Independent Validation Cohort Research Articles

Assessing treatment outcomes of chemoimmunotherapy in extensive-stage small cell lung cancer: an integrated clinical and radiomics approach

BackgroundSmall cell lung cancer (SCLC) is a highly malignant cancer characterized by metastasis and an extremely poor prognosis. Although combined chemoimmunotherapy improves the prognosis of extensive-stage (ES)-SCLC, the survival benefits...

Genome-wide methylation profiling identified methylated KCNA3 and OTOP2 as promising diagnostic markers for esophageal squamous cell carcinoma.

Early detection of esophageal squamous cell carcinoma (ESCC) can considerably improve the prognosis of patients. Aberrant cell-free DNA (cfDNA) methylation signatures are a promising tool for detecting ESCC. However, available markers based on cell-free DNA methylation are still inadequate. This study aimed to identify ESCC-specific cfDNA methylation markers and evaluate the diagnostic performance in the early detection of ESCC. We performed whole-genome bisulfite sequencing (WGBS) for 24 ESCC tissues and their normal adjacent tissues. Based on the WGBS data, we identified 21,469,837 eligible CpG sites (CpGs). By integrating several methylation datasets, we identified several promising ESCC-specific cell-free DNA methylation markers. Finally, we developed a dual-marker panel based on methylated KCNA3 and OTOP2 , and then, we evaluated its performance in our training and validation cohorts. The ESCC diagnostic model constructed based on KCNA3 and OTOP2 had an AUC of 0.91 [95% CI: 0.85-0.95], and an optimal sensitivity and specificity of 84.91% and 94.32%, respectively, in the training cohort. In the independent validation cohort, the AUC was 0.88 [95% CI: 0.83-0.92], along with an optimal sensitivity of 81.5% and specificity of 92.9%. The model sensitivity for stage I-II ESCC was 78.4%, which was slightly lower than the sensitivity of the model (85.7%) for stage III-IV ESCC. The dual-target panel based on cfDNA showed excellent performance for detecting ESCC and might be an alternative strategy for screening ESCC.

Small extrachromosomal circular DNAs as biomarkers for multi-cancer diagnosis and monitoring.

Small extrachromosomal circular DNAs (eccDNAs) have the potential to be cancer biomarkers. However, the formation mechanisms and functions of small eccDNAs selected in carcinogenesis are not clear, and whether the small eccDNA profile in the plasma of cancer patients represents that in cancer tissues remains to be elucidated. A novel sequencing workflow based on the nanopore sequencing platform was used to sequence naturally existing full-length small eccDNAs in tissues and plasma collected from 25 cancer patients (including prostate cancer, hepatocellular carcinoma and colorectal cancer), and from an independent validation cohort (including 7 cancer plasma and 14 healthy plasma). Compared with those in non-cancer tissues, small eccDNAs detected in cancer tissues had a significantly larger number and size (P=0.040 and 2.2e-16, respectively), along with more even distribution and different formation mechanisms. Although small eccDNAs had different general characteristics and genomic annotation between cancer tissues and the paired plasma, they had similar formation mechanisms and cancer-related functions. Small eccDNAs originated from some specific genes had great multi-cancer diagnostic value in tissues (AUC ≥ 0.8) and plasma (AUC>0.9), especially increasing the accuracy of multi-cancer prediction of CEA/CA19-9 levels. The high multi-cancer diagnostic value of small eccDNAs originated from ALK&ETV6 could be extrapolated from tissues (AUC=0.804) to plasma and showed high positive predictive value (100%) and negative predictive value (82.35%) in a validation cohort. As independent and stable circular DNA molecules, small eccDNAs in both tissues and plasma can be used as ideal biomarkers for cost-effective multi-cancer diagnosis and monitoring.

Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer

ABSTRACT Microbial signatures show remarkable potentials in predicting colorectal cancer (CRC). This study aimed to evaluate the diagnostic powers of multimodal microbial signatures, multi-kingdom species, genes, and single-nucleotide variants (SNVs) for detecting precancerous adenomas. We performed cross-cohort analyses on whole metagenome sequencing data of 750 samples via xMarkerFinder to identify adenoma-associated microbial multimodal signatures. Our data revealed that fungal species outperformed species from other kingdoms with an area under the ROC curve (AUC) of 0.71 in distinguishing adenomas from controls. The microbial SNVs, including dark SNVs with synonymous mutations, displayed the strongest diagnostic capability with an AUC value of 0.89, sensitivity of 0.79, specificity of 0.85, and Matthews correlation coefficient (MCC) of 0.74. SNV biomarkers also exhibited outstanding performances in three independent validation cohorts (AUCs = 0.83, 0.82, 0.76; sensitivity = 1.0, 0.72, 0.93; specificity = 0.67, 0.81, 0.67, MCCs = 0.69, 0.83, 0.72) with high disease specificity for adenoma. In further support of the above results, functional analyses revealed more frequent inter-kingdom associations between bacteria and fungi, and abnormalities in quorum sensing, purine and butanoate metabolism in adenoma, which were validated in a newly recruited cohort via qRT-PCR. Therefore, these data extend our understanding of adenoma-associated multimodal alterations in the gut microbiome and provide a rationale of microbial SNVs for the early detection of CRC.

