Biology subjects

Makokha, F.

Publications and source records attributed to Makokha, F..

3 recordsLinked to original sources

bioRxiv2025

Mutational and Expression Profile of ZNF217, ZNF750, ZNF703 Zinc Finger Genes in Kenya Women diagnosed with Breast Cancer

ObjectiveTo characterize the mutational landscape and expression profiles of ZNF217, ZNF703, and ZNF750, and assess their clinical relevance in breast cancer patients from Kenya. MethodsWhole-exome sequencing (WES) and RNA sequencing (RNA-Seq) data from 23 paired tumor-normal samples were analyzed in a Linux-based environment. Somatic mutations were identified using MuTect2 following alignment to the hg38 reference genome and annotation with VEP. Variants were classified by type, coding consequence, and protein position, and mapped to functional domains. Recurrent mutations were identified, and comparisons were made with The Cancer Genome Atlas (TCGA). Gene expression was quantified using STAR and featureCounts, normalized with DESeq2, and analyzed using paired statistical tests with multiple testing correction. Principal component analysis (PCA) and regression analyses were performed to assess expression patterns and clinical associations. ResultsZNF217 and ZNF750 exhibited high mutational burdens, whereas ZNF703 showed a lower mutation frequency. Mutations were predominantly single nucleotide variants, with missense and synonymous variants as the major classes. Variants were distributed across protein sequences, with limited domain enrichment and no clear hotspot clustering. Recurrent mutations were gene-specific and infrequent. Comparison with TCGA data showed concordant mutation prevalence for ZNF217, low frequency for ZNF703, and absence of ZNF750 mutations. All three genes were significantly upregulated in tumors compared to matched normal tissues (ZNF217: p = 0.00068; ZNF703: p = 0.00475; ZNF750: p = 0.00366). Tumor expression exceeded normal expression in 74% of cases for ZNF217, 64% for ZNF703, and 83% for ZNF750. PCA demonstrated partial separation between tumor and normal samples. ZNF703 expression was positively associated with body mass index ({beta} = 0.194, p = 0.025), and ZNF750 expression was higher in estrogen receptor-positive tumors ({beta} = 1.050, p = 0.005). ConclusionZNF217, ZNF703, and ZNF750 display distinct mutation and expression profiles in breast cancer, with evidence of cohort-specific variation. These findings highlight gene-specific mechanisms of dysregulation and emphasize the value of integrating genomic and transcriptomic analyses.

cancer biology↗

bioRxiv2025

Single-cell-guided identification of logic-gated antigen combinations for designing effective and safe CAR therapy

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment of hematological malignancies. However, its application in solid tumors remains limited because single targets are unlikely to suffice due to tumor antigen heterogeneity and off-tumor toxicities. To overcome these obstacles, we developed LogiCAR designer, a computational approach that utilizes single-cell transcriptomics data from patient tumors to systematically identify the cancer-specific antigen circuits with logic gates ("AND," "OR," and "NOT") that target the majority of cancer cells in a tumor while sparing normal cells and tissues as much as possible. LogiCAR designer efficiently scales to higher-order antigen combinations involving up to five genes. Applied to a large-scale dataset encompassing approximately 2 million cells (including > 620k tumor cells) from 342 clinical patient samples across all major breast cancer subtypes, LogiCAR designer identified antigen circuits with enhanced tumor-targeting efficacy and improved safety profiles compared to both previously reported circuits and single-target therapies in clinical trials. However, even these optimized shared circuits still proved insufficient for some patients. We hence systematically studied LogiCAR designers ability to identify highly effective CAR circuits that are individualized to each patient. Remarkably, such personalized CAR circuits provide estimated tumor-targeting efficacy tantamount to complete response in 76% of patients and partial response for all patients. Taken together, this analysis is the first systematic quantification of the efficacy and safety of all possible CAR circuits, showing that: (a) the quality of existing solutions leaves much to be desired; (b) the ability of shared circuits optimized across many patients is moderate, and finally, (c) individually tailored circuits offer significantly higher tumor-targeting efficacies for patients. LogiCAR designer offers a rigorous, data-driven way to facilitate the rational design of safe and effective CAR-based immunotherapies for cancer. Statement of Significance Development of a computational approach that efficiently identifies logic-gated CAR target combinations, called circuits, from single-cell transcriptomics, addressing a critical unmet clinical need. Application to the largest ensemble of breast cancer datasets to date, comprising [~]2 million cells (> 620k tumor cells) from 17 clinical cohorts, to identify CAR circuits predicted to be effective. Comprehensive safety profiling of candidate circuits spanning major tissues at both RNA and protein levels. Logic-gated CAR circuits generated by our pipeline address tumor heterogeneity and achieve efficacy and safety scores that surpass clinical trial and previously computationally identified circuits. Individualized rational CAR design offers a transformative approach to deliver precision-engineered CAR therapies with unprecedented efficacy.

bioinformatics↗

bioRxiv2024

The Association between the JAK-STAT Pathway and Hypertension among Kenyan Women Diagnosed with Breast Cancer

BackgroundBreast cancer is the most common malignant tumor in women worldwide, and disproportionately affects Sub-Saharan Africa compared to high income countries. The global disease burden is growing, with Sub-Saharan Africa reporting majority of the cases. In Kenya, breast cancer is the most commonly diagnosed cancer, with an annual incidence of 7,243 new cases in 2022, representing 25.5% of all reported cancers in women. Evidence suggests that women receiving breast cancer treatment are at a greater risk of developing hypertension than women without breast cancer. Hypertension prevalence has been on the rise in SSA, with poor detection, treatment and control. The JAK-STAT signaling is activated in hormone receptor-positive breast tumors, leading to inflammation, cell proliferation, and treatment resistance in cancer cells. We sought to understand the association between the expression of JAK-STAT Pathway genes and hypertension among Kenyan women diagnosed with breast cancer. MethodsBreast tumor and non-tumor tissues were acquired from patients with a pathologic diagnosis of invasive breast carcinoma. RNA was extracted from fresh frozen tumor and adjacent normal tissue samples of 23 participants who had at least 50% tumor after pathological examination, as well as their corresponding adjacent normal samples. Differentially expressed JAK-STAT genes between tumor and normal breast tissues were assessed using the DESEq2 R package. Pearson correlation was used to assess the correlation between differentially expressed JAK-STAT genes and participants blood pressure, heart rate, and body mass index (BMI). Results11,868 genes were differentially expressed between breast tumor and non-tumor tissues. Eight JAK-STAT genes were significantly dysregulated (Log2FC [≥] 1.0 and an Padj [≤] 0.05), with two genes (CISH and SCNN1A) being upregulated. Six genes (TGFBR2, STAT5A, STAT5B, TGFRB3, SMAD9, and SOCS2) were downregulated. We identified STAT5A and SOCS2 genes to be significantly correlated with elevated systolic pressure and heart rate, respectively. ConclusionsOur study provides insights underlying the molecular mechanisms of hypertension among Kenyan women diagnosed with breast cancer. Understanding these mechanisms may help develop targeted treatments that may improve health outcomes of Kenyan women diagnosed with breast cancer. Longitudinal studies with larger cohorts will be needed to validate our results.

cancer biology↗