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. RESULTS: RNA sequencing data were available for 279 patients, of which 74 (26.5%) were hormone receptor (HR)-negative, thus triple-negative breast cancer (TNBC). Patients treated with nab-paclitaxel with high CAV1/2 had higher probability of obtaining a pCR [CAV1 OR, 4.92; 95% confidence interval (CI), 1.70-14.22; P = 0.003; CAV2 OR, 5.39; 95% CI, 1.76-16.47; P = 0.003] as compared with patients with high CAV1/2 treated with solvent-based paclitaxel (CAV1 OR, 0.33; 95% CI, 0.11-0.95; P = 0.040; CAV2 OR, 0.37; 95% CI, 0.12-1.13; P = 0.082). High CAV1 expression was significantly associated with worse DFS and OS in paclitaxel-treated patients (DFS HR, 2.29; 95% CI, 1.08-4.87; P = 0.030; OS HR, 4.97; 95% CI, 1.73-14.31; P = 0.003). High CAV2 was associated with worse DFS and OS in all patients (DFS HR, 2.12; 95% CI, 1.23-3.63; P = 0.006; OS HR, 2.51; 95% CI, 1.22-5.17; P = 0.013), in paclitaxel-treated patients (DFS HR, 2.47; 95% CI, 1.12-5.43; P = 0.025; OS HR, 4.24; 95% CI, 1.48-12.09; P = 0.007) and in patients with TNBC (DFS HR, 4.68; 95% CI, 1.48-14.85; P = 0.009; OS HR, 10.43; 95% CI, 1.22-89.28; P = 0.032). CONCLUSIONS: Our findings indicate high CAV1/2 expression is associated with worse DFS and OS in paclitaxel-treated patients. Conversely, in nab-paclitaxel-treated patients, high CAV1/2 expression is associated with increased pCR and no significant detriment to DFS or OS compared with low CAV1/2 expression.

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

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. CD47 on tumor cells and SIRPα on TAMs facilitate a "don't-eat-me" signal which prevents cancer cells from immune clearance. Therefore, blockade of the CD47-SIRPα interaction represents a promising strategy for tumor immunotherapy. Here, we present the results on ZL-1201, a differentiated and potent anti-CD47 antibody with improved hematologic safety profile compared with 5F9 benchmark. ZL-1201 enhanced phagocytosis in combination with standards of care (SoC) therapeutic antibodies in in vitro coculture systems using a panel of tumor models and differentiated macrophages, and these combinational effects are Fc dependent while potently enhancing M2 phagocytosis. In vivo xenograft studies showed that enhanced antitumor activities were seen in a variety of tumor models treated with ZL-1201 in combination with other therapeutic mAbs, and maximal antitumor activities were achieved in the presence of chemotherapy in addition to the combination of ZL-1201 with other mAbs. Moreover, tumor-infiltrating immune cells and cytokine analysis showed that ZL-1201 and chemotherapies remodel the tumor microenvironment, which increases antitumor immunity, leading to augmented antitumor efficacy when combined with mAbs. Significance: ZL-1201 is a novel anti-CD47 antibody that has improved hematologic safety profiles and combines with SoC, including mAbs and chemotherapies, to potently facilitate phagocytosis and antitumor efficacy.

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. macrodissected replicates. Poly-A versus ribo-depletion RNA extraction methods were compared using transcriptomes of TCGA cohort and 3116 FFPE samples. Compared to FF, FFPE transcripts coding for nuclear/cytoplasmic proteins involved in DNA packaging, replication, and protein synthesis were detected at lower rates and zinc finger family transcripts were of poorer quality. The greatest difference in extraction methods was in histone transcripts which typically lack poly-A tails. Encouragingly, the overall sequencing success rate was 81%. Exome coverage was highly concordant in direct FFPE and FF replicates, with 98% agreement in coding exon coverage and a median correlation of whole transcriptome profiles of 0.95. We provide strong rationale for clinical use of FFPE-derived RNA based on the robustness, reproducibility, and consistency of whole transcriptome profiling.

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. SNV-bearing genes consistently transcribed were TP53, PIK3CA, and KRAS; those with lower transcription rates were ALK, CSF1R, ERBB4, FLT3, GNAS, HNF1A, KDR, PDGFRA, RET, and SMO. We also determined the frequency of tumor mutations being germline, rather than somatic, in these and an additional 462 tumors with tumor/normal exomes; 33.8% of germline SNVs within the gene panel were rare (not found after filtering through variant information domains) and at risk of being falsely reported as somatic. Both the frequency of silenced variant transcription and the risk of falsely identifying germline mutations as somatic/tumor related are important phenomena. Therefore, transcriptomics is a critical adjunct to genomics when interrogating patient tumors for actionable alterations, because, without expression of the target aberrations, there will likely be therapeutic resistance.

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. This algorithm was trained on images from 443 tumors that had previously undergone PAM50 subtyping to classify small patches of the images into four major molecular subtypes-Basal-like, HER2-enriched, Luminal A, and Luminal B-as well as Basal vs. non-Basal. The algorithm was subsequently used for subtype classification of a held-out set of 222 tumors. RESULTS: This deep learning image-based classifier correctly subtyped the majority of samples in the held-out set of tumors. However, in many cases, significant heterogeneity was observed in assigned subtypes across patches from within a single whole-slide image. We performed further analysis of heterogeneity, focusing on contrasting Luminal A and Basal-like subtypes because classifications from our deep learning algorithm-similar to PAM50-are associated with significant differences in survival between these two subtypes. Patients with tumors classified as heterogeneous were found to have survival intermediate between Luminal A and Basal patients, as well as more varied levels of hormone receptor expression patterns. CONCLUSIONS: Here, we present a method for minimizing manual work required to identify cancer-rich patches among all multiscale patches in H&E-stained WSIs that can be generalized to any indication. These results suggest that advanced deep machine learning methods that use only routinely collected whole-slide images can approximate RNA-seq-based molecular tests such as PAM50 and, importantly, may increase detection of heterogeneous tumors that may require more detailed subtype analysis.