TROPION-Breast02: Datopotamab deruxtecan for locally recurrent inoperable or metastatic triple-negative breast cancer.

Despite recent treatment advances, the prognosis for patients with locally recurrent inoperable or metastatic triple-negative breast cancer (TNBC) remains poor. The antibody-drug conjugate datopotamab deruxtecan (Dato-DXd) is composed of a humanized anti-TROP2 IgG1 monoclonal antibody linked to a topoisomerase I inhibitor payload via a stable, cleavable linker. The phase III TROPION-Breast02 trial in patients previously untreated for locally recurrent inoperable or metastatic TNBC, who are not candidates for PD-1/PD-L1 inhibitors is evaluating efficacy and safety of Dato-DXd versus investigator's choice of chemotherapy (ICC). Approximately 600 patients will be randomized 1:1 to Dato-DXd 6 mg/kg iv. every 3 weeks or ICC (paclitaxel, nab-paclitaxel, carboplatin, capecitabine or eribulin mesylate). Dual primary end points are progression-free survival by blinded independent central review and overall survival.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that is hard to treat. Tumors lack receptors for estrogen and progesterone, which means that standard endocrine therapy is ineffective, and it does not express HER2, so HER2 therapies are also not appropriate. However, the majority of TNBC tumors do possess a cell surface protein called TROP2 which provides a way of directing treatment inside tumor cells that is more selective than traditional chemotherapy. Datopotamab deruxtecan (Dato-DXd) is a drug that consists of two parts: datopotamab (an antibody) and DXd (the cancer-cell killing toxic component), which are joined via a stable linker. Datopotamab binds to the TROP2 protein found on TNBC tumors and is taken into the cell. The linker is then broken and releases DXd, which kills the tumor cell. By binding to cancer cells before releasing the payload, treatment is directed to the tumor, minimizing side effects in the rest of the body. The TROPION-Breast02 study aims to discover whether Dato-DXd is more effective than standard-of-care chemotherapy, allowing patients with TNBC to live longer without their breast cancer getting worse. This study is also looking at how Dato-DXd may affect patients' overall functioning and quality of life. TROPION-Breast02 will recruit approximately 600 patients who: Have cancer that has spread from the original site (metastatic), or cancer that returned to the same site (locally recurrent) that cannot be surgically removed Have not received any prior treatment for this stage of cancer Cannot receive an alternative type of anticancer treatment called PD-(L)1 inhibitors Had any length of time between their last treatment with the aim of cure and return of their disease Eligible patients will be randomly assigned to a treatment group in equal numbers to either Dato-DXd or an appropriate chemotherapy (one of five available options, chosen by the treating doctor). Each patient will generally continue to receive their designated treatments if the tumor is controlled by the drug, there are no unacceptable side effects, or the patient chooses to stop treatment. Clinical Trial Registration: NCT05374512 (ClinicalTrials.gov).

A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application.

BACKGROUND: Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed for the identification of various malignancies. Initially, a series of preprocessing steps and image segmentation steps are performed to extract region of interest features from noisy features. Then, the extracted features are applied to several machine learning and deep learning methods for the detection of cancer. METHODS: In this work, a review of all the methods that have been applied to develop machine learning algorithms that detect cancer is provided. With more than 100 types of cancer, this study only examines research on the four most common and prevalent cancers worldwide: lung, breast, prostate, and colorectal cancer. Next, by using state-of-the-art sentence transformers namely: SBERT (2019) and the unsupervised SimCSE (2021), this study proposes a new methodology for detecting cancer. This method requires raw DNA sequences of matched tumor/normal pair as the only input. The learnt DNA representations retrieved from SBERT and SimCSE will then be sent to machine learning algorithms (XGBoost, Random Forest, LightGBM, and CNNs) for classification. As far as we are aware, SBERT and SimCSE transformers have not been applied to represent DNA sequences in cancer detection settings. RESULTS: The XGBoost model, which had the highest overall accuracy of 73 ± 0.13 % using SBERT embeddings and 75 ± 0.12 % using SimCSE embeddings, was the best performing classifier. In light of these findings, it can be concluded that incorporating sentence representations from SimCSE's sentence transformer only marginally improved the performance of machine learning models.

Discriminatory Gleason grade group signatures of prostate cancer: An application of machine learning methods.

