Reshaping the Tumor Microenvironment of KRASG12D Pancreatic Ductal Adenocarcinoma with combined SOS1 and MEK Inhibition for Improved Immunotherapy Response.

KRAS inhibitors have demonstrated exciting pre-clinical and clinical responses, although resistance occurs rapidly. Here, we investigate the effects of KRAS-targeting therapies on the tumor microenvironment using a library of KRASG12D, p53 mutant, murine PDAC-derived cell lines (KPCY) to leverage immune-oncology combination strategies for long-term tumor efficacy. Our findings show that SOS1 and MEK inhibitors (SOS1i+MEKi) suppressed tumor growth in syngeneic models and increased intra-tumoral CD8+ T cells without durable responses. scRNA-sequencing revealed an increase in inflammatory cancer associated fibroblasts (iCAFs), M2 macrophages, and a decreased dendritic cell quality that ultimately resulted in a highly immunosuppressive microenvironment driven by IL6+ iCAFs. Agonist CD40 treatment was effective to revert macrophage polarization and overcome the lack of mature antigen presenting DCs after SOS1i+MEKi therapy. Treatment increased the overall survival of KPCY tumor-bearing mice. The addition of checkpoint blockade to SOS1i+MEKi combination resulted in tumor free mice with established immune memory. Our data suggests that KRAS inhibition affects myeloid cell maturation and highlights the need for combining KRAS cancer-targeted therapy with myeloid activation to enhance and prolong anti-tumor effects.

Circulating tumor DNA reveals complex biological features with clinical relevance in metastatic breast cancer.

Liquid biopsy has proven valuable in identifying individual genetic alterations; however, the ability of plasma ctDNA to capture complex tumor phenotypes with clinical value is unknown. To address this question, we have performed 0.5X shallow whole-genome sequencing in plasma from 459 patients with metastatic breast cancer, including 245 patients treated with endocrine therapy and a CDK4/6 inhibitor (ET + CDK4/6i) from 2 independent cohorts. We demonstrate that machine learning multi-gene signatures, obtained from ctDNA, identify complex biological features, including measures of tumor proliferation and estrogen receptor signaling, similar to what is accomplished using direct tumor tissue DNA or RNA profiling. More importantly, 4 DNA-based subtypes, and a ctDNA-based genomic signature tracking retinoblastoma loss-of-heterozygosity, are significantly associated with poor response and survival outcome following ET + CDK4/6i, independently of plasma tumor fraction. Our approach opens opportunities for the discovery of additional multi-feature genomic predictors coming from ctDNA in breast cancer and other cancer-types.

Multiomics in primary and metastatic breast tumors from the AURORA US network finds microenvironment and epigenetic drivers of metastasis.

The AURORA US Metastasis Project was established with the goal to identify molecular features associated with metastasis. We assayed 55 females with metastatic breast cancer (51 primary cancers and 102 metastases) by RNA sequencing, tumor/germline DNA exome and low-pass whole-genome sequencing and global DNA methylation microarrays. Expression subtype changes were observed in ~30% of samples and were coincident with DNA clonality shifts, especially involving HER2. Downregulation of estrogen receptor (ER)-mediated cell-cell adhesion genes through DNA methylation mechanisms was observed in metastases. Microenvironment differences varied according to tumor subtype; the ER+/luminal subtype had lower fibroblast and endothelial content, while triple-negative breast cancer/basal metastases showed a decrease in B and T cells. In 17% of metastases, DNA hypermethylation and/or focal deletions were identified near HLA-A and were associated with reduced expression and lower immune cell infiltrates, especially in brain and liver metastases. These findings could have implications for treating individuals with metastatic breast cancer with immune- and HER2-targeting therapies.

Integrated DNA and RNA Sequencing Reveals Drivers of Endocrine Resistance in Estrogen Receptor-Positive Breast Cancer.

