Apparatus for the detection of cellular stress

Abstract

Disclosed are assays for determining resistance in target cells or tissues to treatments associated with cellular stress, using chemical microscopy and high-throughput single-cell analysis to determine functional metabolic changes, including determining metabolic reprogramming in the target cells or tissues to treatments associated with cellular stress, and methods of using the assays.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Application No. 6,3210,286, filed June 14, 2021, the contents of which are incorporated herein by reference in their entirety.

[0002] Government support This invention was made with Government support under Contract No. CA224275 awarded by the National Institutes of Health. The Government has certain rights in this invention.

[0003] Sequence Listing This application contains a Sequence Listing that has been submitted electronically in ASCII format and is incorporated by reference in its entirety. The ASCII copy created on June 8, 2022 is named BOS-0021US-SEQLIST_ST25.TXT and is 5,000 bytes in size. [Background technology]

[0004] The disclosed technology relates to an assay for determining resistance in a target cell or tissue to a treatment that is associated with cellular stress, and methods of using the assay. Summary of the Invention [Problem to be solved by the invention]

[0005] Metabolic reprogramming in cancer cells has been recognized since the discovery of the Warburg effect in the 1920s [1, 2]. Increased aerobic glycolysis is now widely considered a hallmark of many cancers and has been clinically exploited as a cancer biomarker for cancer therapy targeting and diagnosis [3]. In the past decade, numerous studies have explored the heterogeneity and complexity of cancer metabolism beyond the Warburg effect [4]. Metabolic reprogramming allows cancer cells to adapt to endogenous or exogenous cues from the microenvironment through adaptability and high flexibility in nutrient acquisition and utilization [5]. Particular attention has been paid to metabolic changes associated with key stages of cancer progression such as metastasis initiation, circulation and colonization [5–7]. Metabolic reprogramming in cancer stem cells has identified potential vulnerabilities for cancer stem cell-targeted therapies [8, 9]. Cancer cells also rewire their metabolic dependencies within specific microenvironmental niches by interacting with stromal cells

[10] or surrounding adipocytes [11, 12]. In addition, alterations in nutrient utilization under metabolic stress conditions have been recently reported [13–15]. Despite these recent advances, our understanding of cancer cell metabolism remains incomplete. One area that has been less studied is cancer metabolic reprogramming, which is associated with resistance to therapy.

[0006] Drug resistance remains one of the greatest challenges facing cancer treatment. Resistance to chemotherapy or molecular targeted therapy is the main cause of tumor recurrence and death

[16] . Emerging studies support the relationship between metabolic reprogramming and anticancer drug resistance [17, 18]. Several studies have linked the Warburg effect to resistance to radiation

[19] , and lactate production has been shown to promote resistance to chemotherapy in cervical cancer

[20] . Alterations in lipid metabolism have also been implicated in the acquisition of drug resistance

[21] . Increased de novo lipogenesis mediated by FASN promoted gemcitabine resistance in pancreatic cancer

[22] , while cancer-associated adipose tissue promoted resistance to antiangiogenic interventions by supplying fatty acids to cancer cells in areas where glucose demand was insufficient

[23] . In addition, lipid droplet production mediated by lysophosphatidylcholine acyltransferase 2 promoted resistance of colorectal cancer cells to 5-fluorouracil and oxaliplatin

[24] . It has been proposed that drug-resistant cells adopt a dormant state similar to embryonic developmental arrest to survive chemotherapy toxic insults, in which cell proliferation and metabolic processes are suppressed.

[25] These studies support that metabolic reprogramming underlies the development of drug resistance and point out the potential metabolic vulnerability of resistant cancer cells that remains untapped.

[0007] Platinum-based drugs, including cisplatin, carboplatin and oxaliplatin, represent one of the most widely used classes of chemotherapy drugs

[26] . Resistance to platinum is a barrier to effective treatment in multiple cancers, including ovarian, testicular, bladder, head and neck, non-small cell lung, etc.

[27] . Understanding the metabolic reprogramming of potential platinum-resistant cancer cells is important for the development of effective therapeutic strategies. However, within a cell population, only a small fraction of cells are drug resistant or resistant, making it difficult to accurately profile metabolic reprogramming using conventional techniques. In this study, we demonstrate the metabolic profile of platinum-resistant cancer cells at the single-cell level by utilizing a hyperspectral stimulated Raman scattering (SRS) imaging platform.

[0008] SRS microscopy is a recently developed label-free chemical imaging technique that detects intrinsic chemical bond vibrations [28–31]. The value of SRS microscopy was demonstrated in identifying cholesteryl ester accumulation as a signature associated with multiple aggressive cancers [32, 33], discovering increased lipid desaturation in OC stem cells, and tracking metabolic flux by isotopic labeling [34–36]. More recently, large-area hyperspectral SRS microscopy and high-throughput single-cell analysis revealed lipid-rich protrusions in cancer cells under stress

[37] . Single-cell metabolomics based on Raman spectroscopic microscopy revealed a critical role for lipid unsaturation in aggressive melanoma

[38] . This technique holds promise for understanding aerobic glycolysis and lipid metabolism associated with cellular stress. [Means for solving the problem]

[0009] The present disclosure provides an assay for determining resistance in a target cell or tissue to a treatment associated with cellular stress or perturbation, and a method for using the assay. One aspect of the present disclosure is an assay for determining resistance in a target cell or tissue to a treatment associated with cellular stress, comprising measuring functional metabolic alteration or change in the target cell or tissue by chemical microscopy, and determining a metabolic indicator of resistance to the treatment in the target cell or tissue. The functional metabolic alteration or change is an alteration from glucose and glycolysis-dependent anabolism and energy metabolism to fatty acid uptake and fatty acid oxidation-dependent anabolism and energy metabolism. In embodiments, the metabolic indicator correlates with resistance to the treatment in the target cell if the metabolic alteration or change is a decrease in glucose and glycolysis-dependent anabolism and an increase in fatty acid uptake and fatty acid oxidation-dependent anabolism and energy metabolism. In some embodiments, the metabolic indicator further correlates with resistance to the treatment in the target cell if the metabolic alteration is a decrease in de novo lipogenesis in the target cell.

