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.

Multi-omic characterization of ILC and ILC-like cell lines as part of ILC cell line encyclopedia (ICLE) defines new models to study potential biomarkers and explore therapeutic opportunities.

Invasive lobular carcinoma (ILC), the most common histological "special type", accounts for ∼10-15% of all BC diagnoses, is characterized by unique features such as E-cadherin loss/deficiency, lower grade, hormone receptor positivity, larger diffuse tumors, and specific metastatic patterns. Despite ILC being acknowledged as a disease with distinct biology that necessitates specialized and precision medicine treatments, the further exploration of its molecular alterations with the goal of discovering new treatments has been hindered due to the scarcity of well-characterized cell line models for studying this disease. To address this, we generated the ILC Cell Line Encyclopedia (ICLE), providing a comprehensive multi-omic characterization of ILC and ILC-like cell lines. Using consensus multi-omic subtyping, we confirmed luminal status of previously established ILC cell lines and uncovered additional ILC/ILC-like cell lines with luminal features for modeling ILC disease. Furthermore, most of these luminal ILC/ILC-like cell lines also showed RNA and copy number similarity to ILC patient tumors. Similarly, ILC/ILC-like cell lines also retained molecular alterations in key ILC genes at similar frequency to both primary and metastatic ILC tumors. Importantly, ILC/ILC-like cell lines recapitulated the CDH1 alteration landscape of ILC patient tumors including enrichment of truncating mutations in and biallelic inactivation of CDH1 gene. Using whole-genome optical mapping, we uncovered novel genomic-rearrangements including novel structural variations in CDH1 and functional gene fusions and characterized breast cancer specific patterns of chromothripsis in chromosomes 8, 11 and 17. In addition, we systematically analyzed aberrant DNAm events and integrative analysis with RNA expression revealed epigenetic activation of TFAP2B - an emerging biomarker of lobular disease that is preferentially expressed in lobular disease. Finally, towards the goal of identifying novel druggable vulnerabilities in ILC, we analyzed publicly available RNAi loss of function breast cancer cell line datasets and revealed numerous putative vulnerabilities cytoskeletal components, focal adhesion and PI3K/AKT pathway in ILC/ILC-like vs NST cell lines. In summary, we addressed the lack of suitable models to study E-cadherin deficient breast cancers by first collecting both established and putative ILC models, then characterizing them comprehensively to show their molecular similarity to patient tumors along with uncovering their novel multi-omic features as well as highlighting putative novel druggable vulnerabilities. Not only we expand the array of suitable E-cadherin deficient cell lines available for modelling human-ILC disease but also employ them for studying epigenetic activation of a putative lobular biomarker as well as identifying potential druggable vulnerabilities for this disease towards enabling precision medicine research for human-ILC.

WCRC-25: A novel luminal Invasive Lobular Carcinoma cell line model.

Breast cancer is categorized by the molecular and histologic presentation of the tumor, with the major histologic subtypes being No Special Type (NST) and Invasive Lobular Carcinoma (ILC). ILC are characterized by growth in a single file discohesive manner with stromal infiltration attributed to their hallmark pathognomonic loss of E-cadherin ( CDH1 ). Few ILC cell line models are available to researchers. Here we report the successful establishment and characterization of a novel ILC cell line, WCRC-25, from a metastatic pleural effusion from a postmenopausal Caucasian woman with metastatic ILC. WCRC-25 is an ER-negative luminal epithelial ILC cell line with both luminal and Her2-like features. It exhibits anchorage independent growth and haptotactic migration towards Collagen I. Sequencing revealed a CDH1 Q706* truncating mutation, together with mutations in FOXA1, CTCF, BRCA2 and TP53 , which were also seen in a series of metastatic lesions from the patient. Copy number analyses revealed amplification and deletion of genes frequently altered in ILC while optical genome mapping revealed novel structural rearrangements. RNA-seq analysis comparing the primary tumor, metastases and the cell line revealed signatures for cell cycle progression and receptor tyrosine kinase signaling. To assess targetability, we treated WCRC-25 with AZD5363 and Alpelisib confirming WCRC-25 as susceptible to PI3K/AKT signaling inhibition as predicted by our RNA sequencing analysis. In conclusion, we report WCRC-25 as a novel ILC cell line with promise as a valuable research tool to advance our understanding of ILC and its therapeutic vulnerabilities. Financial support: The work was in part supported by a Susan G Komen Leadership Grant to SO (SAC160073) and NCI R01 CA252378 (SO/AVL). AVL and SO are Komen Scholars, Hillman Foundation Fellows and supported by BCRF. This project used the UPMC Hillman Cancer Center and Tissue and Research Pathology/Pitt Biospecimen Core shared resource which is supported in part by award P30CA047904. This research was also supported in part by the University of Pittsburgh Center for Research Computing, RRID:SCR_022735, through the resources provided. Specifically, this work used the HTC cluster, which is supported by NIH award number S10OD028483. Finally, partial support was provided by the Magee-Womens Research Institute and Foundation, The Shear Family Foundation, and The Metastatic Breast Cancer Network.