Identification and validation of SERPINE1 as a prognostic and immunological biomarker in pan-cancer and in ccRCC.

Background: SERPINE1, a serine protease inhibitor involved in the regulation of the plasminogen activation system, was recently identified as a cancer-related gene. However, its clinical significance and potential mechanisms in pan-cancer remain obscure. Methods: In pan-cancer multi-omics data from public datasets, including The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx), and online web tools were used to analyze the expression of SERPINE1 in different cancers and its correlation with prognosis, genetic alteration, DNA promoter methylation, biological processes, immunoregulator expression levels, immune cell infiltration into tumor, tumor mutation burden (TMB), microsatellite instability (MSI), immunotherapy response and drug sensitivity. Further, two single-cell databases, Tumor Immune Single-cell Hub 2 (TISCH2) and CancerSEA, were used to explore the expression and potential roles of SERPINE1 at a single-cell level. The aberrant expression of SERPINE1 was further verified in clear cell renal cell carcinoma (ccRCC) through qRT-PCR of clinical patient samples, validation in independent cohorts using The Gene Expression Omnibus (GEO) database, and proteomic validation using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Results: The expression of SERPINE1 was dysregulated in cancers and enriched in endothelial cells and fibroblasts. Copy number amplification and low DNA promoter methylation could be partly responsible for high SERPINE1 expression. High SERPINE1 expression was associated with poor prognosis in 21 cancers. The results of gene set enrichment analysis (GSEA) indicated SERPINE1 involvement in the immune response and tumor malignancy. SERPINE1 expression was also associated with the expression of several immunoregulators and immune cell infiltration and could play an immunosuppression role. Besides, SERPINE1 was found to be related with TMB, MSI, immunotherapy response and sensitivity to several drugs in cancers. Finally, the high expression of SERPINE1 in ccRCC was verified using qRT-PCR performed on patient samples, six independent GEO cohorts, and proteomic data from the CPTAC database. Conclusion: The findings of the present study revealed that SERPINE1 exhibits aberrant expression in various types of cancers and is associated with cancer immunity and tumor malignancy, providing novel insights for individualized cancer treatment.

ProGraME: A novel flow cytometry algorithm for the diagnosis of low-risk myelodysplastic syndromes in patients with cytopenia.

Flow cytometry (FC) is, together with morphology, genetics, and cytogenetics, used in the diagnostic assessment of cytopenia, as its value in evaluating bone marrow dysplasia been highlighted by several studies. However, despite the development of algorithms and guidelines, there is still a lack of standardization of the FC assessment of bone marrow dysplasia. By combining FC, together with morphological analysis and cytogenetic/molecular assessment in a training cohort of 209 patients, we created a novel score, ProGraME, which includes four parameters, each from a different cell lineage (Progenitor cells, Granulocytes, Monocytes, Erythroid precursors), solely based on relevant population gating. Points for ProGraME were attained for: lymphoid precursors ≤5% of all CD34+ cells (1.5 point); a granulocyte-to-lymphocyte side-scatter ratio ≤6 (1 point); a monocyte CD33-CV% ≥ 63 (2 points), and an erythroid precursor CD36-CV% ≥ 65 (2 points). Using a cutoff of ≥2 as suggestive of dysplasia, ProGraME showed a sensitivity of 91% and a specificity of 81% in the training cohort and 95% and 75%, respectively, in an independent validation cohort of 159 patients. In addition, ProGraME had a very high negative predictive value of 97.1% and 97.8% in the training and validation cohorts, respectively, offering a useful tool for excluding bone marrow dysplasia. Finally, among the 23 CCUS patients that scored positive for dysplasia with ProGraME in the training cohort, 16 of them (69%) carried high-risk mutations, suggesting that FC might help identify early changes of dysplasia. ProGraME can potentially optimize the FC diagnosis of low-risk myelodysplasia without minimal requirements of flow analysis other than accurate population gating.

