**PHASE I/II** **1. Determination of the effect of biofield therapy on the proliferation of HCC and PDAC cells and relevant molecular mechanism** *Rationale and research plan*: To gain comprehensive understanding on the impact of biofield therapy on HCC and PDAC cells *in vitro*. Under this specific aim, we will examine the proliferation of 2D cultured human HCC Hep3B cells, human PDAC Panc-1 cells, and mouse pancreatic cancer mM1 cells after they are treated with different doses of local, distant, or prerecorded biofield therapy. Human breast cancer MDA-MB-231 cells and mouse lung carcinoma LLC cells will be used as positive control and explored further as indicated. EEGs will be collected from the therapists during the first sessions of each experiment of the in-person treatments. Given 3D culture offers the microenvironment that is closer to mimic the *in vivo* setting and allows us to observe the long-term effects of the treatment, we will then test the survival of these cells after being treated with biofield therapy. These studies will be followed with the examination of the molecular mechanisms including cell signaling proteins by Reverse Phase Proteomic Array (RPPA), epigenetic alteration including DNA methylation and histone acetylation, and stem cell markers in the cells treated with localized biofield therapy. If results of the cell proliferation and cell survival are positive, the effect of local biofield therapy will be evaluated in the cell cultures that are placed in a faraday cage during treatment sessions. If the result of cell proliferation and cell survival are negative, we will either examine the effect of these biofield therapies in other cancer types or continue to the *in vivo* study under *in vivo* phase II. **1.1. Cell proliferation in human HCC Hep3B, human PDAC Panc-1 cells and mouse pancreatic cancer mM1 cells, human breast cancer MDA-MB-231 and mouse lung carcinoma LLC cells in 2D culture condition.** Hep3B, Panc-1, mM1, MDA-MB-231 and LLC cells (6- 8 x 104) will be plated in 96 well plates the day prior to the treatment. Following incubation, cells will be either exposed to normal control (no treatment and cells will be kept in the incubator when the cells are treated with sham and biofield therapist), sham, or the biofield therapies including onsite treatment or distance treatment at three different doses, i.e. 1, 2, and 4 hours split over 2 days (0.5, 1, and 2 hours per day for 2 days) at room temperature. After the respective treatments at day 2, they will be either pulled for the measurement or transferred back to the incubator and incubated for an additional 3 hrs, 48hr and 10 days (Figure 1). To test the antiproliferative effect of recorded sounds, aforementioned cells will be either not treated or treated with sham or recorded sounds for 0.5 hr, 1 hr, or 2 hr per day for 1, 2, 6, 10, and 14 days. For the prerecorded treatment sessions, cells will be harvested immediately after the treatment is finished. For these studies we will have 8 samples for each condition. The cell viability will be measured either using the PrestoBlue reagent or CellTiter-Glo kit according to the manufacturer's instructions (Thermo Fisher Scientific, Waltham, MA and Promega, respectively)8. Briefly, for prestoblue assay, 20 µl of the PrestoBlue reagent will be added to each well containing 200 µl media. After 30 min to 1 hr of incubation at 37°C, the absorbance will be read at a wavelength of 590 (Ex/Em: 560/590 nm) using a V-Max Micro-plate Reader by Molecular Devices, Inc. (Sunnyvale, CA). For the cell proliferation measurement by CellTiter-Glo, an aliquot of 100 µl of the mixture of CellTiter Glo Substrate and buffer to each well containing 100 µl media. After 10 min of the incubation at room temperature, the luminescence will be recorded with similar plate reader. Experiments will be conducted in duplicated plates. **1.2 Cell survival in human HCC Hep3B, human PDAC Panc-1 cells and mouse pancreatic cancer mM1 cells, human breast cancer MDA-MB-231 cells and mouse lung carcinoma LLC cells in 3D culture condition.** To further validate the anti-proliferative effect of biofield therapy in aforementioned cells, we will examine the survival of these cells growing in 3D culture after they are repeatedly treated with biofield therapy. It is believed that 3D culture offers the microenvironment that is closer to mimic the *in vivo* setting. The 3D culture of these cancer cells has been established in Dr. Yang’s laboratory. The detailed experimental procedure is listed in Figure 2. Briefly, the aforementioned cells will be cultured in the matrigel of the 8-well chamber slides. Cells will be treated with either normal control, sham or biofield therapist (in person and distance) for 1, 2, and 4 hours split over 2 days at room temperature. After the respective treatments, they will be transferred back to the incubator and incubated for an additional 6, 10, and 14 days. Extra treatments will be delivered per suggestions from the biofield therapists. Recorded sounds will be delivered for 0.5, 1, or 2 hrs daily for 1, 2, 5, 10, and 14 days. For these studies we will have 8 samples for each condition. At the end of each study period, cells will be immediately harvested after treatment, washed with PBS, digested, and fixed then cytospin onto glass slides with StatSpin Cytofuge 12 (Iris International Inc, Westwood, MA). Cells will be then stained with DAPI, sealed and, imaged with Olympus fluorescence microscope (Olympus America Inc, Waltham, MA). The cell number will be counted in the pictures and recorded for quantification. **2. EEG Procedures**: EEGs will be collected from biofield therapists and sham control participants at baseline (before the first session), during the first session, and at the end of the first session. 