[0010] Another aspect of the present disclosure is the use of the disclosed assay in a method for treating or inhibiting the resistance of target cells or tissue to a treatment associated with cell stress.Embodiments include a method for treating or inhibiting the resistance of target cells or tissue to a treatment associated with cell stress in a subject, by carrying out the assay disclosed herein to determine the metabolic indicator of resistance in target cells to a treatment, administering at least one fatty acid oxidation inhibitor to the subject, and administering at least one therapy to the subject.

[0011] One embodiment of the method includes measuring functional metabolic alterations in glucose and glycolysis dependent assimilation and increased fatty acid uptake and oxidation using chemical microscopy, where glucose and glycolysis dependent assimilation are decreased and fatty acid uptake and oxidation are increased.

[0012] In disclosed embodiments, the target cell is a cell (e.g., cancer cell, immune cell, or benign neoplasm) that can undergo metabolic reprogramming or change in response to cellular stress.In embodiments, the target cell is a cancer cell, such as ovarian cancer, prostate cancer, testicular cancer, bladder cancer, pancreatic cancer, lung cancer, breast cancer, esophageal cancer, head cancer, and neck cancer.In embodiments, the treatment is a cancer treatment that induces metabolic change in cells.

[0013] Other features and advantages of the disclosed aspects will become apparent from the following more detailed description, taken in conjunction with the accompanying drawings, which illustrate, by way of example, the principles of aspects of the invention. [Brief description of the drawings]

[0014] The patent or application file contains at least one drawing executed in color. Copies of this patent publication or this patent application publication containing color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0015] The teachings in the present disclosure will be more fully understood from the following description of various exemplary embodiments when read in conjunction with the accompanying drawings, in which: It should be understood that the drawings described below are for illustration purposes only and are not intended to limit the scope of any of the present teachings.

[0016] [Figure 1]Figure 1A-Q show high-throughput profiling of lipid metabolism in ovarian cancer cell lines. Figure 1A shows the process flow for high-throughput single-cell analysis of lipids using hyperspectral stimulated Raman scattering (SRS) imaging. Lipids are color-coded based on their parent cell color, and quantitative analysis of cell morphology, lipid amount, and intensity are also generated along with the images, with an image area of ​​500 μm × 500 μm. Figure 1B-E show dose response to cisplatin in PEO1 and PEO4 (Figure 1B), SKOV3 and SKOV3-cisR (Figure 1C), OVCAR5 and OVCAR5-cisR (Figure 1D), COV362 and COV362-cisR cells (Figure 1E); n=6 biological replicates. Figure 1F shows representative large-area SRS images of parental PEO1 cells and cisplatin-resistant PEO4 cells (an isogenic pair of cisplatin-sensitive and cisplatin-resistant ovarian cancer (OC) cells). Figure 1G shows histograms of integrated cellular lipid intensity in PEO1 and PEO4 cells generated by high-throughput single-cell analysis. Figure 1H shows representative large-area SRS images of parental SKOV3 cells and cisplatin-resistant SKOV3-cisR cells. Figure 1I shows histograms of integrated cellular lipid intensity in SKOV3 and SKOV3-cisR cells. Data in Figure 1F-1I are presented as mean ± 1 SD; n = 3 animals; two-tailed Student's t-test; P = 0.043; *P < 0.05. All scale bars: 20 µm. Figures 1J-K show histograms of integrated cellular lipid intensity in OVCAR5 and OVCAR5-cisR (Figure 1J), and COV362 and COV362-cisR cells (Figure 1K). Figures 1L-M show histograms of integrated cellular lipid intensity in SKOV3 cells (Figure 1L) and SKOV3-cisR cells (Figure 1M) treated or not with cisplatin. Data are presented as mean ± SD; n = 3 animals; two-tailed Student's t test; P = 0.043; *P < 0.05. All scale bars: 20 μm.Figure 1N shows the weight of xenografts from mice treated with saline or carboplatin for 3 weeks; (n=4, two-tailed Student's t-test; P=0.030; *P<0.05). Figure 1O shows the dose response to carboplatin in OC cells derived from xenografts developed in mice treated with carboplatin or saline; dose-response curves and scatter plots are shown as mean ± SD, n=4 technical replicates. Figure 1P shows representative hyperspectral SRS images (sum of all channels) and phasor-mapped lipid images of sliced ​​OVCAR5 xenograft tumor tissues from mice treated with vehicle (sensitive) or carboplatin (resistant). Figure 1Q shows quantitative analysis by area fraction of SRS signal from lipids in carboplatin-sensitive and -resistant ovarian tumor tissues. Data in Figures 1P-Q are presented as mean ± SD; n = 3 animals; two-tailed Student's t test; P = 0.043; *P < 0.05. All scale bars: 20 μm.

[0017] [Diagram 2]Figures 2A-M show that increased fatty acid uptake, but not de novo lipogenesis, is a major contributor to lipid accumulation in cisplatin-resistant OC cells. Figure 2A shows representative brightfield and SRS images of PEO1 and PEO4 cells fed with glucose-d7 for 3 days. Figure 2B shows quantitative analysis of SRS signals of CD binding in PEO1 and PEO4 cells fed with glucose-d7 by mean intensity and area fraction; n=5. P=0.0076 and 0.0083. Figure 2C shows representative brightfield and SRS images of PEO1 and PEO4 cells fed with PA-d31 for 6 hours. Figure 2D shows quantitative analysis of SRS signals of CD binding in PEO1 and PEO4 cells fed with PA-d31 by mean intensity and area fraction; n=6. P=0.0051 and 3×10-5. Figure 2E shows representative bright field and SRS images of PEO1 and PEO4 cells fed with OA-d34 for 6 h. Figure 2F shows quantitative analysis of the SRS signal of CD binding in PEO1 (n=6) and PEO4 (n=7) cells fed with OA-d34 by average intensity and area percentage; P=0.030 and 0.0048. Figure 2G shows representative SRS images of SKOV3 and SKOV3-cisR cells fed with glucose-d7 for 3 days, as well as quantitative analysis of the SRS signal of CD binding by average intensity. Figures 2H-I show representative SRS images of SKOV3 and SKOV3-cisR cells fed with PA-d31 for 6 h (Figure 2H) (n=5; P=0.0010) and OA-d34 for 6 h (Figure 2I) (n=8; P=2.2×10-5), as well as quantitative analysis of SRS signals of CD binding by mean intensity. All bar graph results are shown as mean ± SD. All statistical significance was analyzed using one-tailed Student's t-test. *P<0.05, **P<0.01, and ***P<0.001. All scale bars: 20 μm.Figure 2J-K show representative brightfield and SRS images of OVCAR5 and OVCAR5-cisR cells (Figure 2J) and COV362 and COV362-cisR cells (Figure 2K) fed glucose-d7 for 3 days, PA-d31 for 6 h, or OA-d34 for 6 h; n=6. Figure 2L shows representative whole hSRS and phasor-mapped lipid images of SKOV3 and SKOV3-cisR cells treated with vehicle or 10 μM C-75. Figure 2M shows quantitative analysis of SRS signals from lipids in SKOV3 and SKOV3-cisR cells treated with vehicle or 10 μM C-75. Results are shown as mean ± SD, n=6-8. *P<0.05, **P<0.01, and ***P<0.001.