A Personalized Therapeutics Approach Using an In Silico Drosophila Patient Model Reveals Optimal Chemo- and Targeted Therapy Combinations for Colorectal Cancer.

In silico models of biomolecular regulation in cancer, annotated with patient-specific gene expression data, can aid in the development of novel personalized cancer therapeutic strategies. Drosophila melanogaster is a well-established animal model that is increasingly being employed to evaluate such preclinical personalized cancer therapies. Here, we report five Boolean network models of biomolecular regulation in cells lining the Drosophila midgut epithelium and annotate them with colorectal cancer patient-specific mutation data to develop an in silico Drosophila Patient Model (DPM). We employed cell-type-specific RNA-seq gene expression data from the FlyGut-seq database to annotate and then validate these networks. Next, we developed three literature-based colorectal cancer case studies to evaluate cell fate outcomes from the model. Results obtained from analyses of the proposed DPM help: (i) elucidate cell fate evolution in colorectal tumorigenesis, (ii) validate cytotoxicity of nine FDA-approved CRC drugs, and (iii) devise optimal personalized treatment combinations. The personalized network models helped identify synergistic combinations of paclitaxel-regorafenib, paclitaxel-bortezomib, docetaxel-bortezomib, and paclitaxel-imatinib for treating different colorectal cancer patients. Follow-on therapeutic screening of six colorectal cancer patients from cBioPortal using this drug combination demonstrated a 100% increase in apoptosis and a 100% decrease in proliferation. In conclusion, this work outlines a novel roadmap for decoding colorectal tumorigenesis along with the development of personalized combinatorial therapeutics for preclinical translational studies.

Outcomes After Sentinel Lymph Node Biopsy and Radiotherapy in Older Women With Early-Stage, Estrogen Receptor-Positive Breast Cancer.

Importance: Overtreatment of early-stage breast cancer with favorable tumor biology in older patients may be harmful without affecting recurrence and survival. Guidelines that recommend deimplementation of sentinel lymph node biopsy (SLNB) (Choosing Wisely) and radiotherapy (RT) (National Comprehensive Cancer Network) have been published. Objective: To describe the use rates and association with disease recurrence of SLNB and RT in older women with breast cancer. Design, Setting, and Participants: This cohort study obtained patient and clinical data from an integrated cancer registry and electronic health record of a single health care system in Pennsylvania. The cohort was composed of consecutive female patients 70 years or older who were diagnosed with early-stage, estrogen receptor-positive, ERBB2 (formerly HER2)-negative, clinically node-negative breast cancer from January 1, 2010, to December 31, 2018, who were treated at 15 community and academic hospitals within the health system. Exposures: Sentinel lymph node biopsy and adjuvant RT. Main Outcomes and Measures: Primary outcomes were 5-year locoregional recurrence-free survival (LRFS) rate and disease-free survival (DFS) rate after SLNB and after RT. Secondary outcomes included recurrence rate, subgroups that may benefit from SLNB or RT, and use rate of SLNB and RT over time. Propensity scores were used to create 2 cohorts to separately evaluate the association of SLNB and RT with recurrence outcomes. Cox proportional hazards regression model was used to estimate hazard ratios (HRs). Results: From 2010 to 2018, a total of 3361 women 70 years or older (median [interquartile range {IQR}] age, 77.0 [73.0-82.0] years) with estrogen receptor-positive, ERBB2-negative, clinically node-negative breast cancer were included in the study. Of these women, 2195 (65.3%) received SLNB and 1828 (54.4%) received adjuvant RT. Rates of SLNB steadily increased (1.0% per year), a trend that persisted after the 2016 adoption of the Choosing Wisely guideline. Rates of RT decreased slightly (3.4% per year). To examine patient outcomes and maximize follow-up time, the analysis was limited to cases from 2010 to 2014, identifying 2109 patients with a median (IQR) follow-up time of 4.1 (2.5-5.7) years. In the propensity score-matched cohorts, no association was found between SLNB and either LRFS (HR, 1.26; 95% CI, 0.37-4.30; P = .71) or DFS (HR, 1.92; 95% CI, 0.86-4.32; P = .11). In addition, RT was not associated with LRFS (HR, 0.33; 95% CI, 0.09-1.24; P = .10) or DFS (HR, 0.99; 95% CI, 0.46-2.10; P = .97). Subgroup analysis showed that stratification by tumor grade or comorbidity was not associated with LRFS or DFS. Low absolute rates of recurrence were observed when comparing the groups that received SLNB (3.5%) and those that did not (4.5%) as well as the groups that received RT (2.7%) and those that did not (5.5%). Conclusions and Relevance: This study found that receipt of SLNB or RT was not associated with improved LRFS or DFS in older patients with ER-positive, clinically node-negative breast cancer. Despite limited follow-up time and wide 95% CIs, this study supports the continued deimplementation of both SLNB and RT in accordance with the Choosing Wisely and National Comprehensive Cancer Network guidelines.