20 minutes of resting EEG will be recorded with consistent light and temperature conditions. EEG recordings will be obtained continuously and using 19 electrodes in the standard 10-20 international system placement (FP1, FP2, F3, F4, Fz, F7, F8, C3, C4, Cz, T3, T4, T5, T6, P3, P4, Pz, O1, and O2) referenced to A1 and A2 (linked ear montage). Data will be collected in the eyes-closed (when possible) and eyes-open conditions, recorded digitally at 500 samples per second (band passed 0.15–200 Hz). Impedances will be brought to under 5 Kohms. EEG spectrum will be calculated from the first 2 minutes of artifact-free data at each time point of interest with fast Fourier transform using 4-second epochs with 1/32 seconds of overlapping window advancement. Mean amplitude, power, relative, and absolute values will be computed offline for each of the frequency bands delta (0.5-3.5 Hz), theta (4-7HZ), alpha (8-12HZ), beta1 (13-15HZ), high beta (16-29HZ), and gamma (30HZ+). For EEG data collection, we will use a Zeto (Palo Alto, CA) acquisition headset. The Zeto device is the only FDA cleared, HIPPA compliant, bluetooth enabled, dry electrode EEG system approved for clinical use. **3. Data analysis and statistical consideration (details on the data analyses of EEGs are provided below in Section IIIA)**. We will apply standard statistical models and develop appropriate new methods to analyze all results within this specific milestone project. Graphical methods will be used to examine the distribution of the data and any associations among covariates and outcome variables. The phenotypical changes, such as cell growth and proliferation will be compared between control, sham, and biofield therapy treated samples at each dose level (1, 2 and 4 hours), as well as across dose levels, for each type of treatment (in person healer, distant healer, or recording), and at each incubation time point with 8 samples for each as illustrated in Figure 1 and Figure 2. For the molecular biological markers such as cell signaling proteins and genes as well as stem cell markers we will assay from the best incubation time and examine all treatment dose levels by group with 7 samples for each. For the epigenetic markers, we will select the one best incubation time and dose of treatment and examine group differences with 4 samples for each. Analyses will be conducted using analysis of variance (ANOVA) and two-sample t-tests (including the Satterthwaite t-tests in the presence of unequal between-group variances), as appropriate. Our primary objectives are to test for the treatment vs sham control differences at each dose level, as well as the interaction effects between treatment and dose to identify the dose associated with the best treatment effects. All protein and gene expression data will be log transformed before the analysis. For quantification of protein expression in RPPA experiments, we will use the R statistical software package (http://cran.r-project.org) and the SuperCurve package (http://bioinformatics.mdanderson.org/Software/OOMPA). We will estimate and control the FDR to deal with multiple testing problems for the data generated by RPPA testing. The interrelationship between epigenetic biomarkers and certain hallmark pathways will be compared to see if we can determine correlation between biofield therapy and pathway activation, which may lead to an understanding of biological mediators and/or mechanism by which subtle energy improves cancer outcomes. *Justification of number of replicates*. Eight replicates are chosen for the comparison of cell growth and proliferation, and molecular biological markers across all experiments due to the commonly observed small within-group variation of outcomes, relative to the between-group mean differences, if any, in typical *in vitro* settings. Our primary interest of comparison is between the sham and treatment group and to define the best treatment dose by testing for the interaction effects between treatment doses. Specifically, 8 replicates in each group (sham or local/distant treatment) will have 94% power to detect a difference in mean of 0.23 (for log-transformed cell proliferation, corresponding to a 26% improvement in cell proliferation between treatment and sham control) assuming that the common standard deviation is 0.03 using a two-sample t-test with a 0.05 two-sided significance level. Similarly, large effect size was observed for the SOX-2 outcome in our pilot study. To test for the interaction effects between treatment and dose, a two-way ANOVA with a 0.05 significance level will have 76% power to detect an interaction among the 2 treatment levels and the 3 dose levels of 0.003, assuming that the common standard deviation is 0.12, when the number of replicates in each combination cell is 8. This above interaction effect corresponds to a treatment improvement over sham control in cell proliferation across three doses of 8%, 26% and 40%, respectively. Changes or differences in scores of 30% or greater will be viewed as