Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify, through TGFβ-withdrawal, a distinct MET state. We demonstrate significant differences between EMT and MET trajectories using a computational tool (TRACER) for reconstructing trajectories between cell states. In addition, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET PHENOtypic STAte MaP (PHENOSTAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET PHENOSTAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework to phenotypically characterize clinical samples in the context of in vitro EMT-MET findings which could help assess clinical relevance of EMT in cancer in future studies.

An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple ([Formula: see text]40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples.

The tumor specificity of JAA-F11, a novel monoclonal antibody specific for the Thomsen-Friedenreich cancer antigen (TF-Ag-alpha linked), has been comprehensively studied by in vitro immunohistochemical (IHC) staining of human tumor and normal tissue microarrays and in vivo biodistribution and imaging by micro-positron emission tomography imaging in breast and lung tumor models in mice. The IHC analysis detailed herein is the comprehensive biological analysis of the tumor specificity of JAA-F11 antibody performed as JAA-F11 is progressing towards preclinical safety testing and clinical trials. Wide tumor reactivity of JAA-F11, relative to the matched mouse IgG₃ (control), was observed in 85% of 1269 cases of breast, lung, prostate, colon, bladder, and ovarian cancer. Staining on tissues from breast cancer cases was similar regardless of hormonal or Her2 status, and this is particularly important in finding a target on the currently untargetable triple-negative breast cancer subtype. Humanization of JAA-F11 was recently carried out as explained in a companion paper "Humanization of JAA-F11, a Highly Specific Anti-Thomsen-Friedenreich Pancarcinoma Antibody and In Vitro Efficacy Analysis" (Neoplasia 19: 716-733, 2017), and it was confirmed that humanization did not affect chemical specificity. IHC studies with humanized JAA-F11 showed similar binding to human breast tumor tissues. In vivo imaging and biodistribution studies in a mouse syngeneic breast cancer model and in a mouse-human xenograft lung cancer model with humanized ¹²⁴I- JAA-F11 construct confirmed in vitro tumor reactivity and specificity. In conclusion, the tumor reactivity of JAA-F11 supports the continued development of JAA-F11 as a targeted cancer therapeutic for multiple cancers, including those with unmet need.

Collective cell behavior contributes to all stages of cancer progression. Understanding how collective behavior emerges through cell-cell interactions and decision-making will advance our understanding of cancer biology and provide new therapeutic approaches. Here, we summarize an interdisciplinary discussion on multicellular behavior in cancer, draw lessons from other scientific disciplines, and identify future directions.