancer screening with faecal testing, sigmoidoscopy or colonoscopy: a systematic review and network meta-analysis . BMJ Open 9 , e032773 ( 2019 ). OpenUrl Abstract / FREE Full Text 7. ↵ de Koning HJ , et al. Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial . N Engl J Med 382 , 503 – 513 ( 2020 ). OpenUrl CrossRef PubMed 8. ↵ National Lung Screening Trial Research T , et al. Reduced lung-cancer mortality with low-dose computed tomographic screening . N Engl J Med 365 , 395 – 409 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 9. ↵ Menon U , et al. Ovarian cancer population screening and mortality after long-term follow-up in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial . Lancet 397 , 2182 – 2193 ( 2021 ). OpenUrl CrossRef PubMed 10. ↵ Cohen JD , et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test . Science 359 , 926 – 930 ( 2018 ). OpenUrl Abstract / FREE Full Text 11. ↵ Chen X , et al. Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA . Clin Cancer Res 27 , 4221 – 4229 ( 2021 ). OpenUrl Abstract / FREE Full Text 12. ↵ Chen X , et al. Non-invasive early detection of cancer four years before conventional diagnosis using a blood test . Nat Commun 11 , 3475 ( 2020 ). OpenUrl CrossRef PubMed 13. ↵ Klein EA , et al. Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set . Ann Oncol 32 , 1167 – 1177 ( 2021 ). OpenUrl CrossRef PubMed 14. ↵ Liu MC , Oxnard GR , Klein EA , Swanton C , Seiden MV , Consortium C . Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA . Ann Oncol 31 , 745 – 759 ( 2020 ). OpenUrl CrossRef PubMed 15. ↵ Neal RD , et al. Cell-Free DNA-Based Multi-Cancer Early Detection Test in an Asymptomatic Screening Population (NHS-Galleri): Design of a Pragmatic, Prospective Randomised Controlled Trial . Cancers (Basel) 14 , ( 2022 ). 16. ↵ Schneider JL , Johnson CA , Jenkins C , Mummadi R , Coronado GD . “I was screaming hallelujah”: Patient and provider perceptions of blood-based testing for colorectal cancer screening . PLoS One 18 , e0295685 ( 2023 ). OpenUrl PubMed 17. ↵ Symonds EL , Cock C , Meng R , Cole SR , Fraser RJL , Young GP . Uptake of a colorectal cancer screening blood test in people with elevated risk for cancer who cannot or will not complete a faecal occult blood test . Eur J Cancer Prev 27 , 425 – 432 ( 2018 ). OpenUrl PubMed 18. ↵ Hariharan R , Jenkins M . Utility of the methylated SEPT9 test for the early detection of colorectal cancer: a systematic review and meta-analysis of diagnostic test accuracy . BMJ Open Gastroenterol 7 , e000355 ( 2020 ). OpenUrl Abstract / FREE Full Text 19. ↵ Song L , Jia J , Peng X , Xiao W , Li Y . The performance of the SEPT9 gene methylation assay and a comparison with other CRC screening tests: A meta-analysis . Sci Rep 7 , 3032 ( 2017 ). OpenUrl CrossRef PubMed 20. ↵ Imperiale TF , Gruber RN , Stump TE , Emmett TW , Monahan PO . Performance Characteristics of Fecal Immunochemical Tests for Colorectal Cancer and Advanced Adenomatous Polyps: A Systematic Review and Meta-analysis . Ann Intern Med 170 , 319 – 329 ( 2019 ). OpenUrl CrossRef PubMed 21. ↵ Selby K , et al. Effect of Sex, Age, and Positivity Threshold on Fecal Immunochemical Test Accuracy: A Systematic Review and Meta-analysis . Gastroenterology 157 , 1494 – 1505 ( 2019 ). OpenUrl PubMed 22. ↵ Bosch LJW , et al. Multitarget Stool DNA Test Performance in an Average-Risk Colorectal Cancer Screening Population . Am J Gastroenterol 114 , 1909 – 1918 ( 2019 ). OpenUrl CrossRef PubMed 23. ↵ Imperiale TF , et al. Multitarget stool DNA testing for colorectal-cancer screening . N Engl J Med 370 , 1287 – 1297 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 24. ↵ Glimelius B , et al. U-CAN: a prospective longitudinal collection of biomaterials and clinical information from adult cancer patients in Sweden . Acta Oncol 57 , 187 – 194 ( 2018 ). OpenUrl PubMed 25. ↵ Lind L , et al. EpiHealth: a large population-based cohort study for investigation of gene-lifestyle interactions in the pathogenesis of common diseases . Eur J Epidemiol 28 , 189 – 197 ( 2013 ). OpenUrl PubMed 26. ↵ Kishida T , et al. Clinical significance of serum iron and ferritin in patients with colorectal cancer . J Gastroenterol 29 , 19 – 23 ( 1994 ). OpenUrl CrossRef PubMed 27. ↵ Wild N , et al. A combination of serum markers for the early detection of colorectal cancer . Clin Cancer Res 16 , 6111 – 6121 ( 2010 ). OpenUrl Abstract / FREE Full Text 28. ↵ Bunger S , et al. A novel multiplex-protein array for serum diagnostics of colon cancer: a case-control study . BMC Cancer 12 , 393 ( 2012 ). OpenUrl CrossRef PubMed 29. ↵ Christensen IJ , et al. Plasma TIMP-1 and CEA as Markers for Detection of Primary Colorectal Cancer: A Prospective Validation Study Including Symptomatic and Non-symptomatic Individuals . Anticancer Res 35 , 4935 – 4941 ( 2015 ). OpenUrl Abstract / FREE Full Text 30. ↵ Croner LJ , et al. Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects . Clin Proteomics 14 , 28 ( 2017 ). OpenUrl CrossRef PubMed 31. ↵ Li Q , Wang K , Su C , Fang J . Serum Trefoil Factor 3 as a Protein Biomarker for the Diagnosis of Colorectal Cancer . Technol Cancer Res Treat 16 , 440 – 445 ( 2017 ). OpenUrl PubMed 32. ↵ Luo H , Shen K , Li B , Li R , Wang Z , Xie Z . Clinical significance and diagnostic value of serum NSE, CEA, CA19-9, CA125 and CA242 levels in colorectal cancer . Oncol Lett 20 , 742 – 750 ( 2020 ). OpenUrl CrossRef PubMed 33. ↵ Rasmussen L , Nielsen HJ , Christensen IJ . Early Detection and Recurrence of Colorectal Adenomas by Combination of Eight Cancer-Associated Biomarkers in Plasma . Clin Exp Gastroenterol 13 , 273 – 284 ( 2020 ). OpenUrl PubMed 34. ↵ Thorsen SB , et al. Detection of serological biomarkers by proximity extension assay for detection of colorectal neoplasias in symptomatic individuals . J Transl Med 11 , 253 ( 2013 ). OpenUrl CrossRef PubMed 35. ↵ Fei W , et al. RBP4 and THBS2 are serum biomarkers for diagnosis of colorectal cancer . Oncotarget 8 , 92254 – 92264 ( 2017 ). OpenUrl CrossRef PubMed 36. ↵ Sun J , Chen X , Wang Y . Comparison of the diagnostic value of CEA combined with OPN or DKK1 in non-small cell lung cancer . Oncol Lett 20 , 3046 – 3052 ( 2020 ). OpenUrl PubMed 37. ↵ Wu H , Wang Q , Liu Q , Zhang Q , Huang Q , Yu Z . The Serum Tumor Markers in Combination for Clinical Diagnosis of Lung Cancer . Clin Lab 66 , ( 2020 ). 38. ↵ Shang GH , et al. Serum high mobility group box protein 1 as a clinical marker for non-small cell lung cancer . Respir Med 103 , 1949 – 1953 ( 2009 ). OpenUrl CrossRef PubMed 39. ↵ Chen F , Shen J , Wang J , Cai P , Huang Y . Clinical analysis of four serum tumor markers in 458 patients with ovarian tumors: diagnostic value of the combined use of HE4, CA125, CA19-9, and CEA in ovarian tumors . Cancer Manag Res 10 , 1313 – 1318 ( 2018 ). OpenUrl PubMed 40. ↵ Funston G , Hamilton W , Abel G , Crosbie EJ , Rous B , Walter FM . The diagnostic performance of CA125 for the detection of ovarian and non-ovarian cancer in primary care: A population-based cohort study . PLoS Med 17 , e1003295 ( 2020 ). OpenUrl PubMed 41. ↵ Han C , et al. A novel multiple biomarker panel for the early detection of high-grade serous ovarian carcinoma . Gynecol Oncol 149 , 585 – 591 ( 2018 ). OpenUrl PubMed 42. ↵ Lertkhachonsuk AA , Buranawongtrakoon S , Lekskul N , Rermluk N , Wee-Stekly WW , Charakorn C . Serum CA19-9, CA-125 and CEA as tumor markers for mucinous ovarian tumors . J Obstet Gynaecol Res 46 , 2287 – 2291 ( 2020 ). OpenUrl CrossRef PubMed 43. ↵ Li Y , et al. The clinical value of the combined detection of sEGFR, CA125 and HE4 for epithelial ovarian cancer diagnosis . Eur Rev Med Pharmacol Sci 24 , 604 – 610 ( 2020 ). OpenUrl PubMed 44. ↵ Song HJ , Yang ES , Kim JD , Park CY , Kyung MS , Kim YS . Best serum biomarker combination for ovarian cancer classification . Biomed Eng Online 17 , 152 ( 2018 ). OpenUrl PubMed 45. ↵ Yip P , et al. Comprehensive serum profiling for the discovery of epithelial ovarian cancer biomarkers . PLoS One 6 , e29533 ( 2011 ). OpenUrl CrossRef PubMed 46. ↵ Zivny JH , Leahomschi S , Klener P , Jr. , Zivny J , Haluzik M , Cibula D . Comparison of Plasma Osteopontin Levels between Patients with Borderline Ovarian Tumours and Serous Ovarian Carcinoma . Folia Biol (Praha ) 62 , 258 – 262 ( 2016 ). OpenUrl PubMed 47. ↵ He MM , et al. Circulating liver function markers and colorectal cancer risk: A prospective cohort study in the UK Biobank . Int J Cancer 148 , 1867 – 1878 ( 2021 ). OpenUrl PubMed 48. ↵ Chandler PD , et al. Association of N-Linked Glycoprotein Acetyls and Colorectal Cancer Incidence and Mortality . PLoS One 11 , e0165615 ( 2016 ). OpenUrl PubMed 49. ↵ Deja S , et al. Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease . J Pharm Biomed Anal 100 , 369 – 380 ( 2014 ). OpenUrl CrossRef PubMed 50. ↵ Zhang Z , et al. Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer . Cancer Res 64 , 5882 – 5890 ( 2004 ). OpenUrl Abstract / FREE Full Text 51. ↵ Ekstrom J , Akerren Ogren J , Sjoblom T . Exact Probability Distribution for the ROC Area under Curve . Cancers (Basel ) 15 , 1788 ( 2023 ). OpenUrl PubMed 52. ↵ Chung DC , et al. A Cell-free DNA Blood-Based Test for Colorectal Cancer Screening . N Engl J Med 390 , 973 – 983 ( 2024 ). OpenUrl CrossRef PubMed 53. ↵ Imperiale TF , et al. Next-Generation Multitarget Stool DNA Test for Colorectal Cancer Screening . N Engl J Med 390 , 984 – 993 ( 2024 ). OpenUrl CrossRef PubMed 54. ↵ Kumar H , Heer K , Greenman J , Kerin MJ , Monson JR . Soluble FLT-1 is detectable in the sera of colorectal and breast cancer patients . Anticancer Res 22 , 1877 – 1880 ( 2002 ). OpenUrl PubMed Web of Science 55. ↵ Pappot H , et al. Elevated plasma levels of urokinase plasminogen activator receptor in non-small cell lung cancer patients . Eur J Cancer 33 , 867 – 872 ( 1997 ). OpenUrl CrossRef PubMed Web of Science 56. ↵ Li J , et al. Secreted proteins MDK, WFDC2, and CXCL14 as candidate biomarkers for early diagnosis of lung adenocarcinoma . BMC Cancer 23 , 110 ( 2023 ). OpenUrl PubMed 57. ↵ Rice GE , Edgell TA , Autelitano DJ . Evaluation of midkine and anterior gradient 2 in a multimarker panel for the detection of ovarian cancer . J Exp Clin Cancer Res 29 , 62 ( 2010 ). OpenUrl CrossRef PubMed 58. ↵ Elzek MA , Rodland KD . Proteomics of ovarian cancer: functional insights and