[0018] [Diagram 3]Figure 3A-3N show that metabolic indices calculated by integrating glucose-derived lipogenesis and fatty acid uptake directly correlate with cisplatin resistance. Figure 3A-3C show the linear regression of glucose-d7 intensity against the IC50 of cisplatin in various OC cell lines (Figure 3A), PA-d31 intensity against the IC50 of cisplatin in various OC cell lines (Figure 3B), and PA-d31 / (PA-d31 + glucose-d7) against the IC50 of cisplatin in various OC cell lines (Figure 3C). Figure 3D shows the normalized SRS spectra of 17-octadecanoic acid (ODYA) and glucose-d7 in cells. Figure 3E shows the output SRS spectra from phasor analysis of C≡C bonds from ODYA and CD bonds from glucose-d7 and metabolites. Figure 3F shows representative bright-field, raw, and processed SRS images of ODYA and glucose-d7 in OVCAR5 and OVCAR5-cisR cells. Figure 3G shows quantitative analysis of ODYA-derived C≡C intensity (n=4), glucose-d7-derived CD intensity (n=6), and the ratio of C≡C / (C≡C+CD (n=4)) in OVCAR5 and OVCAR5-cisR cells (P=0.0082). Figure 3H shows representative bright-field, raw, and processed SRS images of ODYA and glucose-d7 in PEO1 and PEO4 cells; scale bar 20 μm. Figure 3I shows quantitative analysis of ODYA-derived C≡C intensity, glucose-d7-derived CD intensity, and the ratio of C≡C / (C≡C+CD) in PEO1 (n=6) and PEO4 cells (n=7). All bar graph results are presented as mean ± SD. All statistical significance was analyzed using one-tailed Student's t-test. *P<0.05, **P<0.01, and ***P<0.001. Figure 3J shows the linear regression of metabolic indicators, as defined by the ratio of C≡C / (C≡C+CD), against the IC50 of cisplatin in various OC cell lines; for (a-c) and (e), R2=0.9235; n=6.Figure 3K shows representative brightfield images, raw SRS images, and processed SRS images of ODYA and glucose-d7 in primary OC cells from cisplatin-treated resistant and cisplatin-treated sensitive patients. Figure 3L shows quantitative analysis of metabolic indices (ratio of C≡C / (C≡C+CD)) for primary OC cells from cisplatin-treated resistant and cisplatin-treated sensitive patients; each data point represents the average metabolic indices of individual cancer cells from a patient, and the error bars show the SEM; n=30, 31, 19, 25, 27, 33, 12, 11, 24, 20, and 30. Box plots show the analysis of each group (sensitive (n=7) vs. resistant (n=4)). The outer box borders represent the SEM, the inner box shows the mean, the line shows the median, the whiskers show 25%-75% of the data, and the circles show the maximum and minimum of the data. Statistical significance was analyzed using a two-tailed Student's t-test; P=0.011. *P<0.05. All scale bars: 20 μm. Figure 3M shows histograms of metabolic indices of primary ovarian cancer cells from platinum-resistant and platinum-sensitive patients; n=4. Figure 3N is a receiver operating characteristic (ROC) curve for metabolic indices of primary ovarian cancer cells from patients with platinum-resistant or platinum-sensitive tumors. AUC: area under the curve.

