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Colorectal cancer (CRC) is the second leading cause of cancer-related death in developed countries. Early detection of CRC leads to decreased CRC mortality. A blood-based CRC screening test is highly desirable due to limited invasiveness and high acceptance rate among patients compared to currently used fecal occult blood testing and colonoscopy. Here we describe the discovery and validation of a 29-gene panel in peripheral blood mononuclear cells (PBMC) for the detection of CRC and adenomatous polyps (AP). Blood samples were prospectively collected from a multicenter, case-control clinical study. First, we profiled 93 samples with 667 candidate and 3 reference genes by high throughput real-time PCR (OpenArray system). After analysis, 160 genes were retained and tested again on 51 additional samples. Low expressed and unstable genes were discarded resulting in a final dataset of 144 samples profiled with 140 genes. To define which genes, alone or in combinations had the highest potential to discriminate AP and/or CRC from controls, data were analyzed by a combination of univariate and multivariate methods. A list of 29 potentially discriminant genes was compiled and evaluated for its predictive accuracy by penalized logistic regression and bootstrap. This method discriminated AP >1cm and CRC from controls with a sensitivity of 59% and 75%, respectively, with 91% specificity. The behavior of the 29-gene panel was validated with a LightCycler 480 real-time PCR platform, commonly adopted by clinical laboratories. In this work we identified a 29-gene panel expressed in PBMC that can be used for developing a novel minimally-invasive test for accurate detection of AP and CRC using a standard real-time PCR platform.

BackgroundColorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. CRC deaths can be reduced with prevention and early diagnosis. Circulating tumor DNA-based liquid biopsies, are emerging tools for cancer detection. However, the tumor-signal-dependent nature of this approach results in low sensitivity in precancerous and early CRC stages. Here we propose the host immune response to the onset of cancer as an alternative approach for early detection of CRC.MethodsWe perform whole transcriptome analysis of peripheral blood mononuclear cells (PBMCs) isolated from individuals with CRC, precancerous lesions or negative colonoscopy in two independent cohorts using next-generation sequencing.ResultsWe discover and validate novel early CRC RNA biomarkers. Taking into account, and adjusting for, the sensitivity of PBMCs transcriptomes to processing times, we report distinct transcriptomic changes in the periphery related to specific CRC stages. Activation of innate immunity is already detectable in the peripheral blood of individuals with pre-malignant advanced adenomas. This immune response is followed by signs of transient B-cell activation and sustained inhibition of T-cell responses along CRC progression, whereby at late stages, protumoral myeloid cells, wound healing and coagulation processes prevail. Moreover, some biomarkers show similar dysregulation in tumors and are implicated in known pathways of CRC pathophysiology.ConclusionsThe strong systemic immune modulation triggered during CRC progression leads to previously unnoticed alterations detectable in PBMCs, paving the way for the development of an early CRC screening blood test, incorporating 226 validated biomarkers identified through immunotranscriptomics.

Previously, we demonstrated that changes in circulating tumor DNA (ctDNA) are promising biomarkers for early response prediction (ERP) to immune checkpoint inhibitors (ICIs) in metastatic urothelial cancer (mUC). In this study, we investigated the value of whole-blood immunotranscriptomics for ERP-ICI and integrated both biomarkers into a multimodal model to boost accuracy. Blood samples of 93 patients were collected at baseline and after 2-6 weeks of ICI for ctDNA (n = 88) and immunotranscriptome (n = 79) analyses. ctDNA changes were dichotomized into increase or no increase, the latter including patients with undetectable ctDNA. For RNA model development, the cohort was split into discovery (n = 29), test (n = 29), and validation sets (n = 21). Finally, RNA- and ctDNA-based predictions were integrated in a multimodal model. Clinical benefit (CB) was defined as progression-free survival beyond 6 months. Sensitivity (SN) and specificity (SP) of ctDNA increase for predicting non-CB (N-CB) was 59% and 92%, respectively. Immunotranscriptome analysis revealed upregulation of T cell activation, proliferation, and interferon signaling during treatment in the CB group, in contrast with N-CB patients. Based on these differences, a 10-gene RNA model was generated, reaching an SN and SP of 73% and 79%, respectively, in the test and 67% and 67% in the validation set for predicting N-CB. Multimodal model integration led to superior performance, with an SN and SP of 79% and 100%, respectively, in the validation cohort. The combination of whole-blood immunotranscriptome and ctDNA in a multimodal model showed promise for ERP-ICI in mUC and accurately identified patients with N-CB. Eurostars grant E! 114908 - PRECISE, Paul Speth Foundation (Bullseye project).

