Colorectal cancers with microsatellite instability (MSI) represent 15% of all colorectal cancers, including Lynch syndrome as the most frequent hereditary form of this disease. Notably, MSI colorectal cancers have a higher density of tumor-infiltrating lymphocytes (TIL) than other colorectal cancers. This feature is thought to reflect the accumulation of frameshift mutations in sequences that are repeated within gene coding regions, thereby leading to the synthesis of neoantigens recognized by CD8 þ T cells. However, there has yet to be a clear link established between CD8 þ TIL density and frameshift mutations in colorectal cancer. In this study, we examined this link in 103 MSI colorectal cancers from two independent cohorts where frameshift mutations in 19 genes were analyzed and CD3 þ , CD8 þ , and FOXP3 þ TIL densities were quantitated. We found that CD8 þ TIL density correlated positively with the total number of frameshift mutations. TIL densities increased when frameshift mutations were present within the ASTE1, HNF1A, or TCF7L2 genes, increasing even further when at least one of these frameshift mutations was present in all tumor cells. Through in vitro assays using engineered antigen-presenting cells, we were able to stimulate peripheral cytotoxic T cells obtained from colorectal cancer patients with peptides derived from frameshift mutations found in their tumors. Taken together, our results highlight the importance of a CD8 þ T cell immune response against MSI colorectal cancer-specific neoantigens, establishing a preclinical rationale to target them as a personalized cellular immunotherapy strategy, an especially appealing goal for patients with Lynch syndrome. Cancer Res; 75(17); 3446-55. Ó2015 AACR.

DCE-US allowed quantitative in vivo evaluation of the functional effects of AVE8062, which was found most effective on tumoral microvasculature 6 hours after its administration. A clinical phase-1 study of AVE8062 is ongoing using the same ultrasonography methodology before and 6 and 24 hours postadministration.

BackgroundBy allowing intercellular communication between cells, tunneling nanotubes (TNTs) could play critical role in cancer progression. If TNT formation is known to require cytoskeleton remodeling, key mechanism controlling their formation remains poorly understood.MethodsThe cells of human bronchial (HBEC-3, A549) or mesothelial (H2452, H28) lines are transfected with different siRNAs (inactive, anti-RASSF1A, anti-GEFH1 and / or anti-Rab11). At 48 h post-transfection, i) the number and length of the nanotubes per cell are quantified, ii) the organelles, previously labeled with specific tracers, exchanged via these structures are monitored in real time between cells cultured in 2D or 3D and in normoxia, hypoxia or in serum deprivation condition.ResultsWe report that RASSF1A, a key-regulator of cytoskeleton encoded by a tumor-suppressor gene on 3p chromosome, is involved in TNTs formation in bronchial and pleural cells since controlling proper activity of RhoB guanine nucleotide exchange factor, GEF-H1. Indeed, the GEF-H1 inactivation induced by RASSF1A silencing, leads to Rab11 accumulation and subsequent exosome releasing, which in turn contribute to TNTs formation. Finally, we provide evidence involving TNT formation in bronchial carcinogenesis, by reporting that hypoxia or nutriment privation, two almost universal conditions in human cancers, fail to prevent TNTs induced by the oncogenic RASSF1A loss of expression.ConclusionsThis finding suggests for the first time that loss of RASSF1A expression could be a potential biomarker for TNTs formation, such TNTs facilitating intercellular communication favoring multistep progression of bronchial epithelial cells toward overt malignancy.Electronic supplementary materialThe online version of this article (10.1186/s12964-018-0276-4) contains supplementary material, which is available to authorized users.

IntroductionImmunohistochemical Ki67 labelling index (Ki67 LI) reflects proliferative activity and is a potential prognostic/predictive marker of breast cancer. However, its clinical utility is hindered by the lack of standardized measurement methodologies. Besides tissue heterogeneity aspects, the key element of methodology remains accurate estimation of Ki67-stained/counterstained tumour cell profiles. We aimed to develop a methodology to ensure and improve accuracy of the digital image analysis (DIA) approach.MethodsTissue microarrays (one 1-mm spot per patient, n = 164) from invasive ductal breast carcinoma were stained for Ki67 and scanned. Criterion standard (Ki67-Count) was obtained by counting positive and negative tumour cell profiles using a stereology grid overlaid on a spot image. DIA was performed with Aperio Genie/Nuclear algorithms. A bias was estimated by ANOVA, correlation and regression analyses. Calibration steps of the DIA by adjusting the algorithm settings were performed: first, by subjective DIA quality assessment (DIA-1), and second, to compensate the bias established (DIA-2). Visual estimate (Ki67-VE) on the same images was performed by five pathologists independently.ResultsANOVA revealed significant underestimation bias (P < 0.05) for DIA-0, DIA-1 and two pathologists’ VE, while DIA-2, VE-median and three other VEs were within the same range. Regression analyses revealed best accuracy for the DIA-2 (R-square = 0.90) exceeding that of VE-median, individual VEs and other DIA settings. Bidirectional bias for the DIA-2 with overestimation at low, and underestimation at high ends of the scale was detected. Measurement error correction by inverse regression was applied to improve DIA-2-based prediction of the Ki67-Count, in particular for the clinically relevant interval of Ki67-Count < 40%. Potential clinical impact of the prediction was tested by dichotomising the cases at the cut-off values of 10, 15, and 20%. Misclassification rate of 5-7% was achieved, compared to that of 11-18% for the VE-median-based prediction.ConclusionsOur experiments provide methodology to achieve accurate Ki67-LI estimation by DIA, based on proper validation, calibration, and measurement error correction procedures, guided by quantified bias from reference values obtained by stereology grid count. This basic validation step is an important prerequisite for high-throughput automated DIA applications to investigate tissue heterogeneity and clinical utility aspects of Ki67 and other immunohistochemistry (IHC) biomarkers.