[0019] [Figure 4]Figures 4A-4DD show that fatty acid uptake directly contributes to cisplatin resistance. Figure 4A is an SRS image of OVCAR5-cisR cells cultured for 24 h with control serum (FBS), delipidated serum, or control serum supplemented with 1% lipid mixture. Figure 4B shows quantitative CH signals from lipid droplets in Figure 1A (OVCAR5-cisR cells cultured for 24 h with control serum (FBS), delipidated serum, or control serum supplemented with 1% lipid). Figure 4C shows representative SRS images of SKOV3-cisR cells cultured for 24 h with control serum (FBS) (n=5), no serum (n=6), and control serum supplemented with 1% lipid mixture (n=6). Scale bar: 20 μm. Figure 4D shows quantitative CH signals from lipid droplets in Figure 4C; P=0.044 and 0.00089. Figure 4E shows the dose response to cisplatin in culture environments with control, reduced (medium containing delipidated serum or medium without serum) and increased (control serum supplemented with 1% lipid mixture) lipid content for OVCAR5-cisR cells; n=3 biological replicates. Figure 4F-G shows the dose response to cisplatin in culture environments with control, reduced (medium containing delipidated serum or medium without serum) and increased (control serum supplemented with 1% lipid mixture) lipid content for PEO1 (Figure 4F) and SKOV3 (Figure 4G); n=3. Figure 4H shows representative bright-field and fluorescent images of SKOV3 (n=19 and 20) and SKOV3-cisR cells (n=16) cultured for 24 h in control or lipid-reduced medium (delipidated serum) and then treated with 100 μM of the fluorescent glucose analog 2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl)amino]-D-glucose (2-NBDG) for 2 h; scale bar: 50 μm. Figure 4I shows the quantified fluorescent signal intensity for Figure 4H; P=0.024. Figure 4J represents the relative mRNA expression levels of CD36, FATP1-6, FABP4-5, and FABP PM in OVCAR5 and OVCAR5-cisR cells; n=2 for FATP6; n=4 for FABP4 and FATP4; n=3 for other genes; n represents biological replicates.Figure 4K-Figure 4L show the relative mRNA expression levels of FABP5 (Figure 4K) and FABP(PM) (Figure 4L) in OVCAR5 and OVCAR5-cisR cells; results are shown as the mean ± SD; n = 4 biological replicates. P = 0.0037 and 0.0018. Figure 4M shows the relative mRNA expression levels of FABP5 in PEO1 (n = 4) and PEO4 (n = 5) cells; P = 0.012. Figure 4N shows the relative mRNA expression levels of FABP5 and FABP(PM) in OVCAR5 cells treated with cisplatin for 0, 6, 12 or 24 h; n = 3. P = 0.00016, 2.4 × 10-6, 0.00037, 0.0069, 0.00037 and 0.052. Figure 4O-4P show the relative mRNA expression levels of GLUT1 in SKOV3 and SKOV3-cisR cells (Figure 4O) (n=8; P=0.0089), and in OVCAR5 cells treated with cisplatin for 0, 6, 12, or 24 h; n=3 (Figure 4P). Figure 4Q and Figure 4S show representative bright-field and SRS images of SKOV3 and SKOV3-cisR cells fed with various concentrations of PA-d31 (Figure 4Q) and OA-d34 (Figure 4S) for 6 h; scale bar: 20 μm. Figure 4R and Figure 4T show quantitative analysis by the average intensity of SRS signal of CD binding in SKOV3 and SKOV3-cisR cells fed with PA-d31 (Figure 4R) or OA-d34 (Figure 4T); n=38, 33, 21, 37, 35, 35, 42, and 23, respectively. P = 2.2 x 10-7, 0.0016, and 0.019. Data for bar graphs in Figure 4D, and 4J, 4M, 4O, and 4P are presented as mean ± SD. Data for dose-response curves in Figure 4F and Figure 4G are presented as mean ± SD. For box plots in Figures 4I, 4R, and 4T, the outer box boundaries indicate 25%-75% of the data; the inner box indicates the mean; the line represents the median; the whiskers indicate SD; and the circles indicate the maximum and minimum of the data. All statistical significance was analyzed using a one-tailed Student's t-test. *P < 0.05, **P < 0.01, and ***P < 0.001.Figure 4U shows representative brightfield and SRS images of OVCAR5-cisR cells after 24 h of fatty acid (FA) transporter inhibitor BMS309403 (BMS) treatment at 10 μM during 6 h of co-incubation with 100 μM of PA-d31 (BMS inhibits FA uptake and sensitizes OC cells to cisplatin treatment). Figure 4V shows quantification of CD SRS signal intensity in Figure 4U; n=9 and 8. P=0.0026. Figure 4W is a representative brightfield and SRS image of OVCAR5-cisR cells after 24 h of FA transporter inhibitor BMS treatment at 5 μM, 10 μM or 20 μM and 6 h of incubation with 100 μM of PA-d31; scale bar: 20 μm. n=6, 5, 6 and 6 technical replicates. FIG. 4X shows quantification of CD SRS signal intensity from OVCAR5-cisR after 24 h treatment with 5 μM, 10 μM, or 20 μM BMS during 6 h co-incubation of 100 μM PA-d31; data shown as mean ± SD; n=6, 5, 6, and 6 technical replicates; one-tailed Student's t-test; P=0.019, 0.0055, and 0.0056. *P<0.05, **P<0.01. FIG. 4Y-4DD shows dose response to cisplatin with or without supplemental BMS treatment for PEO4 (FIG. 4Y), SKOV3-cisR (FIG. 4Z), and OVCAR5-cisR (FIG. 4AA) cells, as well as PEO1 (FIG. 4BB), SKOV3 (FIG. 4CC), and OVCAR5 (FIG. 4DD) cells. Results in all dose-response curves are shown as mean ± SD; n=3 biological replicates. All bar graph data are presented as mean ± SD. All statistical significance was analyzed using two-tailed Student's t-test. **P<0.01, and ***P<0.001. All scale bars: 20 μm.

[0020] [Diagram 5]Figure 5A-5V show that fatty acid uptake contributes to cisplatin resistance by increasing fatty acid oxidation. Figure 5A-5C are oxygen consumption curves of OVCAR5-cisR and OVCAR5 (Figure 5A), and OVCAR5-cisR (Figure 5B) and OVCAR5 (Figure 5C) with 40 μM etomoxir treatment for 3 h; n=4 biological replicates. Figure 5D shows quantification of oxygen consumption rate (OCR) for OVCAR5 and OVCAR5-cisR cells treated with (n=4) or without (n=6) etomoxir (40 μM), measured by using an extracellular oxygen consumption kit (Abcam); P=0.00044 and 0.041. FIG 5E is an OCR profile measured with a Seahorse® XF Analyzer (Seahorse Bioscience / Agilent) (Seahorse®) of OVCAR5 and OVCAR5-cisR cells injected (indicated by arrows) with mitochondrial respiration inhibitors oligomycin, carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP), rotenone, and antimycin A after etomoxir treatment or no treatment; n=3 biological replicates. FIG 5F shows quantified etomoxir-induced basal respiration, ATP production, and maximum respiration reduction in OVCAR5 and OVCAR5-cisR cells; data are presented as mean ± SD; n=3 technical replicates; two-tailed Student's t-test; P=0.0021, 0.018, and 0.0046; *P<0.05 and **P<0.01. FIG. 5G shows quantification of OCR for PEO1 and PEO4 cells measured by Seahorse® XF Analyzer; n=6 technical replicates. P=8.9×10−5. FIG. 5H-J show dose response to etomoxir for cisplatin-resistant cell lines and their parental cell lines, including PEO1 and PEO4 (FIG. 5H), OVCAR5 and OVCAR5-cisR (FIG. 5I), and COV362 and COV362-cisR (FIG. 5J).Figure 5K-5M show dose response to cisplatin with or without supplemental etomoxir treatment at 40 μM for PEO4 (Figure 5K), OVCAR5-cisR (Figure 5L), and COV362-cisR (Figure 5M) cells. Figure 5N shows relative mRNA expression levels of CPT1a in OVCAR5-cisR shCtrl and shRNA targeting CPT1a (shCPT1a); n=3. P=0.033. Figure 5O is a Western blot of CPT1a and GAPDH for OVCAR5-cisR transduced with shCtrl and shCPT1a cells; n=3 biological replicates. Figure 5P is a dose response to cisplatin for OVCAR5-cisR shCtrl and shCPT1a cells. Data in all dose response curves in Figure 5A-5C, Figure 5E, Figure 5H-5M, and Figure 5P are shown as mean ± SD; n=3. Figure 5Q shows the relative mRNA expression levels of CPT1a in OVCAR5 and OVCAR5-cisR cells; n=4. Figure 5R is a Western blot of CPT1a and GAPDH in OVCAR5 (n=2) and OVCAR5-cisR cells (n=3). Bar graph results in Figure 5D, Figure 5G, Figure 5N, and Figure 5Q are shown as mean ± SD. For Figure 5D, Figure 5G, Figure 5N-Figure 5O, and Figure 5Q-Figure 5R, statistical significance was analyzed using a one-tailed Student's t-test; *P<0.05. ***P<0.001. nsP>0.05. Figure 5S and Figure 5T are heat map charts of lipid metabolism-related genes analyzed by RNA sequencing in OVCAR5 (Figure 5S) and SKOV3 (Figure 5T) cell line pairs, where fatty acid oxidation (FAO)-related genes are highlighted in red and lipogenesis-related genes are shown in green. Figure 5U is a total tumor volume growth curve from day 14 to day 37 after tumor cell inoculation for vehicle (n=3), carboplatin (n=3), etomoxir (n=4) and combination (n=6) treatment groups. Figure 5V shows mouse body weight records since tumor inoculation for vehicle (n=3), carboplatin (n=3), etomoxir (n=4) and combination (n=6) treatment groups; data for PDX in vivo experiments in Figure 5U-V are shown as mean ± SEM.