Identifying Genomic Alterations in Patients With Stage IV Breast Cancer Using MammaSeq: An International Collaborative Study.

BACKGROUND: Identification of genomic alterations present in cancer patients may aid in cancer diagnosis, prognosis and therapeutic target discovery. In this study, we aimed to identify clinically actionable variants present in stage IV breast cancer (BC) samples. MATERIALS AND METHODS: DNA was extracted from formalin-fixed paraffin-embedded samples of BC (n = 41). DNA was sequenced using MammaSeq, a BC-specific next-generation sequencing panel targeting 79 genes and 1369 mutations. Ion Torrent Suite 4.0 was used to make variant calls on the raw data, and the resulting single nucleotide variants were annotated using the CRAVAT toolkit. Single nucleotide variations (SNVs) were filtered to remove common polymorphisms and germline variants. CNVkit was employed to identify copy number variations (CNVs). The Precision Medicine Knowledgebase (PMKB) and OncoKB Precision Oncology Database were used to associate clinical significance with the identified variants. RESULTS: A total of 41 samples from Turkish patients with BC were sequenced (read depth of 94-13,340; median of 1529). These patients were diagnosed with various BC subtypes including invasive ductal carcinoma, invasive lobular carcinoma, apocrine BC, and micropapillary BC. In total, 59 different alterations (49 SNVs and 10 CNVs) were identified. From these, 8 alterations (3 CNVs - ERBB2, FGFR1, and AR copy number gains and 5 SNVs - IDH1.R132H, TP53.E204∗, PI3KCA.E545K, PI3KCA.H1047R, and PI3KCA.R88Q) were identified to have some clinical significance by PMKB and OncoKB. Moreover, the top 5 genes with the most SNVs included PIK3CA, TP53, MAP3K1, ATM, and NCOR1. Additionally, copy number gains and losses were found in ERBB2, GRB7, IGFR1, AR, FGFR1, MYC, and IKBKB, and BRCA2, RUNX1, and RB1, respectively. CONCLUSION: We identified 59 unique alterations in 38 genes in 41 stage IV BC tissue samples using MammaSeqTM. Eight of these alterations were found to have some clinical significance by OncoKB and PKMB. This study highlights the potential use of cancer specific next-generation sequencing panels in clinic to get better insight into the patient-specific genomic alterations.

ATLANTIS - Attractor Landscape Analysis Toolbox for Cell Fate Discovery and Reprogramming.

Boolean modelling of biological networks is a well-established technique for abstracting dynamical biomolecular regulation in cells. Specifically, decoding linkages between salient regulatory network states and corresponding cell fate outcomes can help uncover pathological foundations of diseases such as cancer. Attractor landscape analysis is one such methodology which converts complex network behavior into a landscape of network states wherein each state is represented by propensity of its occurrence. Towards undertaking attractor landscape analysis of Boolean networks, we propose an Attractor Landscape Analysis Toolbox (ATLANTIS) for cell fate discovery, from biomolecular networks, and reprogramming upon network perturbation. ATLANTIS can be employed to perform both deterministic and probabilistic analyses. It has been validated by successfully reconstructing attractor landscapes from several published case studies followed by reprogramming of cell fates upon therapeutic treatment of network. Additionally, the biomolecular network of HCT-116 colorectal cancer cell line has been screened for therapeutic evaluation of drug-targets. Our results show agreement between therapeutic efficacies reported by ATLANTIS and the published literature. These case studies sufficiently highlight the in silico cell fate prediction and therapeutic screening potential of the toolbox. Lastly, ATLANTIS can also help guide single or combinatorial therapy responses towards reprogramming biomolecular networks to recover cell fates.