A novel "reactomics" approach for cancer diagnostics.

Non-invasive detection and monitoring of lethal diseases, such as cancer, are considered as effective factors in treatment and survival. We describe a new disease diagnostic approach, denoted "reactomics", based upon reactions between blood sera and an array of vesicles comprising different lipids and polydiacetylene (PDA), a chromatic polymer. We show that reactions between sera and such a lipid/PDA vesicle array produce chromatic patterns which depend both upon the sera composition as well as the specific lipid constituents within the vesicles. The chromatic patterns were processed through machine-learning algorithms, and the bioinformatics analysis could distinguish both between cancer-bearing and healthy patients, respectively, as well between two types of cancers. Size-separation and enzymatic digestion experiments indicate that lipoproteins are the primary components in sera which react with the chromatic biomimetic vesicles. This colorimetric reactomics concept is highly generic, robust, and does not require a priori knowledge upon specific disease markers in sera. Therefore, it could be employed as complementary or alternative approach for disease diagnostics.

African descents are more sensitive than European descents to the antitumor compounds α-hederin and kalopanaxsaponin I.

α-Hederin, a natural triterpene saponin and its derivative kalopanaxsaponin I (ksI) exhibit cytotoxicity against various cancer cell lines and IN VIVO tumors. We studied the genetic variants contributing to the activity of these two anticancer compounds. Cell lines derived from 30 trios of European descent (Centre d'Etude du Polymorphisme Human, CEPH; CEU) and 30 trios of African descent (Yoruban, YRI) were used. Cytotoxicity was determined as inhibition of cell growth at increasing concentrations of α-hederin or ksI for 24 h. In comparison to the European, the Yoruban populations revealed a higher sensitivity to α-hederin and to ksI that can be attributed to several unique SNPs. These SNPs are located near 111 and 130 genes in the European and the Yoruban populations, respectively, raising the possibility that some of these genes contribute to the differential sensitivity to these compounds.

Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial.

Precision medicine focuses on DNA abnormalities, but not all tumors have tractable genomic alterations. The WINTHER trial ( NCT01856296 ) navigated patients to therapy on the basis of fresh biopsy-derived DNA sequencing (arm A; 236 gene panel) or RNA expression (arm B; comparing tumor to normal). The clinical management committee (investigators from five countries) recommended therapies, prioritizing genomic matches; physicians determined the therapy given. Matching scores were calculated post-hoc for each patient, according to drugs received: for DNA, the number of alterations matched divided by the total alteration number; for RNA, expression-matched drug ranks. Overall, 303 patients consented; 107 (35%; 69 in arm A and 38 in arm B) were evaluable for therapy. The median number of previous therapies was three. The most common diagnoses were colon, head and neck, and lung cancers. Among the 107 patients, the rate of stable disease ≥6 months and partial or complete response was 26.2% (arm A: 23.2%; arm B: 31.6% (P = 0.37)). The patient proportion with WINTHER versus previous therapy progression-free survival ratio of >1.5 was 22.4%, which did not meet the pre-specified primary end point. Fewer previous therapies, better performance status and higher matching score correlated with longer progression-free survival (all P < 0.05, multivariate). Our study shows that genomic and transcriptomic profiling are both useful for improving therapy recommendations and patient outcome, and expands personalized cancer treatment.

NKp44 and NKp30 splice variant profiles in decidua and tumor tissues: a comparative viewpoint.

NKp44 and NKp30 splice variant profiles have been shown to promote diverse cellular functions. Moreover, microenvironment factors such as TGF-ß, IL-15 and IL-18 are able to influence both NKp44 and NKp30 splice variant profiles, leading to cytokine-associated profiles. Placenta and cancerous tissues have many similarities; both are immunologically privileged sites and both share immune tolerance mechanisms to support tissue development. Therefore, we studied the profiles of NKp44 and NKp30 splice variants in these states by comparing (i) decidua from pregnancy disorder and healthy gestation and (ii) matched normal and cancer tissue. Decidua samples had high incidence of both NKp44 and NKp30. In cancerous state it was different; while NKp30 expression was evident in most cancerous and matched normal tissues, NKp44 incidence was lower and was mostly associated with the cancerous tissues. A NKp44-1dominant inhibitory profile predominated in healthy pregnancy gestation. Interestingly, the NKp44-2/3 activation profile becomes the leading profile in spontaneous abortions, whereas balanced NKp44 profiles were observed in preeclampsia. In contrast, a clear preference for the NKp30a/b profile was evident in the 1st trimester decidua, yet no significant differences were observed for NKp30 profiles between healthy gestation and spontaneous abortions/preeclampsia. Both cancerous and matched normal tissues manifested balanced NKp30c inhibitory and NKp30a/b activation profiles with a NKp44-1dominant profile. However, a shift in NKp30 profiles between matched normal and cancer tissue was observed in half of the cases. To summarize, NKp44 and NKp30 splice variants profiles are tissue/condition specific and demonstrate similarity between placenta and cancerous tissues.

Serum apolipoproteins C-I and C-III are reduced in stomach cancer patients: results from MALDI-based peptidome and immuno-based clinical assays.

Finding new peptide biomarkers for stomach cancer in human sera that can be implemented into a clinically practicable prediction method for monitoring of stomach cancer. We studied the serum peptidome from two different biorepositories. We first employed a C8-reverse phase liquid chromatography approach for sample purification, followed by mass-spectrometry analysis. These were applied onto serum samples from cancer-free controls and stomach cancer patients at various clinical stages. We then created a bioinformatics analysis pipeline and identified peptide signature discriminating stomach adenocarcinoma patients from cancer-free controls. Matrix Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) results from 103 samples revealed 9 signature peptides; with prediction accuracy of 89% in the training set and 88% in the validation set. Three of the discriminating peptides discovered were fragments of Apolipoproteins C-I and C-III (apoC-I and C-III); we further quantified their serum levels, as well as CA19-9 and CRP, employing quantitative commercial-clinical assays in 142 samples. ApoC-I and apoC-III quantitative results correlated with the MS results. We then employed apoB-100-normalized apoC-I and apoC-III, CA19-9 and CRP levels to generate rules set for stomach cancer prediction. For training, we used sera from one repository, and for validation, we used sera from the second repository. Prediction accuracies of 88.4% and 74.4% were obtained in the training and validation sets, respectively. Serum levels of apoC-I and apoC-III combined with other clinical parameters can serve as a basis for the formulation of a diagnostic score for stomach cancer patients.