HER2-low breast cancer (i.e., HER 1+ or 2+, without gene amplification) is an emerging subtype for which very few data are available, especially within the triple-negative breast cancer (TNBC) group. Our aim was to evaluate HER2 expression and its prognostic value in a large retrospective series of patients with non-metastatic TNBC (median age: 57.7 years; range: 28.5–98.6). Among the 296 TNBC samples, 83.8% were HER2 0, 13.5% were HER2 1+, and 2.7% were HER2 2+ (HercepTest^TM and 2018 ASCO/CAP guidelines for HER2 scoring). CK5/6 and/or EGFR-expressing androgen receptors and FOXA1-expressing tumors were classified as basal-like (63.8%) and molecular apocrine-like (MA, 40.2%), respectively. Compared with HER2 0 tumors, HER2 1+/2+ tumors exhibited a lower histological grade (1/2) (35.4% vs. 18.2%, p = 0.007) and MA profile (57.5% vs. 36.7%, p = 0.008). Moreover, patients with HER2 1+/2+ tumors were older (p = 0.047). After a median follow-up of 9.7 years, HER2 2+ tumors (compared with HER2 0/1+ tumors) were associated with worse relapse-free survival (RFS) (HR = 3.16, 95% CI [1.27; 7.85], p = 0.034) in a univariate analysis. Overall survival (OS) and RFS were not different in the HER2 0 and 1+/2+ groups. HER2 levels were not significantly associated with OS or RFS in a multivariate analysis.

Social media dedicated to health are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of retrieving clinically relevant information from such social media in order to analyze the quality of life of patients with breast cancer. Our aim was to detect the different topics discussed by patients on social media and to relate them to functional and symptomatic dimensions assessed in the internationally standardized self-administered questionnaires used in cancer clinical trials (European Organization for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire Core 30 [QLQ-C30] and breast cancer module [QLQ-BR23]). First, we applied a classic text mining technique, latent Dirichlet allocation (LDA), to detect the different topics discussed on social media dealing with breast cancer. We applied the LDA model to 2 datasets composed of messages extracted from public Facebook groups and from a public health forum (cancerdusein.org, a French breast cancer forum) with relevant preprocessing. Second, we applied a customized Jaccard coefficient to automatically compute similarity distance between the topics detected with LDA and the questions in the self-administered questionnaires used to study quality of life. Among the 23 topics present in the self-administered questionnaires, 22 matched with the topics discussed by patients on social media. Interestingly, these topics corresponded to 95% (22/23) of the forum and 86% (20/23) of the Facebook group topics. These figures underline that topics related to quality of life are an important concern for patients. However, 5 social media topics had no corresponding topic in the questionnaires, which do not cover all of the patients' concerns. Of these 5 topics, 2 could potentially be used in the questionnaires, and these 2 topics corresponded to a total of 3.10% (523/16,868) of topics in the cancerdusein.org corpus and 4.30% (3014/70,092) of the Facebook corpus. We found a good correspondence between detected topics on social media and topics covered by the self-administered questionnaires, which substantiates the sound construction of such questionnaires. We detected new emerging topics from social media that can be used to complete current self-administered questionnaires. Moreover, we confirmed that social media mining is an important source of information for complementary analysis of quality of life.

Metastatic colorectal cancer (mCRC) is one of the major causes of cancer-related death. Despite the substantial progress in mCRC management, it remains important to identify new therapeutic options and biological markers for personalized medicine. Here, we investigated the expression of claudin-1 (CLDN1), a major tight junction transmembrane protein, in the different colorectal cancer (CRC) molecular subtypes and then assessed the anti-tumor effect of a new anti-CLDN1 monoclonal antibody (mAb). Gene expression profiling and immunochemistry analysis of normal and tumor tissue samples from patients with stage IV CRC were used to determine CLDN1 gene expression. Then, the 6F6 mAb against CLDN1 extracellular part was generated. Its effect on CRC cell cycle, proliferation, survival and migration was assessed in vitro, using a 3D cell culture system, flow cytometry, clonogenic and migration assays. In vivo, 6 F6 mAb efficacy was evaluated in nude mice after subcutaneous xenografts or intrasplenic injection of CRC cells. Compared with normal mucosa where it was almost exclusively cytoplasmic, in CRC samples CLDN1 was overexpressed (p < 0.001) and mainly localized at the membrane. Moreover, it was differentially expressed in the various CRC molecular subtypes. The strongest expressions were found in the consensus molecular subtype CMS2 (p < 0.001), the transit-ampliflying (p < 0.001) and the C5 subtypes (p < 0.001). Lower CLDN1 expression predicted a better outcome in the molecular subtypes C3 and C5 (p = 0.012 and p = 0.004, respectively). CLDN1 targeting with the 6 F6 mAb led to reduction of survival, growth and migration of CLDN1-positive cells. In preclinical mouse models, the 6F6 mAb decreased tumor growth and liver metastasis formation. Our data indicate that CLDN1 targeting with an anti-CLDN1 mAb results in decreased growth and survival of CRC cells. This suggests that CLDN1 could be a new potential therapeutic target.

Background: Classical phase 1 dose-finding designs based on a single toxicity endpoint to assess the maximum tolerated dose were initially developed in the context of cytotoxic drugs. With the emergence of molecular targeted agents and immunotherapies, the concept of optimal biological dose (OBD) was subsequently introduced to account for efficacy in addition to toxicity. The objective was therefore to provide an overview of published phase 1 cancer clinical trials relying on the concept of OBD. Methods: We performed a systematic review through a computerized search of the MEDLINE database to identify early phase cancer clinical trials that relied on OBD. Relevant publications were selected based on a two-step process by two independent readers. Relevant information (phase, type of therapeutic agents, objectives, endpoints and dose-finding design) were collected. Results: We retrieved 37 articles. OBD was clearly mentioned as a trial objective (primary or secondary) for 22 articles and was traditionally defined as the smallest dose maximizing an efficacy criterion such as biological target: biological response, immune cells count for immunotherapies, or biological cell count for targeted therapies. Most trials considered a binary toxicity endpoint defined in terms of the proportion of patients who experienced a dose-limiting toxicity. Only two articles relied on an adaptive dose escalation design. Conclusions: In practice, OBD should be a primary objective for the assessment of the recommended phase 2 dose (RP2D) for a targeted therapy or immunotherapy phase I cancer trial. Dose escalation designs have to be adapted accordingly to account for both efficacy and toxicity.

γδ T-cells contribute to the immune response against many tumor types through their direct cytolytic functions and their capacity to recruit and regulate the biological functions of other immune cells. As potent effectors of the anti-tumor immune response, they are considered an attractive therapeutic target for immunotherapies, but their presence and abundance in the tumor microenvironment are not routinely assessed in patients with cancer. Here, we validated an antibody for immunohistochemistry analysis that specifically detects all γδ T-cell subpopulations in healthy tissues and in the microenvironment of different cancer types. Tissue microarray analysis of breast, colon, ovarian, and pancreatic tumors showed that γδ T-cell density varies among cancer types. Moreover, the abundance of γδ tumor-infiltrating lymphocytes was variably associated with the outcome depending on the cancer type, suggesting that γδ T-cell recruitment is influenced by the context. These findings also suggest that γδ T-cell detection and analysis might represent a new and interesting diagnostic or prognostic marker.