Biomedical subjects

Kim Blenman

Publications and source records attributed to Kim Blenman.

3 recordsLinked to original sources

PubMed2026

Blood-based proteomic profiling reveals context-dependent changes in BCL2-associated signaling during taxane therapy in breast cancer patients.

The quality of life for many cancer survivors is compromised due to severe, long-lasting side effects of chemotherapy. As part of a pilot, prospective, non-interventional study to examine the side effects of chemotherapy in breast cancer patients, we examined the change in protein expression in blood collected from patients before and after treatment with taxanes for 12 weeks. Protein expression was measured with reverse phase proteomic arrays (RPPA), which revealed divergent changes in apoptosis, senescence, and calcium signaling-related proteins depending on treatment setting (neoadjuvant vs. adjuvant). The largest change identified was BCL2 (B-cell lymphoma 2), a founding member of the BCL2 family of proteins that regulate apoptosis. Other proteins regulated by BCL2, including RB1 (retinoblastoma protein 1) and NLRP3 (NLR family pyrin domain containing 3) changed significantly over the course of treatment. These differences are consistent with intracellular calcium signaling dysregulation and activation of stress-response pathways that overlap with senescent-associated secretory phenotype (SASP)-like signaling, which has been implicated in cancer recurrence. To contextualize these observations, we generated Kaplan-Meier survival curves using publicly available proteomics data from The Cancer Proteome Atlas (TCPA). This work aims to demonstrate how blood-based proteomics can serve as a non-invasive method to monitor systemic physiological shifts during cancer therapy, offering a framework for generating hypotheses about chemotherapy timing and long-term outcomes.

Humans↗

PubMed2025

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. Among HER2-negative patients, a transformer-based model trained using a signature composed of estrogen-regulated genes, proliferation, apoptosis, and interferon response genes predicted pCR with an AUC of 0.794, outperforming models based on clinical features alone (AUC 0.704, p = 0.001), pathologist TIL assessment, and a model trained directly to predict response from I-SPY2 cases. Tertiles of this signature stratify patients into clinically relevant groups with increasing likelihood of complete response, with pCR rates ≥50% in the top tertile regardless of treatment or hormone receptor status. Additional transformer-based signature models predicted response to specific therapies (but not chemotherapy alone), including a HER2 signaling signature in IO-treated patients, and a claudin-low signature in bevacizumab treated patients. In HER2- cohorts with available gene expression data and histology, models trained on expression data performed similarly to digital histology predictions, but the combination of gene expression and histology outperformed histology alone. These findings suggest that histology-based GES provides additive information to RNA sequencing data and can inform precision treatment selection across breast cancer subtypes.

Journal Article↗

PubMed2025

Dynamic clustering of genomics cohorts beyond race, ethnicity-and ancestry.

BACKGROUND: Recent decades have witnessed a steady decrease in the use of race categories in genomic studies. While studies that still include race categories vary in goal and type, these categories already build on a history during which racial color lines have been enforced and adjusted in the service of social and political systems of power and disenfranchisement. For early modern classification systems, data collection was also considerably arbitrary and limited. Fixed, discrete classifications have limited the study of human genomic variation and disrupted widely spread genetic and phenotypic continuums across geographic scales. Relatedly, the use of broad and predefined classification schemes-e.g. continent-based-across traits can risk missing important trait-specific genomic signals. METHODS: To address these issues, we introduce a dynamic approach to clustering human genomics cohorts based on genomic variation in trait-specific loci and without using a set of predefined categories. We tested the approach on whole-exome sequencing datasets in ten cancer types and partitioned them based on germline variants in cancer-relevant genes that could confer cancer type-specific disease predisposition. RESULTS: Results demonstrate clustering patterns that transcend discrete continent-based categories across cancer types. Functional analysis based on cancer type-specific clusterings also captures the fundamental biological processes underlying cancer, differentiates between dynamic clusters on a functional level, and identifies novel potential drivers overlooked by a predefined continent-based clustering. CONCLUSIONS: Through a trait-based lens, the dynamic clustering approach reveals genomic patterns that transcend predefined classification categories. We propose that coupled with diverse data collection, new clustering approaches have the potential to draw a more complete portrait of genomic variation and to address, in parallel, technical and social aspects of its study.

Humans↗