[0021] [Figure 6]Figure 6A-6Q show that increased fatty acid uptake and oxidation supports cancer cell survival under cisplatin-induced oxidative stress. Figure 6A shows representative bright-field and fluorescent images of OVCAR5 (n=55) and OVCAR5-cisR (n=49) cells after treatment with the fluorescent probe 2',7'-dichlorofluorescein diacetate (DCFDA) cellular reactive oxidative species (ROS) assay kit. Figure 6B shows quantification of DCF fluorescent signal intensity for Figure 6A; P=4.6×10-29. Figure 6C shows representative bright-field and fluorescent images of PEO1 and PEO4 cells using the DCFDA cellular ROS assay kit; scale bar: 30 μm. Figure 6D shows quantification of DCF fluorescence signal intensity for PEO1 and PEO4 cells; outer box boundaries indicate 25%-75% of the data; inner box indicates the mean; line represents the median; whiskers indicate SD; circles indicate maximum and minimum values ​​of the data; n=10; P=0.00013. Figure 6E shows quantification of DCF fluorescence signal intensity for OVCAR5 and OVCAR5-cisR cells with 24 h of cisplatin treatment at 1.6 μM or 3.3 μM; n=2; P=0.0064, 0.040, and 0.016. Figure 6F-G shows quantified NADPH / NADP ratios for PEO1 and PEO4 (Figure 6F), and OVCAR5 and OVCAR5-cisR (Figure 6G); n=3; P=2.4×10-5 and 0.048. FIG. 6H shows the extracellular acidification rate (ECAR) profiles of PEO1 and PEO4 cells after treatment with 13.2 μM cisplatin as measured by Seahorse®; n=5; P=0.041; mean±SD. FIG. 6I shows quantification of ECAR of PEO1 and PEO4 cells before and 30 min after 13.2 μM cisplatin treatment; n=5. FIG. 6J shows the OCR profiles of PEO1 and PEO4 after 13.2 μM cisplatin treatment as measured by Seahorse®; mean±SD; n=5. FIG. 6K shows quantification of OCR for PEO1 and PEO4 cells before and 30 min after 13.2 μM cisplatin treatment as measured by Seahorse®; mean+SD; n=5.Figure 6L shows representative bright field and fluorescent images of OVCAR5 and OVCAR5-cisR cells treated with 100 μM fluorescent glucose analog 2-NBDG for 2 h after incubation with 3.3 μM cisplatin for 24 h. Figure 6M shows the quantified fluorescent signal intensity for Figure 6L; n=13, 16, 16, and 17. P=0.019 and 8.2×10-7. Figure 6N-6O show the quantified ATP / ADP ratios of cisplatin-resistant cell lines and their parental cell lines, including OVCAR5 (n=3) and OVCAR5-cisR (n=2) (Figure 6N), and PEO1 (n=5) and PEO4 (n=6) (Figure 6O); n represents biological repeats. P=0.0071 and 0.039. FIG. 6P shows the quantified ATP / ADP ratios of OVCAR5 and -OVCAR5-cisR treated with 3.3 μM cisplatin for 6 h with or without 100 μM palmitate supplementation; n=3 biological replicates. P=0.046. FIG. 6Q shows the proposed mechanism for cisplatin effects on cell metabolism and cell proliferation. All scale bars: 30 μm. For box plots (FIG. 6B and FIG. 6M), the outer box boundaries indicate 25%-75% of the data; the inner box indicates the mean; the line represents the median; the whiskers indicate the SD; and the circles indicate the maximum and minimum of the data. Data in the bar graphs in FIG. 6E-G and FIG. 6N-P are presented as the mean ± SD. All statistical significance was analyzed using a one-tailed Student's t-test. *P<0.05, **P<0.01, and ***P<0.001.