High expression of insulinoma-associated protein 1 (INSM1) distinguishes colorectal mixed and pure neuroendocrine carcinomas from conventional adenocarcinomas with diffuse expression of synaptophysin.

Complementary to synaptophysin and chromogranin A, insulinoma-associated protein 1 (INSM1) has emerged as a sensitive marker for the diagnosis of neuroendocrine neoplasms. Since there are no comparative data regarding INSM1 expression in conventional colorectal adenocarcinomas (CRCs) and colorectal mixed adenoneuroendocrine carcinomas/neuroendocrine carcinomas (MANECs/NECs), we examined INSM1 in a large cohort of conventional CRCs and MANECs/NECs. In conventional CRC, we put a special focus on conventional CRC with diffuse expression of synaptophysin, which carry the risk of being misinterpreted as a MANEC or a NEC. We investigated INSM1 according to the immunoreactive score in our main cohort of 1,033 conventional CRCs and 21 MANECs/NECs in comparison to the expression of synaptophysin and chromogranin A and correlated the results with clinicopathological parameters and patient survival. All MANECs/NECs expressed INSM1, usually showing high or moderate expression (57% high, 34% moderate, and 9% low), which distinguished them from conventional CRCs, which were usually INSM1 negative or low, even if they diffusely expressed synaptophysin. High expression of INSM1 was not observed in conventional CRCs. Chromogranin A was negative/low in most conventional CRCs (99%), but also in most MANECs/NECs (66%). Comparable results were observed in our independent validation cohorts of conventional CRC (n = 274) and MANEC/NEC (n = 19). Similar to synaptophysin, INSM1 expression had no prognostic relevance in conventional CRCs, while true MANEC/NEC showed a highly impaired survival in univariate and multivariate analyses (e.g. disease-specific survival: p < 0.001). MANECs/NECs are a highly aggressive variant of colorectal cancer, which must be reliably identified. High expression of INSM1 distinguishes MANEC/NEC from conventional CRCs with diffuse expression of the standard neuroendocrine marker synaptophysin, which do not share the same dismal prognosis. Therefore, high INSM1 expression is a highly specific/sensitive marker that is supportive for the diagnosis of true colorectal MANEC/NEC.

Association of CDX2 and mucin expression with chemotherapeutic benefits in patients with stage II/III gastric cancer.

Better predictors of patients with stage II/III gastric cancer (GC) most likely to benefit from adjuvant chemotherapy are urgently needed. This study aimed to assess the ability of CDX2 and mucin markers to predict prognosis and fluorouracil-based adjuvant chemotherapy benefits. CDX2 and mucin protein expressions were examined by immunohistochemistry and compared with survival and adjuvant chemotherapy benefits in a prospective evaluation cohort of 782 stage II/III GC patients. Then, the main findings were validated in an independent validation cohort (n = 386) and an external mRNA sequencing dataset (ACRG cohort, n = 193). In the evaluation cohort, CDX2, CD10, MUC2, MUC5AC, and MUC6 expressions were observed in 59.7%, 26.7%, 27.6%, 55.1%, and 57.7% of patients, respectively. However, only the expression of CDX2 was found to be associated with adjuvant chemotherapy benefits. Most importantly, CDX2-negative patients had a poorer prognosis when treated with surgery only, while the prognosis of CDX2-negative and CDX2-positive patients was similar when receiving postoperative adjuvant chemotherapy. Further analysis revealed that patients with CDX2 negative tumors benefited from chemotherapy (5-year overall survival rates: 60.0% with chemotherapy vs. 23.2% with surgery-only, p < 0.001), whereas patients with CDX2 positive tumors did not (pinteraction = 0.004). Consistent results were obtained in the validation and ACRG cohorts. Negative expression of CDX2 is an independent risk factor for survival in stage II/III GC, but subsequent adjuvant chemotherapy is able to compensate for this unfavorable effect. Therefore, active chemotherapy is more urgent for patients with negative CDX2 expression than for patients with positive CDX2 expression.

MFast-SeqS-based aneuploidy score in circulating cell-free DNA is a prognostic biomarker in prostate cancer.