BackgroundImmune checkpoint inhibitor (ICI)-based immunotherapy has become standard of care in a growing number of advanced cancer indications. Recently, its use has been extended to the adjuvant and even neoadjuvant settings in a few solid tumor types. Despite this tremendous progress, however, a significant proportion of patients across cancer types fail to achieve clinical benefit. For metastatic melanoma (MM), the response rate is 40–60%. Redirecting non-responding patients to other therapeutic options is desired to avoid unnecessary side effects and improve patient outcomes. Whole blood transcriptome has been successfully applied to ICI response monitoring in metastatic urothelial cancer1 and shows great potential for MM.MethodsWhole blood samples from 29 patients with BRAF+ and high lactate dehydrogenase MM were collected before and after 6 or 12 weeks of anti-PD-1/CTLA-4 combination treatment. Nineteen were classified as responders (R) (progression-free survival (PFS) ≥ 6 months) and 10 as non-responders (NR).Transcriptome profiles were generated by RNA-seq and Differential Expression Genes (DEG) were identified by combining differential expression analysis (DEA) and advanced multivariate machine learning analysis (MLA). Biological relevancy was assessed by over representation and gene network analysis.ResultsDEA and MLA between baseline and on-treatment samples from responders (R) identified 141 DEGs associated with early response to the treatment. Functional analysis of the 141 DEGs revealed upregulated cell proliferation, immune checkpoint proteins and interferon-regulated genes. These results are in line with theoretical expectations from the therapy. Interestingly, similar pathway enrichments were observed in our previous analysis of a mUC ICI-treated cohort,1 suggesting a common modulation of immune programming upon immunotherapy, independent of the targeted disease.ConclusionsIn conclusion, whole blood immuno-transcriptome profiling combined with proprietary advanced data analytics identified biomarkers for the monitoring of ICI response in MM and mUC and offers new insights that may help understanding the underlying processes and mechanism of action.ReferencevanWilpe S, Wosika V, Ciarloni L. Whole Blood Transcriptome Profiling Identifies DNA Replication and Cell Cycle Regulation as Early Marker of Response to Anti-PD-1 in Patients with Urothelial Cancer. Cancers. 2021;13.

Colorectal cancer (CRC) is the second leading cause of cancer-related death in developed countries. Early detection of CRC leads to decreased CRC mortality. A blood-based CRC screening test is highly desirable due to limited invasiveness and high acceptance rate among patients compared to currently used fecal occult blood testing and colonoscopy. Here we describe the discovery and validation of a 29-gene panel in peripheral blood mononuclear cells (PBMC) for the detection of CRC and adenomatous polyps (AP). Blood samples were prospectively collected from a multicenter, case-control clinical study. First, we profiled 93 samples with 667 candidate and 3 reference genes by high throughput real-time PCR (OpenArray system). After analysis, 160 genes were retained and tested again on 51 additional samples. Low expressed and unstable genes were discarded resulting in a final dataset of 144 samples profiled with 140 genes. To define which genes, alone or in combinations had the highest potential to discriminate AP and/or CRC from controls, data were analyzed by a combination of univariate and multivariate methods. A list of 29 potentially discriminant genes was compiled and evaluated for its predictive accuracy by penalized logistic regression and bootstrap. This method discriminated AP >1cm and CRC from controls with a sensitivity of 59% and 75%, respectively, with 91% specificity. The behavior of the 29-gene panel was validated with a LightCycler 480 real-time PCR platform, commonly adopted by clinical laboratories. In this work we identified a 29-gene panel expressed in PBMC that can be used for developing a novel minimally-invasive test for accurate detection of AP and CRC using a standard real-time PCR platform.