One of the most precise methods to detect prostate cancer is by evaluation of a stained biopsy by a pathologist under a microscope. Regions of the tissue are assessed and graded according to the observed histological pattern. However, this is not only laborious, but also relies on the experience of the pathologist and tends to suffer from the lack of reproducibility of biopsy outcomes across pathologists. As a result, computational approaches are being sought and machine learning has been gaining momentum in the prediction of the Gleason grade group. To date, machine learning literature has addressed this problem by using features from magnetic resonance imaging images, whole slide images, tissue microarrays, gene expression data, and clinical features. However, there is a gap with regards to predicting the Gleason grade group using DNA sequences as the only input source to the machine learning models. In this work, using whole genome sequence data from South African prostate cancer patients, an application of machine learning and biological experiments were combined to understand the challenges that are associated with the prediction of the Gleason grade group. A series of machine learning binary classifiers (XGBoost, LSTM, GRU, LR, RF) were created only relying on DNA sequences input features. All the models were not able to adequately discriminate between the DNA sequences of the studied Gleason grade groups (Gleason grade group 1 and 5). However, the models were further evaluated in the prediction of tumor DNA sequences from matched-normal DNA sequences, given DNA sequences as the only input source. In this new problem, the models performed acceptably better than before with the XGBoost model achieving the highest accuracy of 74 ± 01, F1 score of 79 ± 01, recall of 99 ± 0.0, and precision of 66 ± 0.1.

First-line drug resistance profiling of Mycobacterium tuberculosis: a machine learning approach.

The persistence and emergence of new multi-drug resistant Mycobacterium tuberculosis (M. tb) strains continues to advance the devastating tuberculosis (TB) epidemic. Robust systems are needed to accurately and rapidly perform drug-resistance profiling, and machine learning (ML) methods combined with genomic sequence data may provide novel insights into drug-resistance mechanisms. Using 372 M. tb isolates, the combined utility of ML and bioinformatics to perform drug-resistance profiling is demonstrated. SNPs, InDels, and dinucleotide frequencies are explored as input features for three ML models, namely Decision Trees, Random Forest, and the eXtreme Gradient Boosted model. Using SNPs and InDels, all three models performed equally well yielding a 99% accuracy, 97% recall, and 99% F1-score. Using dinucleotide frequencies, the XGBoost algorithm was superior with a 97% accuracy, 94% recall and 97% F1-score. This study validates the use of variants and presents dinucleotide features as another effective feature encoding method for ML-based phenotype classification.

Classification of COVID-19 and Other Pathogenic Sequences: A Dinucleotide Frequency and Machine Learning Approach.

The world is grappling with the COVID-19 pandemic caused by the 2019 novel SARS-CoV-2. To better understand this novel virus and its relationship with other pathogens, new methods for analyzing the genome are required. In this study, intrinsic dinucleotide genomic signatures were analyzed for whole genome sequence data of eight pathogenic species, including SARS-CoV-2. The genome sequences were transformed into dinucleotide relative frequencies and classified using the extreme gradient boosting (XGBoost) model. The classification models were trained to a) distinguish between the sequences of all eight species and b) distinguish between sequences of SARS-CoV-2 that originate from different geographic regions. Our method attained 100% in all performance metrics and for all tasks in the eight-species classification problem. Moreover, the models achieved 67% balanced accuracy for the task of classifying the SARS-CoV-2 sequences into the six continental regions and achieved 86% balanced accuracy for the task of classifying SARS-CoV-2 samples as either originating from Asia or not. Analysis of the dinucleotide genomic profiles of the eight species revealed a similarity between the SARS-CoV-2 and MERS-CoV viral sequences. Further analysis of SARS-CoV-2 viral sequences from the six continents revealed that samples from Oceania had the highest frequency of TT dinucleotides as well as the lowest CG frequency compared to the other continents. The dinucleotide signatures of AC, AG,CA, CT, GA, GT, TC, and TG were well conserved across most genomes, while the frequencies of other dinucleotide signatures varied considerably. Altogether, the results from this study demonstrate the utility of dinucleotide relative frequencies for discriminating and identifying similar species.

A meta-analysis of public microarray data identifies gene regulatory pathways deregulated in peripheral blood mononuclear cells from individuals with Systemic Lupus Erythematosus compared to those without.

BACKGROUND: Systemic Lupus Erythematosus (SLE) is a complex, multi-systemic, autoimmune disease for which the underlying aetiological mechanisms are poorly understood. The genetic and molecular processes underlying lupus have been extensively investigated using a variety of -omics approaches, including genome-wide association studies, candidate gene studies and microarray experiments of differential gene expression in lupus samples compared to controls. METHODS: This study analyses a combination of existing microarray data sets to identify differentially regulated genetic pathways that are dysregulated in human peripheral blood mononuclear cells from SLE patients compared to unaffected controls. Two statistical approaches, quantile discretisation and scaling, are used to combine publicly available expression microarray datasets and perform a meta-analysis of differentially expressed genes. RESULTS: Differentially expressed genes implicated in interferon signaling were identified by the meta-analysis, in agreement with the findings of the individual studies that generated the datasets used. In contrast to the individual studies, however, the meta-analysis and subsequent pathway analysis additionally highlighted TLR signaling, oxidative phosphorylation and diapedesis and adhesion regulatory networks as being differentially regulated in peripheral blood mononuclear cells (PBMCs) from SLE patients compared to controls. CONCLUSION: Our analysis demonstrates that it is possible to derive additional information from publicly available expression data using meta-analysis techniques, which is particularly relevant to research into rare diseases where sample numbers can be limiting.