Multiple prognostic biomarkers, including circulating tumour cell (CTC) counts, exist in metastatic castration-resistant prostate cancer (mCRPC) patients, but none of them have been implemented into daily clinical care. The modified fast aneuploidy screening test-sequencing system (mFast-SeqS), which yields a genome-wide aneuploidy score, is able to reflect the fraction of cell-free tumour DNA (ctDNA) within cell-free DNA (cfDNA) and may be a promising biomarker in mCRPC. In this study, we investigated the prognostic value of dichotomized aneuploidy scores (< 5 vs. ≥ 5) as well as CTC counts (< 5 vs. ≥ 5) in 131 mCRPC patients prior to treatment with cabazitaxel. We validated our findings in an independent cohort of 50 similarly treated mCRPC patients. We observed that, similar to the dichotomized CTC count [HR: 2.92; 95% confidence interval (CI);1.84-4.62], dichotomized aneuploidy scores (HR: 3.24; CI: 2.12-4.94) significantly correlated with overall survival in mCRPC patients. We conclude that a dichotomized aneuploidy score from cfDNA is a prognostic marker for survival in mCRPC patients within our discovery cohort and in an independent mCRPC validation cohort. Therefore, this easy and robust minimally-invasive assay can be readily implemented as a prognostic marker in mCRPC. A dichotomized aneuploidy score might also be used as a stratification factor in clinical studies to account for tumour load.

Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers.

To devise and validate radiomic signatures of impending hematoma expansion (HE) based on admission non-contrast head computed tomography (CT) of patients with intracerebral hemorrhage (ICH). Utilizing a large multicentric clinical trial dataset of hypertensive patients with spontaneous supratentorial ICH, we developed signatures predictive of HE in a discovery cohort (n = 449) and confirmed their performance in an independent validation cohort (n = 448). In addition to n = 1,130 radiomic features, n = 6 clinical variables associated with HE, n = 8 previously defined visual markers of HE, the BAT score, and combinations thereof served as candidate variable sets for signatures. The area under the receiver operating characteristic curve (AUC) quantified signatures' performance. A signature combining select radiomic features and clinical variables attained the highest AUC (95% confidence interval) of 0.67 (0.61-0.72) and 0.64 (0.59-0.70) in the discovery and independent validation cohort, respectively, significantly outperforming the clinical (pdiscovery = 0.02, pvalidation = 0.01) and visual signature (pdiscovery = 0.03, pvalidation = 0.01) as well as the BAT score (pdiscovery < 0.001, pvalidation < 0.001). Adding visual markers to radiomic features failed to improve prediction performance. All signatures were significantly (p < 0.001) correlated with functional outcome at 3-months, underlining their prognostic relevance. Radiomic features of ICH on admission non-contrast head CT can predict impending HE with stable generalizability; and combining radiomic with clinical predictors yielded the highest predictive value. By enabling selective anti-expansion treatment of patients at elevated risk of HE in future clinical trials, the proposed markers may increase therapeutic efficacy, and ultimately improve outcomes.

Deep learning–based scan range optimization can reduce radiation exposure in coronary CT angiography

ObjectivesCardiac computed tomography (CT) is essential in diagnosing coronary heart disease. However, a disadvantage is the associated radiation exposure to the patient which depends in part on the scan range. This study aimed to develop a deep neural network to optimize the delimitation of scan ranges in CT localizers to reduce the radiation dose.MethodsOn a retrospective training cohort of 1507 CT localizers randomly selected from calcium scoring and angiography scans and acquired between 2010 and 2017, optimized scan ranges were delimited by two radiologists in consensus. A neural network was trained to reproduce the scan ranges and was tested on two randomly selected and independent validation cohorts: an internal cohort of 233 CT localizers (January 2018–June 2020) and an external cohort from a nearby hospital of 298 CT localizers (July 2020–December 2020). Localizers where a bypass surgery was visible were excluded. The effective radiation dose to the patient was simulated using a Monte Carlo simulation. Scan ranges of radiographers, radiologists, and the network were compared using an equivalence test; likewise, the reduction in effective dose was tested using a superior test.ResultsThe network replicated the radiologists’ scan ranges with a Dice score of 96.5 ± 0.02 (p < 0.001, indicating equivalence). The generated scan ranges resulted in an effective dose reduction of 10.0% (p = 0.002) in the internal cohort and 12.6% (p < 0.001) in the external cohort compared to the scan ranges delimited by radiographers in clinical routine.ConclusionsAutomatic delimitation of the scan range can result in a radiation dose reduction to the patient.Clinical relevance statementFully automated delimitation of the scan range using a deep neural network enables a significant reduction in radiation exposure during CT coronary angiography compared to manual examination planning. It can also reduce the workload of the radiographers.Key Points• Scan range delimitation for coronary computed tomography angiography could be performed with high accuracy by a deep neural network.• Automated scan ranges showed a high agreement of 96.5% with the scan ranges of radiologists.• Using a Monte Carlo simulation, automated scan ranges reduced the effective dose to the patient by up to 12.6% (0.9 mSv) compared to the scan ranges of radiographers in clinical routine.Graphical abstract

Prediction of perioperative myocardial infarction/injury in high-risk patients after noncardiac surgery.

