Integration of Gene Expression and Digital Histology to Predict Treatment-Specific Responses in Breast Cancer.

Deep learning models applied to digital histology can predict gene expression signatures (GES) and offer a low-cost, rapidly available alternative to molecular testing at the time of diagnosis. We optimized transformer-based models to infer GES results and applied this approach to pre-treatment H&E-stained biopsies from 1,940 breast cancer patients treated with neoadjuvant chemotherapy in clinical trial and real-world cohorts. The most predictive histology-derived GES for pathologic complete response (pCR) in the I-SPY2 trial was validated in four external cohorts: CALGB 40601, CALGB 40603, a trial of durvalumab plus CT, and standard-of-care CT-treated patients from the University of Chicago.

Organoid-evaluable clinical biomarkers predict drug responses and guide new breast cancer therapies.

Poor therapeutic response in subsets of breast cancer (BC) patients poses an ongoing challenge. Here, we present a biomarker-guided characterization of 44 patient-derived BC organoids, with the aim of modeling resistant disease with greater fidelity and developing an in-vitro system grounded in clinical data for testing alternative treatment strategies. We utilized patient transcriptomic and outcome data from the I-SPY2 clinical trial to develop predictive models of response to a range of therapies, using only organoid-detectable biomarkers as input.

Acalabrutinib treatment for older (≥80 years old) and/or frail patients with CLL: primary end point analysis of the CLL-Frail trial.

Because frail and patients ≥80 years with CLL are still underrepresented in clinical trials, the CLL-Frail trial aimed to evaluate the efficacy and safety of acalabrutinib in these patients. The primary endpoint was the overall response rate (ORR) after 6 cycles of treatment to test the null hypothesis of ORR ≤ 65%.53 patients were included into the trial and 34 patients are still on therapy.

Immune and Growth Factor Signaling Pathways Are Associated with Pathologic Complete Response to an Anti-Type I Insulin-like Growth Factor Receptor Regimen in Patients with Breast Cancer.

Purpose: Pretreatment specimens from patients treated on the I-SPY2 neoadjuvant breast cancer trial were studied to identify prespecified biomarkers associated with response to the regimen of paclitaxel, the anti-type I insulin-like growth factor receptor (IGF-1R) antibody ganitumab, and metformin (PGM) followed by doxorubicin and cyclophosphamide (AC) compared with control therapy (paclitaxel followed by AC). The primary endpoint of this trial is pathologic complete response (pCR).

Experimental Design: One hundred six patients treated with PGM and 119 contemporary controls were evaluated using laser capture microdissection and reverse-phase protein array to evaluate 32 prespecified potential predictive biomarkers in the IGF-1R pathway and 109 additional exploratory endpoints.

A novel signal peptide toolbox for optimized heterologous expression of yeast pheromones in Bacillus subtilis.

Optimising downstream processes and achieving high product yields are essential for cost-effective and scalable bioproduction, with efficient protein secretion being a critical factor in industrial applications. To address this, we leveraged the widely utilised Sec secretion system of Bacillus subtilis to enhance heterologous protein secretion. Given the lack of reliable in silico tools for predicting optimal combinations of Sec system signal peptides (SPs) with a protein of interest (POI), we aimed to optimise the secretion efficiency of SP-POI pairings by a toolbox approach.