Association of Funding and Meal Preparation Time With Nutritional Quality of Meals of Supplemental Nutritional Assistance Program Recipients.

Importance: The Supplemental Nutrition Assistance Program (SNAP) is a federal program that provides food-purchasing assistance to low-income people; however, its current design does not account for the time availability of SNAP recipients to prepare meals. Objective: To evaluate the association of the availability of funding for food purchases and time for meal preparation with the nutritional quality of meals of SNAP recipients. Design, Setting, and Participants: This study used decision analytical modeling to evaluate the nutritional quality of meals of SNAP recipients. The model was developed from February 6, 2017, to December 12, 2020, using data from 2017 and is based on discrete optimization. The model describes food and grocery purchasing, in-home meal preparation, and meal plan choices of a family of SNAP participants (2 adults and 2 children) while considering food preferences, meal preparation time, and food costs. The model assumes food preferences match the foods typically purchased by SNAP households. Costs of food ingredients and prepared foods are taken from a single zip code. Exposures: Time availability and total amount and type of funding were varied. Allowing prepared delicatessen foods and disallowing frozen prepared foods for purchase using SNAP funds were considered. Main Outcomes and Measures: The primary outcome was the number of home-cooked meals and the amounts of fruits, vegetables, protein, sodium, sugar, and fiber consumed from generated meal plans. Amounts were evaluated as a percentage of the quantity recommended by established dietary guidelines. Results: Increased time availability was associated with increases in the percentage of home-cooked meals and servings of fruits/vegetables and decreased sodium consumption. Higher levels of funding were associated with increased consumption of fiber, fruits/vegetables, protein, sodium, and sugar. With 20 min/d of cooking time, $400/mo of SNAP benefits, and $100/mo of self-funding, the meal plan had a mean (SE) of 20.1% (0.3%) of meals home cooked, 0.5 (<0.1) servings/d per person of fruits/vegetables, 100.3% (0.6%) of daily recommended protein per person, 115.1% (0.8%) of daily recommended sodium per person, 241.8% (1.0%) of daily recommended sugar per person, and 31.2% (0.3%) of daily recommended fiber per person. With 20 min/d of cooking time, $400/mo of SNAP benefits, and $600/mo of self-funding, the meal plan had a mean (SE) of 23.9% (1.0%) of meals home cooked, 2.8 (0.1) servings/d per person of fruits/vegetables, 134.9% (1.6%) of daily recommended protein per person, 200.9% (3.1%) of daily recommended sodium per person, 295.1% (3.1%) of daily recommended sugar per person, and 90.1% (1.0%) of daily recommended fiber per person. With 60 min/d of cooking time, $400/mo of SNAP benefits, and $100/mo of self-funding, the meal plan had a mean (SE) of 52.7% (0.9%) of meals home cooked, 1.4 (<0.1) servings/d per person of fruits/vegetables, 109.0% (1.1%) of daily recommended protein per person, 108.7% (1.0%) of daily recommended sodium per person, 298.6% (2.0%) of daily recommended sugar per person, and 38.8% (0.4%) of daily recommended fiber per person. With 60 min/d of cooking time, $400/mo of SNAP benefits, and $600/mo of self-funding, the meal plan had a mean (SE) of 42.8% (1.2%) meals home cooked, 4.3 (0.1) servings/d per person of fruits/vegetables, 144.4% (1.8%) of daily recommended protein per person, 165.2% (2.8%) of daily recommended sodium per person, 322.4% (2.4%) of daily recommended sugar per person, and 91.0% (0.9%) of daily recommended fiber per person. Conclusions and Relevance: In this decision analytical model, meal preparation time was associated with the ability of SNAP recipient families to consume nutritious meals, suggesting that increased funding alone may be insufficient for improving the nutritional profiles of SNAP recipients. Given the current US food supply, governmental interventions that provide the equivalence in increased time availability to achieve nutritious meals may be needed.

Correction: Modeling differentiation-state transitions linked to therapeutic escape in triple-negative breast cancer.

[This corrects the article DOI: 10.1371/journal.pcbi.1006840.].

Modeling differentiation-state transitions linked to therapeutic escape in triple-negative breast cancer.

Drug resistance in breast cancer cell populations has been shown to arise through phenotypic transition of cancer cells to a drug-tolerant state, for example through epithelial-to-mesenchymal transition or transition to a cancer stem cell state. However, many breast tumors are a heterogeneous mixture of cell types with numerous epigenetic states in addition to stem-like and mesenchymal phenotypes, and the dynamic behavior of this heterogeneous mixture in response to drug treatment is not well-understood. Recently, we showed that plasticity between differentiation states, as identified with intracellular markers such as cytokeratins, is linked to resistance to specific targeted therapeutics. Understanding the dynamics of differentiation-state transitions in this context could facilitate the development of more effective treatments for cancers that exhibit phenotypic heterogeneity and plasticity. In this work, we develop computational models of a drug-treated, phenotypically heterogeneous triple-negative breast cancer (TNBC) cell line to elucidate the feasibility of differentiation-state transition as a mechanism for therapeutic escape in this tumor subtype. Specifically, we use modeling to predict the changes in differentiation-state transitions that underlie specific therapy-induced changes in differentiation-state marker expression that we recently observed in the HCC1143 cell line. We report several statistically significant therapy-induced changes in transition rates between basal, luminal, mesenchymal, and non-basal/non-luminal/non-mesenchymal differentiation states in HCC1143 cell populations. Moreover, we validate model predictions on cell division and cell death empirically, and we test our models on an independent data set. Overall, we demonstrate that changes in differentiation-state transition rates induced by targeted therapy can provoke distinct differentiation-state aggregations of drug-resistant cells, which may be fundamental to the design of improved therapeutic regimens for cancers with phenotypic heterogeneity.

Differentiation-state plasticity is a targetable resistance mechanism in basal-like breast cancer.

Intratumoral heterogeneity in cancers arises from genomic instability and epigenomic plasticity and is associated with resistance to cytotoxic and targeted therapies. We show here that cell-state heterogeneity, defined by differentiation-state marker expression, is high in triple-negative and basal-like breast cancer subtypes, and that drug tolerant persister (DTP) cell populations with altered marker expression emerge during treatment with a wide range of pathway-targeted therapeutic compounds. We show that MEK and PI3K/mTOR inhibitor-driven DTP states arise through distinct cell-state transitions rather than by Darwinian selection of preexisting subpopulations, and that these transitions involve dynamic remodeling of open chromatin architecture. Increased activity of many chromatin modifier enzymes, including BRD4, is observed in DTP cells. Co-treatment with the PI3K/mTOR inhibitor BEZ235 and the BET inhibitor JQ1 prevents changes to the open chromatin architecture, inhibits the acquisition of a DTP state, and results in robust cell death in vitro and xenograft regression in vivo.