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SummaryPurpose and Methods Trop-2 is a glycoprotein over-expressed in many solid tumors but at low levels in normal human tissue, providing a potential therapeutic target. We conducted a phase 1 dose-finding study of PF-06664178, an antibody-drug conjugate that targets Trop-2 for the selective delivery of the cytotoxic payload Aur0101. The primary objective was to determine the maximum tolerated dose and recommended phase 2 dose. Secondary objectives included further characterization of the safety profile, pharmacokinetics and antitumor activity. Eligible patients were enrolled and received multiple escalating doses of PF-06664178 in an open-label and unblinded manner based on a modified continual reassessment method. Results Thirty-one patients with advanced or metastatic solid tumors were treated with escalating doses of PF-06664178 given intravenously every 21 days. Doses explored ranged from 0.15 mg/kg to 4.8 mg/kg. Seven patients experienced at least one dose limiting toxicity (DLT), either neutropenia or rash. Doses of 3.60 mg/kg, 4.2 mg/kg and 4.8 mg/kg were considered intolerable due to DLTs in skin rash, mucosa and neutropenia. Best overall response was stable disease in 11 patients (37.9%). None of the patients had a partial or complete response. Systemic exposure of PF-06664178 increased in a dose-related manner. Serum concentrations of free Aur0101 were substantially lower than those of PF-06664178 and total antibody. No correlation of Trop-2 expression and objective response was observed, although Trop-2 overexpression was not required for study entry. The intermediate dose of 2.4 mg/kg appeared to be the highest tolerated dose, but this was not fully explored as the study was terminated early due to excess toxicity. Conclusion PF-06664178 showed toxicity at high dose levels with modest antitumor activity. Neutropenia, skin rash and mucosal inflammation were dose limiting toxicities. Findings from this study may potentially aid in future antibody drug conjugate design and trials.

Background: The effectiveness of many targeted therapies is limited by toxicity and the rise of drug resistance. A growing appreciation of the inherent redundancies of cancer signaling has led to a rise in the number of combination therapies under development, but a better understanding of the overall cancer network topology would provide a conceptual framework for choosing effective combination partners. In this work, we explore the scale-free nature of cancer protein-protein interaction networks in 14 indications. Scale-free networks, characterized by a power-law degree distribution, are known to be resilient to random attack on their nodes, yet vulnerable to directed attacks on their hubs (their most highly connected nodes). Results: Consistent with the properties of scale-free networks, we find that lethal genes are associated with ~5-fold higher protein connectivity partners than non-lethal genes. This provides a biological rationale for a hub-centered combination attack. Our simulations show that combinations targeting hubs can efficiently disrupt 50% of network integrity by inhibiting less than 1% of the connected proteins, whereas a random attack can require inhibition of more than 30% of the connected proteins. Conclusions: We find that the scale-free nature of cancer networks makes them vulnerable to focused attack on their highly connected protein hubs. Thus, we propose a new strategy for designing combination therapies by targeting hubs in cancer networks that are not associated with relevant toxicity networks. Competing Interest Statement The authors have declared no competing interest.

The relationship between regulatory trans-factors binding a gene's cis-regulatory sequence elements and the transcriptional output of that gene is fundamental to even the most complex network behaviors such as metabolism and differentiation. In eukaryotes, chromatin dynamics on gene promoter sequences is an integral part of regulation, and nucleosome remodeling is often required for transcription activation. Though the transient response of these regulated genes is often important in biological contexts, the role of promoter chromatin architecture in activation kinetics is still unclear. We sought to investigate this relationship as well as possible links to the cell cycle, over which chromatin state experiences dramatic changes. To study the activation kinetics of individual promoters, we develop a method to infer real-time transcription rates from protein expression in single Saccharomyces cerevisiae cells using time-lapse fluorescence microscopy. Comparison between the instantaneous transcription rate and cell-cycle phase in each cell demonstrates the majority of transcriptional variability is due to cell cycle-dependent effects with noisy expression restricted to S/G2/M. This is in stark contrast to current stochastic models of gene expression, most of which do not account for extrinsic effects, and reveals a permissive activation period beginning each S-phase. We then employ a switchable transactivator system to probe transient response kinetics as a function of promoter chromatin architecture at the PHO5 promoter, a well-established model system for chromatin-regulated expression. While we show transactivator binding site affinity and location relative to nucleosomes influences promoter response kinetics, the effect is primarily through architecture-dependent reliance on a dominant, permissive activation period in S/G2. Together with similar observations at synthetic promoters using a chimerical, switchable transactivator, these results suggest the cell cycle has a general role in transcription activation. Based on the timing of the permissive period, DNA replication may play a direct role in transactivation. Thus, in network topologies involving noisy genes and positive feedback, the cell cycle-dependent transcription would lead to distinct predictions between frequently- and non-dividing cells. This work reveals an unappreciated yet dominant role for the cell cycle as a general regulator of transcription in eukaryotes with direct implications in better modeling and design of biological networks.