Identification and characterization of microRNAs expressed in the African malaria vector Anopheles funestus life stages using high throughput sequencing.

BACKGROUND: Over the past several years, thousands of microRNAs (miRNAs) have been identified in the genomes of various insects through cloning and sequencing or even by computational prediction. However, the number of miRNAs identified in anopheline species is low and little is known about their role. The mosquito Anopheles funestus is one of the dominant malaria vectors in Africa, which infects and kills millions of people every year. Therefore, small RNA molecules isolated from the four life stages (eggs, larvae, pupae and unfed adult females) of An. funestus were sequenced using next generation sequencing technology. RESULTS: High throughput sequencing of four replicates in combination with computational analysis identified 107 mature miRNA sequences expressed in the An. funestus mosquito. These include 20 novel miRNAs without sequence identity in any organism and eight miRNAs not previously reported in the Anopheles genus but are known in non-anopheles mosquitoes. Finally, the changes in the expression of miRNAs during the mosquito development were determined and the analysis showed that many miRNAs have stage-specific expression, and are co-transcribed and co-regulated during development. CONCLUSIONS: This study presents the first direct experimental evidence of miRNAs in An. funestus and the first profiling study of miRNA associated with the maturation in this mosquito. Overall, the results indicate that miRNAs play important roles during the growth and development. Silencing such molecules in a specific life stage could decrease the vector population and therefore interrupt malaria transmission.

Identification of phenotype-relevant differentially expressed genes in breast cancer demonstrates enhanced quantile discretization protocol's utility in multi-platform microarray data integration.

Microarray for transcriptomics experiments often suffer from limited statistical power due to small sample size. Quantile discretization (QD) maps expression values for a sample into a series of equivalently sized 'bins' that represent a discrete numerical range, e.g. [Formula: see text]4 to [Formula: see text]4, which enables normalized data from multiple experiments and/or expression platforms to be combined for re-analysis. We found, however, that informal selection of bin numbers often resulted in loss of the underlying correlation structure in the data through assigning of the same numerical value to genes that are in reality expressed at significantly different levels within a sample. Here we report a procedure for determining an optimal bin number for dataset. Applying this to integrated public breast cancer datasets enabled statistical identification of several differentially expressed tumorigenesis-related genes that were not found when analyzing the individual datasets, and also several cancer biomarkers not previously indicated as having utility in the disease. Notably, differential modulation of translational control and protein synthesis via multiple pathways were found to potentially have central roles in breast cancer development and progression. These findings suggest that our protocol has significant utility in making meaningful novel biomedical discoveries by leveraging the large public expression data repositories.

Baseline Predictors of Mortality among Predominantly Rural-Dwelling End-Stage Renal Disease Patients on Chronic Dialysis Therapies in Limpopo, South Africa.

BACKGROUND: Dialysis therapy for end-stage renal disease (ESRD) continues to be the readily available renal replacement option in developing countries. While the impact of rural/remote dwelling on mortality among dialysis patients in developed countries is known, it remains to be defined in sub-Saharan Africa. METHODS: A single-center database of end-stage renal disease patients on chronic dialysis therapies treated between 2007 and 2014 at the Polokwane Kidney and Dialysis Centre (PKDC) of the Pietersburg Provincial Hospital, Limpopo South Africa, was retrospectively reviewed. All-cause, cardiovascular, and infection-related mortalities were assessed and associated baseline predictors determined. RESULTS: Of the 340 patients reviewed, 52.1% were male, 92.9% were black Africans, 1.8% were positive for the human immunodeficiency virus (HIV), and 87.5% were rural dwellers. The average distance travelled to the dialysis centre was 112.3 ± 73.4 Km while 67.6% of patients lived in formal housing. Estimated glomerular filtration rate (eGFR) at dialysis initiation was 7.1 ± 3.7 mls/min while hemodialysis (HD) was the predominant modality offered (57.1%). Ninety-two (92) deaths were recorded over the duration of follow-up with the majority (34.8%) of deaths arising from infection-related causes. Continuous ambulatory peritoneal dialysis (CAPD) was a significant predictor of all-cause mortality (HR: 1.62, CI: 1.07-2.46) and infection-related mortality (HR: 2.27, CI: 1.13-4.60). On multivariable cox regression, CAPD remained a significant predictor of all-cause mortality (HR: 2.00, CI: 1.29-3.10) while the risk of death among CAPD patients was also significantly modified by diabetes mellitus (DM) status (HR: 4.99, CI: 2.13-11.71). CONCLUSION: CAPD among predominantly rural dwelling patients in the Limpopo province of South Africa is associated with an increased risk of death from all-causes and infection-related causes.

