Caveolin Gene Expression Predicts Clinical Outcomes for Early-Stage HER2-Negative Breast Cancer Treated with Paclitaxel-Based Chemotherapy in the GeparSepto Trial.

Purpose: Caveolin-1 and -2 (CAV1/2) dysregulation are implicated in driving cancer progression and may predict response to nab-paclitaxel. We explored the prognostic and predictive potential of CAV1/2 expression for patients with early-stage HER2-negative breast cancer receiving neoadjuvant paclitaxel-based chemotherapy regimens, followed by epirubicin and cyclophosphamide.

Experimental Design: We correlated tumor CAV1/2 RNA expression with pathologic complete response (pCR), disease-free survival (DFS), and overall survival (OS) in the GeparSepto trial, which randomized patients to neoadjuvant paclitaxel- versus nab-paclitaxel-based chemotherapy.

CD47-blocking Antibody ZL-1201 Promotes Tumor-associated Macrophage Phagocytic Activity and Enhances the Efficacy of the Therapeutic Antibodies and Chemotherapy.

Unlabelled: Tumor-associated macrophages (TAM) are the most abundant immune cells in the tumor microenvironment. They consist of various subsets but primarily resemble the M2 macrophage phenotype. TAMs are known to promote tumor progression and are associated with poor clinical outcomes.

Large scale, robust, and accurate whole transcriptome profiling from clinical formalin-fixed paraffin-embedded samples.

Transcriptome profiling can provide information of great value in clinical decision-making, yet RNA from readily available formalin-fixed paraffin-embedded (FFPE) tissue is often too degraded for quality sequencing. To assess the clinical utility of FFPE-derived RNA, we performed ribo-deplete RNA extractions on > 3200 FFPE slide samples; 25 of these had direct FFPE vs. fresh frozen (FF) replicates, 57 were sequenced in 2 different labs, 87 underwent multiple library analyses, and 16 had direct microdissected vs.

Transcriptomic silencing as a potential mechanism of treatment resistance.

Next-generation sequencing (NGS) has not revealed all the mechanisms underlying resistance to genomically matched drugs. Here, we performed in 1417 tumors whole-exome tumor (somatic)/normal (germline) NGS and whole-transcriptome sequencing, the latter focusing on a clinically oriented 50-gene panel in order to examine transcriptomic silencing of putative driver alterations. In this large-scale study, approximately 13% of the somatic single nucleotide variants (SNVs) were unexpectedly not expressed as RNA; 23% of patients had ≥1 nonexpressed SNV.

A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival.

Background: Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal status, yet the molecular testing required to elucidate these subtypes is not routinely performed. Furthermore, when such bulk assays as RNA sequencing are performed, intratumoral heterogeneity that may affect prognosis and therapeutic decision-making can be missed.

Methods: As a more facile and readily available method for determining IMS in breast cancer, we developed a deep learning approach for approximating PAM50 intrinsic subtyping using only whole-slide images of H&E-stained breast biopsy tissue sections.