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Abstract Triple-negative breast cancers (TNBCs) are a subset of breast cancers that have remained difficult to treat. A proportion of TNBCs arising in non-carriers of BRCA pathogenic variants have genomic features that are similar to BRCA carriers and may also benefit from PARP inhibitor treatment. Using genomic data from 129 TNBC samples from the Malaysian Breast Cancer (MyBrCa) cohort, we developed a gene expression-based machine learning classifier for homologous recombination deficiency (HRD) in TNBCs. The classifier identified samples with HRD mutational signature at an AUROC of 0.93 in MyBrCa validation datasets and 0.84 in TCGA TNBCs. Additionally, the classifier strongly segregated HRD-associated genomic features in TNBCs from TCGA, METABRIC, and ICGC. Thus, our gene expression classifier may identify triple-negative breast cancer patients with homologous recombination deficiency, suggesting an alternative method to identify individuals who may benefit from treatment with PARP inhibitors or platinum chemotherapy.

BackgroundMachine learning offers quantitative pattern recognition analysis of wearable device data and has the potential to detect illness onset and monitor influenza-like illness (ILI) in patients who are infected. ObjectiveThis study aims to evaluate the ability of machine-learning algorithms to distinguish between participants who are influenza positive and influenza negative in a cohort of symptomatic patients with ILI using wearable sensor (activity) data and self-reported symptom data during the latent and early symptomatic periods of ILI. MethodsThis prospective observational cohort study used the extreme gradient boosting (XGBoost) classifier to determine whether a participant was influenza positive or negative based on 3 models using symptom-only data, activity-only data, and combined symptom and activity data. Data were collected from the Home Testing of Respiratory Illness (HTRI) study and FluStudy2020, both conducted between December 2019 and October 2020. The model was developed using the FluStudy2020 data and tested on the HTRI data. Analyses included participants in these studies with an at-home influenza diagnostic test result. Fitbit (Google LLC) devices were used to measure participants’ steps, heart rate, and sleep parameters. Participants detailed their ILI symptoms, health care–seeking behaviors, and quality of life. Model performance was assessed by area under the curve (AUC), balanced accuracy, recall (sensitivity), specificity, precision (positive predictive value), negative predictive value, and weighted harmonic mean of precision and recall (F2) score. ResultsAn influenza diagnostic test result was available for 953 and 925 participants in HTRI and FluStudy2020, respectively, of whom 848 (89%) and 840 (90.8%) had activity data. For the training and validation sets, the highest performing model was trained on the combined symptom and activity data (training AUC=0.77; validation AUC=0.74) versus symptom-only (training AUC=0.73; validation AUC=0.72) and activity-only (training AUC=0.68; validation AUC=0.65) data. For the FluStudy2020 test set, the performance of the model trained on combined symptom and activity data was closely aligned with that of the symptom-only model (combined symptom and activity test AUC=0.74; symptom-only test AUC=0.74). These results were validated using independent HTRI data (combined symptom and activity evaluation AUC=0.75; symptom-only evaluation AUC=0.74). The top features guiding influenza detection were cough; mean resting heart rate during main sleep; fever; total minutes in bed for the combined model; and fever, cough, and sore throat for the symptom-only model. ConclusionsMachine-learning algorithms had moderate accuracy in detecting influenza, suggesting that previous findings from research-grade sensors tested in highly controlled experimental settings may not easily translate to scalable commercial-grade sensors. In the future, more advanced wearable sensors may improve their performance in the early detection and discrimination of viral respiratory infections.

Abstract Rare protein-truncating variants (PTVs) in PALB2 confer increased risk to breast cancer, but relatively few studies have reported the characteristics of tumours with PALB2 PTVs. In this study, we describe molecular characteristics of tumours with either germline or somatic alterations in PALB2. DNA from fresh frozen tumour tissues and matched peripheral blood lymphocytes for 560 breast cancer patients was subjected for whole-exome sequencing (WES), and RNA from tumour tissues was subjected to RNA sequencing (RNA-seq). We found six cases with germline and three with somatic protein-truncating variants in PALB2. The characteristics of tumours in patients with PALB2 PTVs were similar to those with BRCA1 and BRCA2 PTVs, having significantly more somatic alterations, and a high proportion of the mutational signature and genomic scar scores characteristic of deficiencies in homologous recombination (HR), compared to tumours arising in non-carriers. Unlike tumours arising in patients with BRCA1 and BRCA2 PTVs, PALB2 tumours did not have high prevalence of TP53 somatic alterations or an enriched immune microenvironment. In summary, PALB2 tumours show the homologous recombination deficiencies characteristic of BRCA1 and BRCA2 tumours, and highlight the potential clinical relevance of PALB2 mutational status in guiding therapeutic choices.

Molecular profiling of breast cancer in non-Caucasian populations remains underexplored. Here the authors report a high prevalence of HER2-subtypes and enriched immune score with improved survival and higher rates of TP53 somatic mutations with poorer survival in ER+ tumours in a Malaysian cohort.