biologically significant. Using the automated protein analysis platform, such as Jess, all the analysis within the same treatment group and same incubation time point will be performed in a single analysis. Using 7 replicates in each group (sham or local/distant treatment) will have 92% power to detect a difference in mean of 0.438 in log-transformed SOX2 level, corresponding to a 34% reduction in mean SOX2 level on its original scale for the treatment as compared to the sham control group, assuming that the common standard deviation is 0.22, using a two-sample t-test with a 0.05 two-sided significance level. In addition, a two-way ANOVA with a 0.05 significance level will have 80% power to detect an interaction among the 2 treatment levels and 3 dose levels of 0.013, assuming that the common standard deviation is 0.22, when the number of replicates in each combination cell is 7. Changes or differences in scores, of30% or greater will be viewed as biologically significant. For epigenetics data, four replicates in each group (sham or local treatment) will have 80% power to detect a difference in mean of 0.438 in log-transformed data, corresponding to a 34% reduction in mean on its original scale for the treatment as compared to the sham control groups, assuming that the common standard deviation is 0.15, using a two-sample t-test with a 0.05 two-sided significance level. The assumed standard deviation is 0.15 because these in vitro experiments will be run in the same time, resulting in a smaller standard deviation. Changes or differences in scores of 30% or greater will be viewed as biologically significant. All data analysis for the *in vitro* and *in vivo* studies will be performed or supervised by Dr. Yisheng Li. In the latter case, a biostatistician from the Department of Biostatistics at MD Anderson Cancer Center will be identified by Dr. Li to help perform the analysis, whose effort is funded by the MD Anderson Comprehensive Cancer Center Support Grant (CCSG). **4. Expected results and alternative approach**. In light of a number of studies reported that biofield therapy is able to slow down the growth of various tumor cells, including breast and lung cancer, we would expect, 1) The number of viable mouse pancreatic tumor mM1 cells, human pancreatic cancer Panc-1, and human HCC hep3B cells in biofield therapy treated group will be significantly lower than that of control in 2D culture condition; 2) The number of viable cells in the biofield therapy treated 3D cultured cells after 10 days of treatment will be significantly lower than that of control group. If we observe the positive results in the cancer cells growing in both 2D and 3D culturing conditions, it would suggest that biofield therapy has the ability to reduce the proliferation of pancreatic cancer and HCC and this effect is not tumor type specific. However, based on the work of Bengston and colleagues, we may actually find increased proliferation as a result of biofield therapy. The proposed two tailed tests will allow exploration of this alternative hypothesis. We will then evaluate the effect of biofield therapy on altering the cell cycle and apoptotic cell death which will be examined by flow cytometry analysis and RPPA analysis. The mechanistic study will be further conducted in the biofield therapy sensitive cell lines. If the cell proliferation and cell survival data did not support our hypothesis, we will first test different cell lines within the same tumor types prior to choosing cell lines from different type of tumor. 3) The expression of PI3k and pPDK1 proteins will be down-regulated in biofield therapy treated cells than that of sham control which will suggest that biofield therapy treatment elicited inhibition of tumor cell proliferation is mediated by targeting PI3K and energy related pathway. If we failed to observe the changes on PI3K and PDK1 protein levels after the cells were treated with biofield therapy, we will further explore other mechanisms of action, such as epigenetic changes and cancer stemness. 4) The epigenetic markers, such as DNA methylation or histone acetylation and methylation would be altered indicating the biofield therapy may influence the epigenetics of these tumor cells. 5) Given we have observed that Sean Harribance treated lung tumors showed significantly lower expression of very important stem cells marker SOX2 protein, we expect that some of the stem cell markers would be lower in the biofield therapy treated cells than that of the control group. If we observed that the expression of stem cell markers in the biofield therapy treated cells was similar to that of control, we would conclude that biofield therapy elicited anticancer activity is not targeting the stemness of cancer cells in pancreatic and liver cancer. If any aforementioned mechanistic pathways are significantly altered by biofield therapy, we will conduct the blocking or enhancing studies of specific targets to further validate the markers or pathways to confirm which are most instrumental for biofield therapy’s anticancer activity. These studies will be carried out in the in vitro Milestone Phase II during the first year of study.   [1]: https://mfr.osf.io/export?url=https://osf.io/v67g5/?direct&mode=render&action=download&public_file=False&initialWidth=774&childId=mfrIframe&parentTitle=OSF%20%7C%20Figure%201.jpg&parentUrl=https://osf.io/v67g5/&format=2400x2400.jpeg