Perioperative myocardial infarction/injury (PMI) is a surprisingly common, yet difficult to predict cardiac complication in patients undergoing non-cardiac surgery. We aimed to assess the incremental value of preoperative cardiac troponin (cTn) concentration in the prediction of PMI. Among prospectively recruited patients at high cardiovascular risk (age >65 years OR >45 years with pre-existing cardiovascular disease), PMI was defined as an absolute increase in high-sensitivity cTnT (hs-cTnT) concentration of ≥14ng/L (the 99th percentile) above the preoperative concentration. PMI was centrally adjudicated by two independent cardiologists using serial measurements of hs-cTnT. Using logistic regression, three models were derived: Model 1 included patient- and procedure-related information, Model 2 adding routinely available laboratory values, and Model 3 further adding preoperative hs-cTnT concentration. Models were compared also versus preoperative hs-cTnT alone. Findings were validated in two independent cohorts. Among 6'944 patients, PMI occurred in 1058 patients (15.2%). The predictive accuracy as quantified by the area under the receiver-operating characteristic curve was 0.73 (95%CI 0.71-0.74) for Model 1, 0.75 (95%CI 0.74-0.77) for Model 2, 0.79 (95%CI 0.77-0.80) for Model 3, and 0.74 for hs-cTnT alone. Model 3 included 10 preoperative variables: age, body-mass-index, known coronary artery disease, metabolic equivalent >4, risk of surgery, emergency surgery, planned duration of surgery, haemoglobin, platelet count and hs-cTnT. These findings were confirmed in both independent validation cohorts (n=722, n=966). Preoperative cTn adds incremental value above patient- and procedure-related variables as well as routine laboratory in the prediction of PMI.

NEIM-13 THE PROGNOSTIC ROLE OF VENTRICULAR SIZE AND ITS DYNAMICS IN PATIENTS WITH LEPTOMENINGEAL METASTASIS FROM SOLID TUMORS

Abstract BACKGROUND Hydrocephalus is a common radiological sign in patients with leptomeningeal metastasis (LM) from solid tumors and can be assessed using the Evans index (EI) with high interrater agreement. Here we explored the prognostic value of ventricular size at diagnosis and during the disease course of LM. METHODS We assembled a cohort of 113 adult patients with a diagnosis of LM from solid extra-CNS tumors and explored the association of ventricular size assessed by the EI at diagnosis, of its modification between diagnosis and evaluation at first follow-up and at first progression, with outcome. RESULTS Median age was 58.2 years (interquartile range (IQR) 46.1-65.7), 41 patients (36%) were male, the most frequent cancers were lung cancer (n=39, 35%), breast cancer (n=36, 32%) andmelanoma (n=23, 20%). The median EI at baseline was 0.28 (IQR 0.26-0.31), the EI value was 0.27 or more in 67 patients (59%) and 0.30 or more in 37 patients (33%). At LM progression, an increase of EI of 0.01 or more was noted in 18 of 34 patients (53%). The median overall survival was 2.9 months (IQR 1-7.2). Patients with a baseline EI of 0.26 or less had a longer survival than those with an EI of 0.27 or more (5.3 months, IQR 2.4-10.8, versus 1.3 months, IQR 0.6-4.1) (p=0.006). Among patients with follow-up scans available, median overall survival was 9.7 months (IQR 5.6-21.4) for patients with stable or decreased EI at first follow-up as opposed to 6.4 months (IQR 3.2-10.5) for those with an increase in the EI (p=0.292). CONCLUSION The EI at baseline is prognostic in LM. An increase of EI during the follow-up is associated with inferior LM-PFS. An independent validation cohort with larger sample size and evaluation of confounding factors will help to better define the role of EI assessments in LM.

Histology-based classifier to distinguish early mycosis fungoides from atopic dermatitis.

