Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects.

Identifying effective therapeutic treatment strategies is a major challenge to improving outcomes for patients with breast cancer. To gain a comprehensive understanding of how clinically relevant anti-cancer agents modulate cell cycle progression, here we use genetically engineered breast cancer cell lines to track drug-induced changes in cell number and cell cycle phase to reveal drug-specific cell cycle effects that vary across time. We use a linear chain trick (LCT) computational model, which faithfully captures drug-induced dynamic responses, correctly infers drug effects, and reproduces influences on specific cell cycle phases. We use the LCT model to predict the effects of unseen drug combinations and confirm these in independent validation experiments. Our integrated experimental and modeling approach opens avenues to assess drug responses, predict effective drug combinations, and identify optimal drug sequencing strategies.

Live Cell Lineage Tracing of Dormant Cancer Cells.

Breast cancer is a leading cause of global cancer-related deaths, and metastasis is the overwhelming culprit of poor patient prognosis. The most nefarious aspect of metastasis is dormancy, a prolonged period between primary tumor resection and relapse. Current therapies are insufficient at killing dormant cells; thus, they can remain quiescent in the body for decades until eventually undergoing a phenotypic switch, resulting in metastases that are more adaptable and drug resistant. Unfortunately, dormancy has few in vitro models, largely because lab-derived cell lines are highly proliferative. Existing models address tumor dormancy, not cellular dormancy, because tracking individual cells is technically challenging. To combat this problem, a live cell lineage approach to find and track individual dormant cells, distinguishing them from proliferative and dying cells over multiple days, is adapted. This approach is applied across a range of different in vitro microenvironments. This approach reveals that the proportion of cells that exhibit long-term quiescence is regulated by both cell intrinsic and extrinsic factors, with the most dormant cells found in 3D collagen gels. This paper envisions that this approach will prove useful to biologists and bioengineers in the dormancy community to identify, quantify, and study dormant tumor cells.

Systems approaches to uncovering the contribution of environment-mediated drug resistance.

Cancer drug response is heavily influenced by the extracellular matrix (ECM) environment. Despite a clear appreciation that the ECM influences cancer drug response and progression, a unified view of how, where, and when environment-mediated drug resistance contributes to cancer progression has not coalesced. Here, we survey some specific ways in which the ECM contributes to cancer resistance with a focus on how materials development can coincide with systems biology approaches to better understand and perturb this contribution. We argue that part of the reason that environment-mediated resistance remains a perplexing problem is our lack of a wholistic view of the entire range of environments and their impacts on cell behavior. We cover a series of recent experimental and computational tools that will aid exploration of ECM reactions space, and how they might be synergistically integrated.

A lineage tree-based hidden Markov model quantifies cellular heterogeneity and plasticity.

Individual cells can assume a variety of molecular and phenotypic states and recent studies indicate that cells can rapidly adapt in response to therapeutic stress. Such phenotypic plasticity may confer resistance, but also presents opportunities to identify molecular programs that could be targeted for therapeutic benefit. Approaches to quantify tumor-drug responses typically focus on snapshot, population-level measurements. While informative, these methods lack lineage and temporal information, which are particularly critical for understanding dynamic processes such as cell state switching. As new technologies have become available to measure lineage relationships, modeling approaches will be needed to identify the forms of cell-to-cell heterogeneity present in these data. Here we apply a lineage tree-based adaptation of a hidden Markov model that employs single cell lineages as input to learn the characteristic patterns of phenotypic heterogeneity and state transitions. In benchmarking studies, we demonstrated that the model successfully classifies cells within experimentally-tractable dataset sizes. As an application, we analyzed experimental measurements in cancer and non-cancer cell populations under various treatments. We find evidence of multiple phenotypically distinct states, with considerable heterogeneity and unique drug responses. In total, this framework allows for the flexible modeling of single cell heterogeneity across lineages to quantify, understand, and control cell state switching.

Recurrence of periodontitis and associated factors in previously treated periodontitis patients without maintenance follow-up.

Background: Preventive dentistry, including supportive periodontal therapy (SPT), is one of the most critical areas of attention. Despite SPT's importance in the long-term success of periodontal treatment, the patients' adherence to it is weak. The present study aimed to evaluate of periodontal disease's recurrence rate and its related factors in periodontal patients without regular follow-up. Methods: A cross-sectional study was set in a specialized periodontics clinic in Tehran, Iran. Patients with periodontitis who completed periodontal therapy during 2005-2014 and did not adhere to the maintenance phase were evaluated. The periodontal history of the patients was updated. The previous diagnoses of patients according to their previous periodontal charts were revised by AAP 2015 criteria. Then, periodontal parameters were assessed, and current periodontal status was evaluated. Statistical analyses consisted of Fisher's exact test, t-test, Man-Whitney test, and Kruskal-Wallis test. Spearman's correlation coefficient was used to assess the relationship between factors and variables. Results: Fifty patients were evaluated, including 29 males and 21 females. There was a significant relationship between the initial diagnosis and recurrence rate of periodontitis (P=0.017). There was also a significant relationship between the recurrence of periodontitis and the years elapsed since the initial treatment (P=0.027, r = 0.353). Smoking significantly affected tooth loss (P=0.001). Conclusion: Patients with severe periodontitis need more attention to participate in supportive periodontal care. The patients must be aware of the disadvantages of neglecting this phase and be reminded of regular follow-up.

Targeting an early and substantial increase in mean arterial pressure is critical in the management of type 1 hepatorenal syndrome: a combined retrospective and pilot study.

BACKGROUND: Appreciation of the central role for arterial vasodilatation in the pathogenesis of hepatorenal syndrome (HRS) has led to routine use of vasoconstrictors in combination with albumin as a medical therapy for HRS. Various vasoconstrictors have been explored but the optimal approach for such therapies has not yet been established. AIMS: The purpose of this study was to examine the role of targeting an early and substantial increase in mean arterial pressure (MAP) in the management of type 1 HRS, a condition associated with very poor prognosis. METHODS: A total of 59 patients with type 1 HRS who received a combination therapy of vasoconstrictors and albumin were enrolled into a retrospective cohort study. Subjects having a substantial increase of more than 10 mmHg in MAP by day 3 after initiation of therapy were categorized as MAP responders and the rest as MAP non-responders. In addition, five patients were enrolled into a prospective pilot study in which a titration protocol of vasoconstrictors was followed to achieve early goal-directed therapy (EGDT). RESULTS: MAP responders achieved significantly higher incidence of treatment success or total response, less requirement of dialysis and more incidence of liver transplantation. More importantly, this response is associated with better short-term and long-term overall survival as well as transplant-free survival. The effectiveness of such an approach was further confirmed in the pilot study which followed an EGDT protocol. CONCLUSIONS: Using an early and substantial increase in MAP as a therapeutic target is associated with favorable clinical outcomes in the management of type 1 HRS.

A new case of TEMPI syndrome.

We present an interesting case of a woman with new onset hypertension and abdominal fullness who was found to have huge bilateral perinephric fluid collections. Extensive workup revealed that she had secondary polycythemia, extensive truncal and proximal extremities telangiectasia and IgA-lambda monoclonal gammopathy of underdetermined significance. We believe that this is one of the rare cases consistent with the recently described TEMPI syndrome.