t(5;9)(q32;p24) KANK1/PDGFRB

Review on t(5;9)(q32;p24) KANK1/PDGFRB, with data on clinics, and the genes involved

Refinement of 1p36 Alterations Not Involving PRDM16 in Myeloid and Lymphoid Malignancies

Fluorescence in situ hybridization was performed to characterize 81 cases of myeloid and lymphoid malignancies with cytogenetic 1p36 alterations not affecting the PRDM16 locus. In total, three subgroups were identified: balanced translocations (N = 27) and telomeric rearrangements (N = 15), both mainly observed in myeloid disorders; and unbalanced non-telomeric rearrangements (N = 39), mainly observed in lymphoid proliferations and frequently associated with a highly complex karyotype. The 1p36 rearrangement was isolated in 12 cases, mainly myeloid disorders. The breakpoints on 1p36 were more widely distributed than previously reported, but with identifiable rare breakpoint cluster regions, such as the TP73 locus. We also found novel partner loci on 1p36 for the known multi-partner genes HMGA2 and RUNX1. We precised the common terminal 1p36 deletion, which has been suggested to have an adverse prognosis, in B-cell lymphomas [follicular lymphomas and diffuse large B-cell lymphomas with t(14;18)(q32;q21) as well as follicular lymphomas without t(14;18)]. Intrachromosomal telomeric repetitive sequences were detected in at least half the cases of telomeric rearrangements. It is unclear how the latter rearrangements occurred and whether they represent oncogenic events or result from chromosomal instability during oncogenesis

Clinical management of first-line advanced triple-negative breast cancer patients

Chemotherapy has represented the main treatment option for patients with advanced triple-negative breast cancer for a long time. However, due to our better understanding of tumour biology, recent clinical trials led to a change in the treatment paradigm of this disease, identifying clinically relevant subgroups with different therapeutic options. Both clinical and biological factors have become relevant and need to be considered in the treatment decision algorithm of this heterogeneous disease

Machine Learning Algorithm to Estimate Distant Breast Cancer Recurrence at the Population Level with Administrative Data

Hava Izci,1 Gilles Macq,2 Tim Tambuyzer,2 Harlinde De Schutter,2 Hans Wildiers,1,3 Francois P Duhoux,4 Evandro de Azambuja,5 Donatienne Taylor,6 Gracienne Staelens,7 Guy Orye,8 Zuzana Hlavata,9 Helga Hellemans,10 Carine De Rop,11 Patrick Neven,1,3 Freija Verdoodt2 1KU Leuven - University of Leuven, Department of Oncology, Leuven, B-3000, Belgium; 2Belgian Cancer Registry, Research Department, Brussels, Belgium; 3University Hospitals Leuven, Multidisciplinary Breast Center, Leuven, B-3000, Belgium; 4Department of Medical Oncology, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Brussels, Belgium; 5Institut Jules Bordet and l’Université Libre de Bruxelles (U.L.B), Brussels, Belgium; 6CHU UCL Namur, Site Sainte-Elisabeth, Namur, Belgium; 7Multidisciplinary Breast Center, General Hospital Groeninge, Kortrijk, Belgium; 8Department of Obstetrics and Gynecology, Jessa Hospital, Hasselt, Belgium; 9Department of Medical Oncology, CHR Mons-Hainaut, Mons, Hainaut, Belgium; 10Department of Obstetrics and Gynaecology, AZ Delta, Roeselaere, Belgium; 11Department of Obstetrics and Gynaecology, Imelda Hospital, Bonheiden, BelgiumCorrespondence: Hava Izci, KU Leuven, Department of oncology, Herestraat 49 Box 7003-06, Leuven, 3000, Belgium, Email [email protected]: High-quality population-based cancer recurrence data are scarcely available, mainly due to complexity and cost of registration. For the first time in Belgium, we developed a tool to estimate distant recurrence after a breast cancer diagnosis at the population level, based on real-world cancer registration and administrative data.Methods: Data on distant cancer recurrence (including progression) from patients diagnosed with breast cancer between 2009– 2014 were collected from medical files at 9 Belgian centers to train, test and externally validate an algorithm (i.e., gold standard). Distant recurrence was defined as the occurrence of distant metastases between 120 days and within 10 years after the primary diagnosis, with follow-up until December 31, 2018. Data from the gold standard were linked to population-based data from the Belgian Cancer Registry (BCR) and administrative data sources. Potential features to detect recurrences in administrative data were defined based on expert opinion from breast oncologists, and subsequently selected using bootstrap aggregation. Based on the selected features, classification and regression tree (CART) analysis was performed to construct an algorithm for classifying patients as having a distant recurrence or not.Results: A total of 2507 patients were included of whom 216 had a distant recurrence in the clinical data set. The performance of the algorithm showed sensitivity of 79.5% (95% CI 68.8– 87.8%), positive predictive value (PPV) of 79.5% (95% CI 68.8– 87.8%), and accuracy of 96.7% (95% CI 95.4– 97.7%). The external validation resulted in a sensitivity of 84.1% (95% CI 74.4– 91.3%), PPV of 84.1% (95% CI 74.4– 91.3%), and an accuracy of 96.8% (95% CI 95.4– 97.9%).Conclusion: Our algorithm detected distant breast cancer recurrences with an overall good accuracy of 96.8% for patients with breast cancer, as observed in the first multi-centric external validation exercise.Keywords: machine learning, breast cancer, distant metastases, recurrences, algorithm, administrative dat

Letrozole and palbociclib versus chemotherapy as neoadjuvant therapy of high-risk luminal breast cancer

International audiencePalbociclib is a CDK4/6 inhibitor with demonstrated efficacy and safety in combination with endocrine therapy in advanced luminal breast cancer (LBC). We evaluated the respective efficacy and safety of chemotherapy and letrozole-palbociclib (LETPAL) combination as neoadjuvant treatment in patients with high-risk LBC