clinical applications . Cancer Metastasis Rev 34 , 83 – 96 ( 2015 ). OpenUrl PubMed 59. ↵ Zhang M , Cheng S , Jin Y , Zhao Y , Wang Y . Roles of CA125 in diagnosis, prediction, and oncogenesis of ovarian cancer . Biochim Biophys Acta Rev Cancer 1875 , 188503 ( 2021 ). OpenUrl CrossRef 60. ↵ Cooper BC , et al. Preoperative CA 125 levels: an independent prognostic factor for epithelial ovarian cancer . Obstet Gynecol 100 , 59 – 64 ( 2002 ). OpenUrl CrossRef PubMed Web of Science 61. ↵ Diamandis EP , Yousef GM , Soosaipillai AR , Bunting P . Human kallikrein 6 (zyme/protease M/neurosin): a new serum biomarker of ovarian carcinoma . Clin Biochem 33 , 579 – 583 ( 2000 ). OpenUrl CrossRef PubMed Web of Science 62. ↵ Hefler L , et al. Monocyte chemoattractant protein-1 serum levels in ovarian cancer patients . Br J Cancer 81 , 855 – 859 ( 1999 ). OpenUrl CrossRef PubMed Web of Science 63. ↵ Bertini I , et al. Metabolomic NMR fingerprinting to identify and predict survival of patients with metastatic colorectal cancer . Cancer Res 72 , 356 – 364 ( 2012 ). OpenUrl Abstract / FREE Full Text 64. ↵ Di Donato S , et al. A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting . Cancers (Basel) 13 , ( 2021 ). 65. ↵ Derveaux E , Thomeer M , Mesotten L , Reekmans G , Adriaensens P . Detection of Lung Cancer via Blood Plasma and (1)H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor . Metabolites 11 , 537 ( 2021 ). OpenUrl PubMed 66. ↵ Garcia E , et al. Diagnosis of early stage ovarian cancer by 1H NMR metabonomics of serum explored by use of a microflow NMR probe . J Proteome Res 10 , 1765 – 1771 ( 2011 ). OpenUrl CrossRef PubMed 67. ↵ Karjalainen MK , et al. Genome-wide characterization of circulating metabolic biomarkers . Nature 628 , 130 – 138 ( 2024 ). OpenUrl CrossRef PubMed 68. ↵ Gyllensten U , et al. Next Generation Plasma Proteomics Identifies High-Precision Biomarker Candidates for Ovarian Cancer . Cancers (Basel) 14 , 1757 ( 2022 ). OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted May 21, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. 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Share Composite proteomic and metabolomic plasma biomarkers for detection of colorectal, lung and ovarian cancers Jim Åkerrén Ögren , Joakim Ekström , Natallia Rameika , Emma Torell , Chatarina Larsson , Gunilla Enblad , Ivaylo Stoimenov , Patrick Micke , Ulf Gyllensten , Mats Hellström , Bengt Glimelius , Karin Stålberg , Tobias Sjöblom medRxiv 2025.05.19.25326282; doi: https://doi.org/10.1101/2025.05.19.25326282 Share This Article: Copy Citation Tools Composite proteomic and metabolomic plasma biomarkers for detection of colorectal, lung and ovarian cancers Jim Åkerrén Ögren , Joakim Ekström , Natallia Rameika , Emma Torell , Chatarina Larsson , Gunilla Enblad , Ivaylo Stoimenov , Patrick Micke , Ulf Gyllensten , Mats Hellström , Bengt Glimelius , Karin Stålberg , Tobias Sjöblom medRxiv 2025.05.19.25326282; doi: https://doi.org/10.1101/2025.05.19.25326282 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Oncology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15227) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6597) Geriatric Medicine (668) Health Economics (997) Health Informatics (4534) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9230) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a003895b6914dfa9',t:'MTc3OTUzMzgxMw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();