PURPOSE: Endocrine therapy resistance (ETR) remains the greatest challenge in treating patients with hormone receptor-positive breast cancer. We set out to identify molecular mechanisms underlying ETR through in-depth genomic analysis of breast tumors. EXPERIMENTAL DESIGN: We collected pre-treatment and sequential on-treatment tumor samples from 35 patients with estrogen receptor-positive breast cancer treated with neoadjuvant then adjuvant endocrine therapy; 3 had intrinsic resistance, 19 acquired resistance, and 13 remained sensitive. Response was determined by changes in tumor volume neoadjuvantly and by monitoring for adjuvant recurrence. Twelve patients received two or more lines of endocrine therapy, with subsequent treatment lines being initiated at the time of development of resistance to the previous endocrine therapy. DNA whole-exome sequencing and RNA sequencing were performed on all samples, totalling 169 unique specimens. DNA mutations, copy-number alterations, and gene expression data were analyzed through unsupervised and supervised analyses to identify molecular features related to ETR. RESULTS: Mutations enriched in ETR included ESR1 and GATA3. The known ESR1 D538G variant conferring ETR was identified, as was a rarer E380Q variant that confers endocrine hypersensitivity. Resistant tumors which acquired resistance had distinct gene expression profiles compared with paired sensitive tumors, showing elevated pathways including ER, HER2, GATA3, AKT, RAS, and p63 signaling. Integrated analysis in individual patients highlighted the diversity of ETR mechanisms. CONCLUSIONS: The mechanisms underlying ETR are multiple and characterized by diverse changes in both somatic genetic and transcriptomic profiles; to overcome resistance will require an individualized approach utilizing genomic and genetic biomarkers and drugs tailored to each patient.

Molecular analysis of TCGA breast cancer histologic types.

Breast cancer is classified into multiple distinct histologic types, and many of the rarer types have limited characterization. Here, we extend The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) dataset with additional histologic type annotations, in a total of 1063 breast cancers. We analyze this extended dataset to define transcriptomic and genomic profiles of six rare special histologic types: cribriform, micropapillary, mucinous, papillary, metaplastic, and invasive carcinoma with medullary pattern. We show the broader applicability of our constructed special histologic type gene signatures in the TCGA Pan-Cancer Atlas dataset with a predictive model that detects mucinous histologic type across cancers of other organ systems. Using a normal mammary cell differentiation score analysis, we order histologic types into a continuum from stem cell-like to luminal progenitor-like to mature luminal-like. Finally, we classify TCGA-BRCA into 12 consensus groups based on integrated genomic and histological features. We present a rich openly accessible resource of histologic and genomic characterization of TCGA-BRCA to enable studies of the range of breast cancers.

Genetic determinants of the molecular portraits of epithelial cancers.

The ability to characterize and predict tumor phenotypes is crucial to precision medicine. In this study, we present an integrative computational approach using a genome-wide association analysis and an Elastic Net prediction method to analyze the relationship between DNA copy number alterations and an archive of gene expression signatures. Across breast cancers, we are able to quantitatively predict many gene signatures levels within individual tumors with high accuracy based upon DNA copy number features alone, including proliferation status and Estrogen-signaling pathway activity. We can also predict many other key phenotypes, including intrinsic molecular subtypes, estrogen receptor status, and TP53 mutation. This approach is also applied to TCGA Pan-Cancer, which identify repeatedly predictable signatures across tumor types including immune features in lung squamous and basal-like breast cancers. These Elastic Net DNA predictors could also be called from DNA-based gene panels, thus facilitating their use as biomarkers to guide therapeutic decision making.

Biologically Targeted Photo-Crosslinkable Nanopatch to Prevent Postsurgical Peritoneal Adhesion.

Peritoneal adhesion occurs in a majority of patients following abdominal surgery and can result in significant side effects and complications. Current strategies to minimize adhesions involve the use of nontargeted anatomical barriers that are either inefficient in protecting injured areas or lacking the adequate residence time to prevent adhesions. Herein, the development of a biologically targeted photo-crosslinkable nanopatch (pCNP) is reported that can prevent postsurgical adhesion. It is demonstrated that pCNP can form a compact protective barrier over surfaces with exposed collagen IV. Using a rat parietal peritoneal excision adhesion model, it is showed that pCNP is highly effective and safe in preventing postsurgical adhesions. This work presents a novel approach to preventing peritoneal adhesion with nanomaterials.