[0022] [Figure 7]Figures 7A-7L show that cisplatin-induced fatty acid uptake is a universal metabolic feature in multiple types of cancer. Figures 7A-7C show dose response to cisplatin for Mia Paca2 cells (Figure 7A), A549 cells (Figure 7B), and MD-MBA231 cells (Figure 7C). Data are shown as mean ± SD; n=3. Figure 7D shows representative brightfield and SRS images of Mia Paca2 cells treated with 6.6 μM cisplatin for 24 h followed by incubation with 100 μM PA-d31 or OA-d34 for 6 h. Figure 7E shows quantification by mean intensity of CD signal in Mia Paca-2 cells treated or not with cisplatin; n=7 for PA-d31, n=8 for OA-d34. P=0.00029 and 0.026. Figure 7F shows quantification of fold change of CD signal in Mia Paca-2 cells treated or not with cisplatin (n=7 for PA-d31, n=8 for OA-d34). Figure 7G shows representative bright field and SRS images of A549 cells treated with 13.2 μM cisplatin for 48 h, followed by incubation with 100 μM PA-d31 or OA-d34 for 6 h. Figure 7H shows quantification of mean intensity of CD signal in A549 cells treated or not with cisplatin; n=8. P=0.0031 and 0.016. Figure 7I shows quantification of fold change of CD signal in A549 cells treated or not with cisplatin; (n=8). Figure 7J shows representative bright field and SRS images of MDA-MB-231 cells treated with 6.6 μM cisplatin for 24 h, followed by incubation with 100 μM PA-d31 or OA-d34 for 6 h. Figure 7K shows quantification of the mean intensity of CD signals in MDA-MB-231 cells treated or not with cisplatin; n=6. P=0.0024. Figure 7L shows quantification of the fold change of CD signals in MDA-MB-231 cells treated or not with cisplatin; (n=6). Data in all bar graphs (Figures 7E, 7H, and 7K) are presented as mean ± SD.All statistical significance was analyzed using one-tailed Student's t-test. P=0.00029, 0.026, 0.0031, 0.016 and 0.0024; *P<0.05. **P<0.01. ***P<0.001. All scale bars: 20 μm.

[0023] [Figure 8] FIG. 8 shows cellular metabolic reprogramming from glycolysis to fatty acid oxidation in cisplatin-resistant ovarian cancer cells. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0024] It is to be understood that the described embodiments in this disclosure are not limited to particular methods, reagents, compounds, compositions or biological systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0025] definition Unless otherwise defined, all technical and scientific terms used in this disclosure, together with the appended claims, have the same meaning as commonly understood by one of ordinary skill in the art to which this subject belongs. As used in this disclosure and the appended claims, unless expressly stated to the contrary, the following definitions are set forth to provide the meaning and scope of the terms and to facilitate understanding of the invention.

[0026] As used in the context of this disclosure, the terms "a," "an," "the," and similar references include both the singular and the plural, unless otherwise indicated or clearly contradicted by context. All methods described herein can be performed in any suitable order, unless otherwise indicated or clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., "etc.") is intended merely to clarify the disclosure and does not pose limitations on the scope of the invention as otherwise claimed.

[0027] Additionally, as used herein, the term "about" when referring to a measurable value such as an amount, dose, time, temperature, and the like, is meant to encompass variations of ±20%, ±10%, ±5%, ±1%, ±0.5%, or even ±0.1% of the specified amount.

[0028] Also, as used herein, "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative ("or").

[0029] As used herein, the terms "comprising" or "comprises" are used in reference to compositions, methods, and their respective component(s) that are essential to the method or composition, but are open to the inclusion of unspecified elements, whether essential or not.

[0030] The term "consisting of" refers to the compositions, methods, and their respective components described herein, and excludes any element not recited in that description of an embodiment.

[0031] As used herein, the term "consisting essentially of" refers to those elements required for a given embodiment. The term permits the presence of additional elements that do not materially affect the basic and novel or functional characteristics of that embodiment of the invention.

[0032] As used herein, the terms "treat," "treatment," and "treating" refer to administering a compound, composition, agent, therapeutic, or pharmaceutical composition containing same for therapeutic purposes. As used herein, the terms "compound," "composition," "agent," "therapeutic," or "drug" used or useful in treatment may be used interchangeably.

[0033] As used herein, the term "cancer" refers to abnormal cells that can divide uncontrollably and spread or invade tissues or spread throughout the body, and to diseases or conditions involving such abnormal cells. As used herein, the term "cancer" may be used interchangeably with "tumor," "malignant tumor," and "malignant neoplasm."

[0034] As used herein, the term "benign neoplastic cells" refers to abnormal cells that divide more than normal and do not spread or invade tissues, and "benign neoplasm" refers to a collection or mass of abnormal cells that divide more than normal. A benign neoplasm is not cancer.

[0035] As used herein, the term "immune cell" refers to a cell that is part of the immune system and helps the body fight infection or other diseases or conditions.

[0036] Detailed Description of the Invention The present disclosure provides assays for determining resistance in a target cell or tissue to a treatment associated with cellular stress or perturbation, assays for determining metabolic reprogramming in a target cell or tissue to a treatment associated with cellular stress or perturbation, and methods of using the assays.

[0037] 1. Assays to determine resistance to a therapy in target cells or tissues. Increased aerobic glycolysis is widely considered a hallmark of cancer. Metabolic reprogramming in cancer cells is known to occur during the development of therapeutic drug resistance. However, the mechanism of cellular metabolic reprogramming during the development of therapeutic drug resistance to stress and the inhibition of aerobic glycolysis is unknown. As disclosed through chemical microscopy or spectroscopy, cells resistant to therapy-induced cellular stress have been found to exhibit increased uptake of exogenous fatty acids (FAs), which occurs concomitantly with decreased glucose uptake and de novo lipogenesis. The change or alteration to increased uptake of exogenous fatty acids, which occurs concomitantly with decreased glucose uptake and de novo lipogenesis, is an indication of reprogramming from glucose and glycolysis-dependent anabolism and energy metabolism to fatty acid uptake and beta-oxidation-dependent anabolism and energy metabolism. Mechanistically, increased fatty acid uptake promotes cell survival under therapy-induced cellular stress by enhancing energy production via beta-oxidation.

[0038] One aspect of the present disclosure is an assay for determining resistance to a treatment associated with cellular stress in a target cell or tissue, comprising measuring functional metabolic alterations in the target cell or tissue by chemical microscopy or spectroscopy, and determining metabolic indicators of resistance to the treatment in the target tissue. The functional metabolic alterations are a switch or change from glucose and glycolysis-dependent anabolism and energy metabolism to fatty acid uptake and fatty acid oxidation-dependent anabolism and energy metabolism. In an embodiment, the metabolic indicator provides the resistance level of the target cell to the treatment.

