Patterns of genomic change in residual disease after neoadjuvant chemotherapy for estrogen receptor-positive and HER2-negative breast cancer

Background: Treatment of patients with residual disease after neoadjuvant chemotherapy for breast cancer is an unmet clinical need. We hypothesised that tumour subclones showing expansion in residual disease after chemotherapy would contain mutations conferring drug resistance. Methods: We studied oestrogen receptor and/or progesterone receptor-positive, HER2-negative tumours from 42 patients in the EORTC 10994/BIG 00-01 trial who failed to achieve a pathological complete response. Genes commonly mutated in breast cancer were sequenced in pre and post-treatment samples. Results: Oncogenic driver mutations were commonest in PIK3CA (38% of tumours), GATA3 (29%), CDH1 (17%), TP53 (17%) and CBFB (12%); and amplification was commonest for CCND1 (26% of tumours) and FGFR1 (26%). The variant allele fraction frequently changed after treatment, indicating that subclones had expanded and contracted, but there were changes in both directions for all of the commonly mutated genes. Conclusions: We found no evidence that expansion of clones containing recurrent oncogenic driver mutations is responsible for resistance to neoadjuvant chemotherapy. The persistence of classic oncogenic mutations in pathways for which targeted therapies are now available highlights their importance as drug targets in patients who have failed chemotherapy but provides no support for a direct role of driver oncogenes in resistance to chemotherapy. ClinicalTrials.gov: EORTC 10994/BIG 1-00 Trial registration number NCT00017095.SCOPUS: ar.jDecretOANoAutActifinfo:eu-repo/semantics/publishe

Molecular apocrine tumours in EORTC 10994/BIG 1-00 phase III study: pathological response after neoadjuvant chemotherapy and clinical outcomes

Background: We explored, within the EORTC10994 study, the outcomes for patients with molecular apocrine (MA) breast cancer, and defined immunohistochemistry (IHC) as androgen-receptor (AR) positive, oestrogen (ER) and progesterone (PR) negative. We also assessed the concordance between IHC and gene expression arrays (GEA) in the identification of MA cancers. Methods: Centrally assessed biopsies for AR, ER, PR, HER2 and Ki67 by IHC were classified into six subtypes: MA, triple-negative (TN) basal-like, luminal A, luminal B HER2 negative, luminal B HER2 positive and “other”. The two main objectives were the pCR rates and survival outcomes in the overall MA subtype (and further divided by HER2 status) and the remaining five subtypes. Results: IHC subtyping was obtained in 846 eligible patients. Ninety-three (11%) tumours were classified as the MA subtype. Both IHC and GEA data were available for 64 patients. In this subset, IHC concordance was 88.3% in identifying MA tumours compared with GEA. Within the MA subtype, pCR was observed in 33.3% of the patients (95% CI: 29.4–43.9) and the 5-year recurrence-free interval was 59.2% (95% CI: 48.2–68.6). Patients with MA and TN basal-like tumours have lower survival outcomes. Conclusions: Irrespective of their HER2 status, the prognosis for MA tumours remains poor and adjuvant trials evaluating anti-androgens should be considered.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

A biologically-based mathematical model for prediction of metastatic relapse

In the majority of cancers, secondary tumors (metastases) and associated complications are the main cause of death. To design the best therapy for a given patient, one of the major current challenge is to estimate, at diagnosis, the eventual burden of invisible metastases and the future time of emergence of these, as well as their growth speed. In this poster, we recapitulate results towards this aim using a mechanistic model based based on a physiologically-structured partial differential equation for the time dynamics of the population of metastases, combined to a nonlinear mixed-effects model for statistical representation of the parameters’ distribution in the population. Results are presented about the descriptive power of the model on data from clinically relevant ortho-surgical animal models of metastasis (breast and kidney tumors). Then the translation of this modeling approach toward the clinical reality is investigated. Using clinical imaging data of brain metastasis from non-small cell lung cancer, several biological processes were investigated to establish a minimal and biologically realistic model able to describe the data. Integration of this model into a biostatistical approach for individualized prediction of the model’s parameters from data only available at diagnosis is finally presented. This mechanistic approach is compared to biologically agnostic models based on statistical tools such as Cox regression or machine learning algorithms.Together, these results represent a step forward towards the integration of mathematical modeling as a predictive tool for personalized medicine in oncology

Breast Cancer Res Treat

Neurotensin receptor-1 (NTS) is increasingly recognized as a potential target in diverse tumors including breast cancer, but factors associated with NTS expression have not been fully clarified. We studied NTS expression using the Tissue MicroArray (TMA) of primary breast tumors from Institut Bergonié. We also studied association between NTS expression and clinical, pathological, and biological parameters, as well as patient outcomes. Out of 1419 primary breast tumors, moderate to strong positivity for NTS (≥ 10% of tumoral cells stained) was seen in 459 samples (32.4%). NTS staining was cytoplasmic in 304 tumors and nuclear in 155 tumors, a distribution which appeared mutually exclusive. Cytoplasmic overexpression of NTS was present in 21.5% of all breast tumors. In multivariate analysis, factors associated with cytoplasmic overexpression of NTS in breast cancer samples were higher tumor grade, Ki67 ≥ 20%, and higher pT stage. Cytoplasmic NTS was more frequent in tumors other than luminal A (30% versus 17.3%; p < 0.0001). Contrastingly, the main "correlates" of a nuclear location of NTS were estrogen receptor (ER) positivity, low E&E (Elston and Ellis) grade, Ki67 < 20%, and lower pT stage. In NTS-positive samples, cytoplasmic expression of NTS was associated with shorter 10-year metastasis-free interval (p = 0.033) compared to NTS nuclear staining. Ancillary analysis showed NTS expression in 73% of invaded lymph nodes from NTS-positive primaries. NTS overexpression was found in about one-third of breast tumors from patients undergoing primary surgery with two distinct patterns of distribution, cytoplasmic distribution being more frequent in aggressive subtypes. These findings encourage the development of NTS-targeting strategy, including radiopharmaceuticals for imaging and therapy.Translational Research and Advanced Imaging Laborator

Bioinformatics methods for analyzing anti-hormonal treatment resistance in breast cancer

International audienceOne in eight women are affected by breast cancer. Most of them receive hormonal therapy. Neoadjuvant hormonal therapy is a form of hormonal therapy given before surgery. Treatment for 6 months causes tumours to shrink, after which residual tumour is removed by surgery. Unfortunately, in some cases, the tumour cells are resistant to hormonal therapy and the patients relapse. This can be caused by intra-tumour heterogeneity: hormonal therapy eliminates drug-sensitive clones, leaving behind resistant clones. Understanding why some clones are resistant and what their characteristics are may lead to the development of alternative therapies. We compare DNA copy number profiles before and after treatment in the case of ER+ breast cancers. Very low depth sequencing was performed (Illumina GAIIx technology) on biopsies from breast tumours, before and after treatment. Reads were aligned to the human genome hg19 (bwa). CNAnorm was used to partition reference genome in intervals G = (i 1 , ...,i n) of non-overlapping sliding windows. Number of reads was converted to a count vector C = (c 1 , ...,c n) and then to a ratio vector with respect to a pool of normal female DNA

Terahertz pulse time-domain holography method for phase imaging of breast tissue

International audienc

The terahertz pulse time-domain holography method for phase imaging of breast tissue sample

International audienc

Solid papillary carcinoma with reverse polarity of the breast displays morphologic, immunohistochemical and molecular characteristics in comparison to other benign or malignant papillary lesions of the breast. A comparative study of nine additional cases

IF 6.655 (2017)International audienc