Histopathological differentiation of early mycosis fungoides (MF) from benign chronic inflammatory dermatoses remains difficult and often impossible, despite the inclusion of all available diagnostic parameters. To identify the most impactful histological criteria for a predictive diagnostic model to discriminate MF from atopic dermatitis (AD). In this multicentre study, two cohorts of patients with either unequivocal AD or MF were evaluated by two independent dermatopathologists. Based on 32 histological attributes, a hypothesis-free prediction model was developed and validated on an independent patient's cohort. A reduced set of two histological features (presence of atypical lymphocytes in either epidermis or dermis) was trained. In an independent validation cohort, this model showed high predictive power (95% sensitivity and 100% specificity) to differentiate MF from AD and robustness against inter-individual investigator differences. The study investigated a limited number of cases and the classifier is based on subjectively evaluated histological criteria. Aiming at distinguishing early MF from AD, the proposed binary classifier performed well in an independent cohort and across observers. Combining this histological classifier with immunohistochemical and/or molecular techniques (such as clonality analysis or molecular classifiers) could further promote differentiation of early MF and AD.

TIPS-07 LONGITUDINAL CSF PROTEOMICS FOR MONITORING AND PHARMACODYNAMIC BIOMARKER DISCOVERY

Abstract Rapid feedback is needed to understand the individualized biological impacts of novel glioma therapies. We are performing glioma biomarker discovery by serial cerebrospinal fluid (CSF) sampling from Ommaya reservoirs or lumbar punctures to determine how the CSF proteome can reveal early longitudinal intelligence regarding glioma status, biology, and therapeutic response. We aimed to leverage access to each patient’s glioma via CSF to identify candidate biomarkers of tumor burden, biology, and therapeutic challenge. Global proteomic CSF analysis was performed via Somalogic– an aptamer-based technology for highly sensitive and specific analysis of over 7,000 proteins. Discovery analysis comprised of the top 500 ranked proteins in the CSF from seven patients with high-grade gliomas versus non-glioma (normal pressure hydrocephalus) controls. The top HGG proteins were then filtered to include only proteins that met two additional criteria of 1) decrease with resection, and 2) increase with recurrence in individual paired patient samples. Additionally, in individual patients for whom serial CSF samples were available, protein fold change lists were created and ranked to determine the most or least differentially abundant proteins between various time points. Proteomic enrichment analysis revealed a conserved HGG CSF proteomic signature defined by 79 proteins, including ones known to be over-expressed in solid tumor malignancies, such as retinoblastoma binding protein 4, heat shock protein 90, and sorcin. The HGG proteomic signature was consistently enriched in an independent validation cohort consisting of 13 gliomas diverse in primary versus recurrent status, subtype, and grade, when compared to independent control CSF samples. Encouragingly, proteins in the HGG signature decreased in two patients following decreased tumor burden from surgical resection. Our preliminary data demonstrate the ability to gain detailed, individualized insights regarding glioma biology, tumor burden, and evolution through global CSF proteomics acquired from longitudinal neurosurgical access to each patient’s unique glioma.

Identification and initial validation of maximal tumor area as a novel prognostic factor for overall and disease-free survival in patients with resectable colon cancer: a retrospective study.

The tumor area may be a potential prognostic indicator. The present study aimed to determine and validate the prognostic value of tumor area in curable colon cancer. This retrospective study included a training and validation cohorts of patients who underwent radical surgery for colon cancer. Independent prognostic factors for overall survival (OS) and disease-free survival (DFS) were identified using Cox proportional hazards regression models. The prognostic discrimination was evaluated using the integrated area under the receiver operating characteristic curves (iAUCs) for prognostic factors and models. The prognostic discrimination between tumor area and other individual factors was compared, along with the prognostic discrimination between the tumor-node-metastasis (TNM) staging system and other prognostic models. Two-sample Wilcoxon tests were carried out to identify significant differences between the two iAUCs. A two-sided P <0.05 was considered statistically significant. A total of 3051 colon cancer patients were included in the training cohort and 872 patients in the validation cohort. Tumor area, age, differentiation, T stage, and N stage were independent prognostic factors for both OS and DFS in the training cohort. Tumor area had a better OS and DFS prognostic discrimination characteristics than T stage, maximal tumor diameter, differentiation, tumor location, and number of retrieved lymph nodes. The novel prognostic model of T stage + N stage + tumor area (iAUC for OS, 0.714, P <0.001; iAUC for DFS, 0.694, P <0.001) showed a better prognostic discrimination than the TNM staging system (T stage + N stage; iAUC for OS, 0.664; iAUC for DFS, 0.658). Similar results were observed in an independent validation cohort. Tumor area was identified as an independent prognostic factor for both OS and DFS in curable colon cancer patients, and in cases with an adequate number of retrieved lymph nodes. The novel prognostic model of combining T stage, N stage, and tumor area may be an alternative to the current TNM staging system.

A panel of seven protein tumour markers for effective and affordable multi-cancer early detection by artificial intelligence.