[0039] The metabolic index incorporates measurements of glucose-derived anabolism as well as fatty acid uptake and oxidation. The metabolic index is the ratio of increased fatty acid uptake and oxidation to decreased glucose-dependent anabolism in target cells, such as cancer cells. As exemplified, the ratio of fatty acid uptake to glucose-derived anabolism is defined as the "metabolic index." This ratio can provide a dimensionless number ranging from 0 to 1 as an index of the general formula fatty acid uptake / (fatty acid uptake + anabolism derived from glucose). For example, resistance to cisplatin in cancer cells, as quantitatively determined by the metabolic index, was shown to be improved by increasing deuterium-labeled palmitic acid-d in various cell lines. 31 (PA-d 31 ) and deuterium-labeled glucose-d7, i.e., PA-d 31 / (PA-d 31 + glucose-d7). This index was determined as the IC 50 (See FIG. 3C.) Similar metabolic indicators were determined from the increase in binding signal in various cells (Example 3, FIGS. 3D-3J).

[0040] In the present embodiment, metabolic indicator correlates with the level of resistance to treatment in target cells or tissue.In an embodiment, when metabolic alteration is the decrease of glucose and glycolysis-dependent anabolism, and the increase of fat uptake and fat oxidation-dependent anabolism and energy metabolism, metabolic indicator correlates with the resistance to treatment in target tissue.In some embodiments, metabolic indicator correlates linearly with the level of resistance to treatment in target cells or tissue.

[0041] In embodiments herein, the target cell is a cell that can undergo metabolic reprogramming or change in response to cellular stress, such as a cancer cell, an immune cell, or a benign neoplastic cell. In embodiments, the target cell is a cancer cell from any cancer, for example, ovarian cancer, prostate cancer, testicular cancer, bladder cancer, pancreatic cancer, lung cancer, breast cancer, esophageal cancer, head cancer, or neck cancer.

[0042] Fatty acid oxidation, as an alternative pathway for energy production, has been shown to be upregulated under certain conditions, such as under metabolic stress

[59] . However, whether and how fatty acid oxidation is deregulated in drug-resistant cells, such as cancer cells, is less known. As disclosed herein, cellular stress leads to increased fatty acid uptake and oxidation. For example, oxidative stress depletes intracellular antioxidants, such as NADPH, and thus inhibits de novo lipogenesis, which requires such antioxidants. A decrease in de novo lipid biosynthesis may result in reduced levels of malonyl-CoA, an allosteric inhibitor of CPT1, which may induce higher activity of CPT1 [60, 61]. The decreased de novo lipogenesis is at least partially responsible for the observed increase in fatty acid activity. In an embodiment, if the metabolic alteration is a decrease in de novo lipogenesis in the target cells or tissues, the metabolic indicators further correlate with resistance to treatment associated with cellular stress in the target cells or tissues.

[0043] In embodiments, measuring functional metabolic alterations using chemical microscopy includes measuring glucose and glycolysis-derived anabolism in the target cells, measuring fatty acid uptake and oxidation in the target cells, and determining a shift from glucose anabolism to fat uptake and oxidative energy metabolism in the target cells. In some embodiments, measuring functional metabolic alterations using chemical microscopy further includes measuring de novo lipogenesis in the target cells, and determining a shift from glucose and glycolysis-dependent anabolism and de novo lipogenesis to fatty acid uptake and oxidative energy metabolism in the target cells.

[0044] Another embodiment is an assay for determining resistance in a target cell or tissue resistant to a treatment associated with cellular stress or perturbation, the assay comprising measuring functional metabolic alterations in the target cells by measuring glucose-derived assimilation in and fatty acid uptake in the target cells by chemical microscopy, and determining a ratio of fatty acid uptake to glucose assimilation in the target cells to obtain a metabolic indicator of resistance for the target cells. In an embodiment, measuring functional metabolic alterations in the target cells using chemical microscopy comprises measuring glucose-derived assimilation in the target cells by chemical microscopy, and measuring fatty acid uptake in the target cells by chemical microscopy, where glucose-derived assimilation and optionally de novo lipogenesis are decreased and fatty acid uptake is increased. In some embodiments, the metabolic indicator correlates with the level of resistance to the treatment in the target cells. In some embodiments, the correlation is linear.

[0045] In embodiments herein, chemical microscopy is any microscopy or spectroscopy that provides single cell analysis. In embodiments, chemical microscopy is Raman scattering microscopy or infrared microscopy. In some embodiments, the Raman scattering microscopy may be spontaneous Raman scattering microscopy, surface-enhanced Raman scattering microscopy, or coherent Raman scattering microscopy. Coherent Raman scattering microscopy may be coherent anti-Stokes Raman scattering (CARS) or stimulated Raman scattering (SRS) microscopy. For example, SRS microscopy is a label-free chemical imaging technique that detects intrinsic chemical bond vibrations. To identify altered lipid metabolism in resistant cells, a high-throughput single cell analysis approach was used with large-area hyperspectral SRS scanning (see

[39] ). A stack of large-area hyperspectral SRS images was obtained, containing hundreds of individual cells in each field of view. (Figure 1A) SRS spectra were extracted at each pixel from the image stack. The hyperspectral SRS image was then segmented through a spectral phasor algorithm to generate maps of subcellular compartments corresponding to nuclei and lipids based on spectral similarity. The nuclei map was input into CellProfiler™ to guide the identification of the edges of each individual cell from the raw whole cell image. After delineating the individual cells, the lipid map was mapped to the corresponding cell. Finally, quantitative characterization of lipids in terms of integrated intensity, average intensity, area, and lipid droplet size in each individual cell was performed. Thus, the intrinsic chemical bond vibrations provide an indication of changes in cellular metabolism, e.g., lipid metabolism, in resistant cells. In some embodiments, the chemical microscopy used in the assay comprises hyperspectral stimulated Raman scattering imaging. In some embodiments, the chemical microscopy comprises hyperspectral stimulated Raman scattering imaging to provide high-throughput vibrational imaging.

[0046] In embodiments herein, the infrared microscopy may be mid-infrared photothermal (MIP) microscopy, or a microscopy based on direct infrared absorption, such as Fourier transform infrared (FTIR) microscopy or quantum cascade laser (QCL) microscopy. In some embodiments, the infrared microscopy is MIP microscopy.