Lupus nephritis is associated with poor pregnancy outcomes in pregnant SLE patients in Cape Town: a retrospective analysis.

INTRODUCTION: Systemic lupus erythematosus (SLE) is a multi-system auto-immune disease common in females of child-bearing age. The effect of pregnancy on SLE and vice versa have not been well characterised in Africans. The aim of this study is to describe the pregnancy outcomes of patients with SLE presenting to the maternity department of Groote Schuur Hospital, Cape Town. METHODS: This study was designed as a retrospective review of records of pregnant women known with SLE and followed-up at the maternity section of Groote Schuur Hospital. The duration of survey was from the 1(st) January 2003 to 31(st) December 2013. RESULTS: There were 61 pregnancies reviewed in 49 patients; 80.3% of the pregnancies were in patients of mixed ancestry and the rest (19.7%) in black African patients. The mean age at presentation of the current pregnancy was 27.2 ± 5.0 years. Mean gestational age at presentation and delivery was 13.0 ± 6.0 weeks and 28.9 ± 9.8 weeks respectively and 47.5% of the pregnancies were in patients with lupus nephritis (LN). Thirty nine (63.9%) pregnancies reached the third trimester and 11.5% of all pregnancies ended in the first trimester. There was a lower number of live births to mothers of African ancestry than to those of mixed ancestry (p = 0.001). In 55.7% of the pregnancies, no flare was reported while a renal flare was reported in 23%. Pregnancies in patients with LN had higher frequencies of flares (58.6% vs 31.3%; p = 0.032), pre-eclampsia (34.5% vs 12.5%; p = 0.041), longer stay in hospital (12.0 ± 9.1 days vs 6.1 ± 5.1 days; p = 0.004) and low birth weight babies (1.94 ± 1.02 kg vs 2.55 ± 0.95 kg; p = 0.046) than in patients without LN. Only 36 (59%) of the neonates were discharged home alive and of these 2 (5.6%) were to mothers of black African ancestry (p = 0.001). CONCLUSION: Increased lupus activity in pregnant SLE patients may account for the increased deaths of neonates born to SLE mothers. Patients of black African descent and those with LN tend to have a poorer outcome. A multi-disciplinary approach to the management of SLE patients (of child-bearing age or pregnant) needs to be further assessed for better outcomes.

Continuous ambulatory peritoneal dialysis in Limpopo province, South Africa: predictors of patient and technique survival.

UNLABELLED: ♦ INTRODUCTION AND AIM: Continuous ambulatory peritoneal dialysis (CAPD) is not a frequently used modality of dialysis in many parts of Africa due to several socio-economic factors. Available studies from Africa have shown a strong association between outcome and socio-demographic variables. We sought to assess the outcome of patients treated with CAPD in Limpopo, South Africa. ♦ METHODS: This was a retrospective study of 152 patients treated with CAPD at the Polokwane Kidney and Dialysis Centre (PKDC) from 2007 to 2012. We collected relevant demographic and biochemical data for all patients included in the study. A composite outcome of death while still on peritoneal dialysis (PD) or CAPD technique failure from any cause requiring a change of modality to hemodialysis (HD) was selected. The peritonitis rate and causes of peritonitis were assessed from 2008 when all related data could be obtained. ♦ RESULTS: There were 52% males in the study and the average age of the patients was 36.8 ± 11.4 years. Unemployment rate was high (71.1%), 41.1% had tap water at home, the average distance travelled to the dialysis center was 122.9 ± 78.2 kilometres and half the patients had a total income less than USD ($)180 per month. Level of education, having electricity at home, having tap water at home, body mass index (BMI), serum albumin and hemoglobin were significantly different between those reaching the composite outcome and those not reaching it (p < 0.05). The overall peritonitis rate was 0.82/year with 1-year, 2-year and 5-year survival found to be 86.7%, 78.7% and 65.3% (patient survival) and 83.3%, 71.7% and 62.1% (technique survival). Predictors of the composite outcome were BMI (p = 0.011), serum albumin (p = 0.030), hemoglobin (p = 0.002) and more than 1 episode of peritonitis (p = 0.038). ♦ CONCLUSION: Treatment of anemia and malnutrition as well as training and re-training of CAPD patients and staff to prevent recurrence of peritonitis can have positive impacts on CAPD outcomes in this population.