155 Background: Cancer early detection aims at reducing cancer deaths. Unfortunately, many established cancer screening technologies are not suitable for use in low- and middle-income countries (LMICs) due to cost, complexity, and dependency on extensive medical infrastructure. Methods: Nearly 10,000 participants [1959 cancer cases (containing more than nine common cancer types) and 7423 non-cancer cases] were divided into one training and two independent validation cohorts. One tube of peripheral blood samples was collected from each participant and quantified using a panel of seven selected protein tumour markers (PTMs) by a common clinical electrochemiluminescence immunoassay analyser. An algorithm named OncoSeek was established using artificial intelligence (AI) to distinguish cancer from non-cancer cases by calculating the probability of cancer (POC) index based on the quantification results of the seven PTMs and clinical information including sex and age of the subjects, and to predict the possible affected tissue of origin (TOO) for those who have been detected with cancer signal. Results: The conventional clinical method relied only on a single threshold for each PTM which would make a big problem when combining the results of those markers, the false positive rate would accumulate as the number of markers increased. Nevertheless, OncoSeek was empowered by AI technology to significantly reduce the false positive rate, increasing the specificity from 56·9% [95% confidence interval (CI): 55·8% to 58·0%] to 92·9% (95% CI: 92·3% to 93·5%). In all cancer types, the overall sensitivity of the OncoSeek test was 51·7% (95% CI: 49·4% to 53·9%). And the performance was generally consistent in the training and the two validation cohorts. The sensitivities ranged from 37·1% to 77·6% for the detection of the nine common cancer types (breast, colorectum, liver, lung, lymphoma, oesophagus, ovary, pancreas, and stomach), which account for ~59·2% of global cancer deaths annually. Furthermore, it has shown excellent sensitivity in several high-mortality cancer types for which there are lacking routine screening tests in the clinic, such as the sensitivity of pancreatic cancer was 77·6% (95% CI: 69·3% to 84·6%). The overall accuracy of TOO prediction in the true positives was 66·8%, which could assist the clinical diagnostic workup. Conclusions: OncoSeek significantly outperforms the conventional clinical method, representing a novel blood-based test for multi-cancer early detection (MCED) which is a non-invasive, easy, efficient, and robust approach. Moreover, the accuracy of TOO facilitates the follow-up diagnostic workup and this method is affordable (less than $25) and accessible requiring nothing more than a blood draw at the screening sites, which makes it acceptable and sustainable in LMICs.

Prospective validation of the IRSN-23 model for personalized chemotherapy and proposal of new breast cancer subtype classification.

10 Background: The IRSN-23 model uses DNA microarray analysis of tumor tissue to stratify patients into highly sensitive (Gp-R) or less sensitive (Gp-NR) chemotherapy groups based on immune-related gene expression. In a previous report (n=901), Gp-R showed a 39% pCR rate, while Gp-NR had 11% (P=4.98E-23; Sota, Y., et al., Annals of Oncology 25.1 (2014): 100-106). This study aimed to evaluate the reproducibility of the IRSN-23 model in prospective independent validation cohorts and investigate its clinical significance and impact on breast cancer subtype classification. Methods: Tumor tissues were obtained from 143 breast cancer patients undergoing preoperative chemotherapy at Osaka University Hospital (OUH). Patients were classified into Gp-R or Gp-NR using the IRSN-23 model, and its ability to predict pathological complete response (pCR) was assessed. Results were validated with independent public datasets (n=965; breast cancer (n=919) and rectal cancer (n=46)). Results: In the OUH prospective cohort, the pCR rate was significantly higher in the Gp-R group (29.3% (11/41)) than in the Gp-NR group (1.4% (1/71)) without trastuzumab (P=1.70E-5). In all prospective validation cohorts without anti-HER2 therapy, the pCR rate in the Gp-R group was significantly higher than in the Gp-NR group. Pooled analysis of the prospective validation set revealed higher pCR rates in the Gp-R group than in the Gp-NR group, both without (Gp-R 41.9% (104/248) vs. Gp-NR 13.6% (50/367), P=4.0E-15) and with (Gp-R 48.3% (57/118) vs. Gp-NR 35.5% (66/186), P=0.03) anti-HER2 therapy (Table). Collaborative analyses of IRSN-23 Immune Score (IS) and OncotypeDx Recurrence Score (RS) suggested the identification of a highly sensitive chemotherapy group (pCR%; IS&gt;-25+RS&gt;25: 21.6% vs. IS ≤ 25 + RS &gt; 25: 2.9%, P=1.13E-04). PAM50 as an attack factor and IRSN-23 as a defense factor capture the tumor microenvironment, suggesting a new subtype classification of breast cancer. Conclusions: This study offers prospective validation of the IRSN-23 model in predicting chemotherapy efficacy, exhibiting high reproducibility. The findings indicate the IRSN-23 model is clinically valuable and propose new breast cancer subtype classification. These results hold significant implications for personalized treatment strategies.[Table: see text]

