Spatial molecular profiling of mixed invasive ductal and lobular breast cancers reveals heterogeneity in intrinsic molecular subtypes, oncogenic signatures, and mutations.

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity - e.g., MDLC with triple-negative breast cancer (TNBC) or basal ductal and estrogen receptor positive (ER+) luminal lobular regions, distinct enrichment of cell cycle arrest/senescence and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular but not ductal regions, and single-cell ductal and lobular subpopulations with unique oncogenic signatures further highlighting intraregional heterogeneity. Altogether, we demonstrated that the intratumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma.

FOXK2 amplification promotes breast cancer development and chemoresistance.

Oncogene activation through DNA amplification or overexpression is a crucial driver of cancer initiation and progression. The FOXK2 gene, located on chromosome 17q25, encodes a transcription factor with a forkhead DNA-binding domain. Analysis of genomic datasets reveals that FOXK2 is frequently amplified and overexpressed in breast cancer, correlating with poor patient survival. Knockdown of FOXK2 significantly inhibited breast cancer cell proliferation, migration, anchorage-independent growth, and delayed tumor growth in a xenograft mouse model. Additionally, inhibiting FOXK2 sensitized breast cancer cells to chemotherapy. Co-overexpression of FOXK2 and mutant PI3KCA transformed non-tumorigenic MCF-10A cells, suggesting a role for FOXK2 in PI3KCA-driven tumorigenesis. CCNE2, PDK1, and ESR1 were identified as transcriptional targets of FOXK2 in MCF-7 cells. Small-molecule inhibitors of CCNE2/CDK2 (dinaciclib) and PDK1 (dichloroacetate) exhibited synergistic anti-tumor effects with PI3KCA inhibitor (alpelisib) in vitro. Inhibition of FOXK2 by dinaciclib synergistically enhanced the anti-tumor effects of alpelisib in a xenograft mouse model. Collectively, these findings highlight the oncogenic function of FOXK2 and suggest that FOXK2 and its downstream genes represent potential therapeutic targets in breast cancer.

Spatial molecular profiling of mixed invasive ductal-lobular breast cancers reveals heterogeneity in intrinsic molecular subtypes, oncogenic signatures, and mutations.

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially-resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity (e.g., MDLC with TNBC/basal ductal and ER+/luminal lobular regions), distinct enrichment of senescence/dormancy and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular, but not ductal regions, and single-cell ductal and lobular sub-populations with unique oncogenic signatures further highlighting intra-regional heterogeneity. Altogether, we demonstrated that the intra-tumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma. Significance: MDLC displays both ductal and lobular tumor regions. Our multi-omic profiling approach revealed that these morphologically distinct tumor regions harbor distinct intrinsic subtypes and oncogenic features that may cause prognostic uncertainty and therapeutic dilemma. Thus histopathological/molecular profiling of individual tumor regions may guide clinical decision making and benefit patients with MDLC, particularly in the advanced setting where there is increased reliance on next generation sequencing.

RhoA downregulation in the murine intestinal epithelium results in chronic Wnt activation and increased tumorigenesis.

Rho GTPases are molecular switches regulating multiple cellular processes. To investigate the role of RhoA in normal intestinal physiology, we used a conditional mouse model overexpressing a dominant negative RhoA mutant (RhoAT19N) in the intestinal epithelium. Although RhoA inhibition did not cause an overt phenotype, increased levels of nuclear ß-catenin were observed in the small intestinal epithelium of RhoAT19N mice, and the overexpression of multiple Wnt target genes revealed a chronic activation of Wnt signaling. Elevated Wnt signaling in RhoAT19N mice and intestinal organoids did not affect the proliferation of intestinal epithelial cells but significantly interfered with their differentiation. Importantly, 17-month-old RhoAT19N mice showed a significant increase in the number of spontaneous intestinal tumors. Altogether, our results indicate that RhoA regulates the differentiation of intestinal epithelial cells and inhibits tumor initiation, likely through the control of Wnt signaling, a key regulator of proliferation and differentiation in the intestine.

Genomic and epigenomic basis of breast invasive lobular carcinomas lacking CDH1 genetic alterations.

CDH1 (E-cadherin) bi-allelic inactivation is the hallmark alteration of breast invasive lobular carcinoma (ILC), resulting in its discohesive phenotype. A subset of ILCs, however, lack CDH1 genetic/epigenetic inactivation, and their genetic underpinning is unknown. Through clinical targeted sequencing data reanalysis of 364 primary ILCs, we identified 25 ILCs lacking CDH1 bi-allelic genetic alterations. CDH1 promoter methylation was frequent (63%) in these cases. Targeted sequencing reanalysis revealed 3 ILCs harboring AXIN2 deleterious fusions (n = 2) or loss-of-function mutation (n = 1). Whole-genome sequencing of 3 cases lacking bi-allelic CDH1 genetic/epigenetic inactivation confirmed the AXIN2 mutation and no other cell-cell adhesion genetic alterations but revealed a new CTNND1 (p120) deleterious fusion. AXIN2 knock-out in MCF7 cells resulted in lobular-like features, including increased cellular migration and resistance to anoikis. Taken together, ILCs lacking CDH1 genetic/epigenetic alterations are driven by inactivating alterations in other cell adhesion genes (CTNND1 or AXIN2), endorsing a convergent phenotype in ILC.

A Genomics-Driven Artificial Intelligence-Based Model Classifies Breast Invasive Lobular Carcinoma and Discovers CDH1 Inactivating Mechanisms.

Artificial intelligence (AI)-systems can improve cancer diagnosis, yet their development often relies on subjective histological features as ground truth for training. Here, we developed an AI-model applied to histological whole-slide images (WSIs) using CDH1 bi-allelic mutations, pathognomonic for invasive lobular carcinoma (ILC) in breast neoplasms, as ground truth. The model accurately predicted CDH1 bi-allelic mutations (accuracy=0.95) and diagnosed ILC (accuracy=0.96). A total of 74% of samples classified by the AI-model as having CDH1 bi-allelic mutations but lacking these alterations displayed alternative CDH1 inactivating mechanisms, including a deleterious CDH1 fusion gene and non-coding CDH1 genetic alterations. Analysis of internal and external validation cohorts demonstrated 0.95 and 0.89 accuracy for ILC diagnosis, respectively. The latent features of the AI-model correlated with human-explainable histopathologic features. Taken together, this study reports the construction of an AI-algorithm trained using a genetic rather than histologic ground truth that can robustly classify ILCs and uncover CDH1 inactivating mechanisms, providing the basis for orthogonal ground truth utilization for development of diagnostic AI-models applied to WSI.