Identification of novel candidate biomarkers for acute myocardial infarction by the Olink proteomics platform

BackgroundBoth pathological and normal processes depend on proteins. In this study, plasma protein profiles were analyzed by a novel proximity extension assay (PEA) to identify potential pathogenic mechanisms and diagnostic biomarkers in patients diagnosed with acute myocardial infarction (AMI). MethodsIn this study, we identified a total of 92 plasma proteins using the Olink Target 96 Cardiovascular III panel in a cohort consisting of 30 healthy controls (HC), 28 patients with unstable angina (UA) and 30 patients with AMI. Subsequently, we conducted a differential expression analysis to identify protein molecules that were specifically expressed in patients with AMI. To gain insights into the potential functional mechanisms of these differentially expressed molecules, we performed Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Following that, the utilization of least absolute shrinkage and selection operator (LASSO) regression facilitated the identification of potential protein biomarkers, enabling the differentiation between AMI and UA. A diagnostic model was subsequently developed through logistic regression, and the effectiveness of these markers was assessed using receiver operating characteristic (ROC) analysis. Ultimately, the diagnostic capabilities of these potential biomarkers were validated in an independent validation cohort consisting of 30 UA cases and 30 AMI cases. ResultsIn this study, a comprehensive analysis of plasma proteins identified a total of 92 proteins. Further analysis using analysis of variance revealed that 25 proteins exhibited specific expression in the AMI group compared to the HC and UA groups. Additionally, KEGG enrichment analysis indicated that these differentially expressed proteins were primarily associated with the activation of cytokine-cytokine receptor interaction, PI3K-Akt signaling pathway, and GnRH signaling pathway. AGRP, TGM2, IL6, GH1, and CA5A were identified through LASSO regression as prospective protein biomarkers for distinguishing between UA and AMI. The diagnostic model comprising these five proteins exhibited exceptional performance in both the discovery and validation datasets, surpassing AUC values of 0.9. ConclusionThe findings of our study provide additional insights into the involvement of the inflammatory response and AKT cascade response in the development of AMI. Moreover, we have identified potential protein markers that could be utilized for the accurate diagnosis of AMI. These results offer a fresh perspective for clinical decision-making in the context of AMI.

Identification of Nectin Family Interactive Gene Panel and Stratification of Clinical Outcomes in Patients with Pancreatic Cancer.

Although patient-risk stratification is important for selecting individualized treatment for pancreatic ductal adenocarcinoma (PDAC), predicting the oncologic outcomes after surgery remains a challenge. In this study, we identified a nectin family gene panel (NFGP) that can accurately stratify oncologic outcomes in patients with PDAC. Comprehensive analysis of the expression of 9 nectin family genes identified the NFGP, which was assessed for predictive performance in 2 independent public cohorts (The Cancer Genome Atlas [TCGA] n = 176; International Cancer Genome Consortium [ICGC] n = 89). It was subsequently trained and validated for the in-house training cohort without neo-adjuvant therapy (NAT, n = 213) and the validation cohort with NAT (n = 307). Using the Cox regression model, NFGP derived from 9 nectin family genes accurately stratified overall survival (OS) in TCGA (p = 0.038) and ICGC (p = 0.005). We subsequently optimized NFGP, which robustly discriminated postoperative prognosis, OS (p = 0.014) and relapse-free survival ([RFS] p = 0.006) in the training cohort. The NFGP was successfully validated in an independent validation cohort (OS: p < 0.001; RFS: p = 0.004). Multivariate analysis demonstrated the NFGP was an independent prognostic factor for OS and RFS in the training (p = 0.028 and 0.008, respectively) and validation (p < 0.001 and 0.013, respectively) cohorts. The subcohort analyses showed that the predictive performance of NFGP is applicable to the patients' subcohort according to resectability or adjuvant therapy status. Additionally, a combination model of NFGP score and CA19-9 level emerged with improved accuracy for predicting prognosis. This study established the predictive significance of NFGP for oncologic outcomes after surgery in PDAC. Our data demonstrate its clinical impact as a potent biomarker for optimal patient selection for individualized treatment strategies.