[0047] In the embodiments herein, the cellular stress or perturbation can come from various sources that cause changes in cellular state. In some embodiments, the cellular stress is oxidative stress, metabolic stress, hypoxic stress, nutritional stress, thermal stress, genotoxic stress, or a combination thereof. In embodiments, the treatment induces cellular stress in the target cell or tissue.

[0048] In embodiments herein, the treatment induces metabolic reprogramming or metabolic changes or alterations in cells. In some embodiments, the treatment is a cancer treatment. In embodiments, the cancer treatment is selected from chemotherapy, radiation therapy, immunotherapy, targeted therapy, hormone therapy, light or laser therapy, photodynamic therapy, and combinations thereof. In embodiments, the cancer treatment is chemotherapy such as alkylating agents, e.g., platinum-based agents and nitrosoureas, antimetabolites, antitumor antibiotics, plant alkaloids, e.g., topoisomerase inhibitors and mitotic inhibitors, hormonal agents such as corticosteroids, and biological response modifiers. Platinum-based agents such as carboplatin and oxaliplatin are widely used chemotherapy agents for multiple types of cancer, including ovarian cancer, testicular cancer, bladder cancer, head and neck cancer, non-small cell lung cancer, and the like. Despite high response rates after initial treatment, the efficacy of platinum-based agents such as cisplatin and carboplatin is limited by severe side effects and a high probability of developing drug resistance [53, 54]. For example, the main mechanism of action of cisplatin is the formation of DNA adducts, which block transcription and DNA synthesis and simultaneously activate DNA damage response mechanisms and mitochondrial detoxification mechanisms. If DNA damage is not repaired and oxidative stress is not buffered, apoptosis eventually occurs. Many efforts have been made to elucidate the mechanisms of cancer cell resistance to cisplatin. Most of these studies focused on adduct formation and the subsequent activation of cell death pathways, e.g., reduced formation of DNA adducts due to altered uptake / excretion, enhanced DNA damage repair, or impaired mitochondrial apoptosis pathways after adduct formation

[54] . Other mechanisms of cisplatin resistance have received little attention. Studies have shown that cisplatin may have another mechanism of action by inducing oxidative stress in ovarian cancer [47, 55], prostate cancer

[56] , and lung cancer

[57] . Several studies have highlighted the link between metabolic reprogramming and cisplatin resistance. Alterations in the glycolytic pathway were associated with cisplatin-generated oxidative stress in head and neck squamous cell carcinoma

[58] .Lipid droplet production mediated by lysophosphatidylcholine acyltransferase 2 is associated with resistance to oxaliplatin in colorectal cancer

[24] . Adipocyte-induced FABP4 upregulation was found to mediate carboplatin resistance in ovarian cancer

[44] . As disclosed herein, a metabolic change or switch is found to occur in chemotherapy-resistant cancer cells from glucose-dependent anabolism and energy metabolism to fatty acid uptake and fatty acid oxidation to adapt to chemotherapy-induced oxidative stress.

[0049] In one embodiment, an assay for determining resistance in cancer cells with resistance to a treatment associated with cellular stress comprises measuring functional metabolic alterations in a target cell or tissue using chemical microscopy or spectroscopy, and determining a metabolic indicator of resistance to the treatment in the target tissue. In an embodiment, measuring functional metabolic alterations in the target cell or tissue using chemical microscopy comprises measuring glucose derived anabolism in the cancer cell using chemical microscopy, measuring fatty acid uptake in the cancer cell using chemical microscopy, and determining a ratio of fatty acid uptake to glucose anabolism in the cancer cell to provide a metabolic indicator of resistance in the cancer cell. In some embodiments, the metabolic indicator correlates with a level of resistance to the treatment in the cancer cell. In some embodiments, the cancer cell is selected from ovarian cancer, prostate cancer, testicular cancer, bladder cancer, pancreatic cancer, lung cancer, breast cancer, esophageal cancer, head cancer, and neck cancer. In some embodiments, the cellular stress is oxidative stress. In some embodiments, the treatment is a cancer treatment. In an embodiment, the cancer treatment induces a metabolic change in the cell.

[0050] In some embodiments herein, the cancer treatment is chemotherapy. In some embodiments, the chemotherapy is a platinum-based drug or therapeutic agent. In some embodiments, the platinum-based drug or therapeutic agent is selected from cisplatin, carboplatin, oxaliplatin, and nedaplatin, and combinations thereof.

[0051] Besides fatty acid oxidation, the process of fatty acid uptake represents a target for overcoming drug resistance. Regulation of fatty acid uptake involves multiple redundant transporters, binding proteins and carrier proteins [42, 43, 45, 62], and fatty acid uptake contributes to several mechanisms important for tumor survival and growth, including membrane biogenesis, fatty acid pool replenishment, and ER stress prevention [63, 64]. Apart from pointing to potential therapeutic strategies for therapy-resistant cancers, ex vivo quantitative metabolic imaging of anaerobic glycolysis, de novo lipogenesis and fatty acid uptake in tumor cells represents a novel functional marker for therapy responsiveness in clinical specimens at the single-cell level. Conventional methods for determining cell therapy resistance rely on cell viability assays or the measurement of various protein markers, which are time-consuming and lack precision. The disclosed metabolic imaging approach provides a rapid, functional and quantitative method for determining the resistance of target cells or tissues based on functional metabolic signatures in resistant cells.

[0052] 2. Methods for inhibiting resistance of target cells to therapy Another aspect of the present disclosure is the use of the disclosed assay in a method for treating or inhibiting resistance of a target cell or tissue to a treatment associated with cellular stress or perturbation. An embodiment includes a method for treating or inhibiting resistance of a target cell or tissue in a subject to a treatment associated with cellular stress by performing the assay disclosed herein to determine a metabolic indicator of resistance in the target cell or tissue to a treatment, administering at least one fatty acid oxidation inhibitor to the subject, and administering at least one therapy to the subject. In an embodiment, measuring functional metabolic alterations using chemical microscopy includes measuring glucose and glycolysis-dependent assimilation in the target cell, and measuring fatty acid uptake oxidation in the target cell, where glucose and glycolysis-dependent assimilation is decreased and fatty acid uptake and oxidation is increased. In an embodiment, the metabolic indicator of resistance to a treatment in the target cell further includes a decrease in de novo lipogenesis.

[0053] In an embodiment, the metabolic indicator is the ratio of the increase in fatty acid uptake and oxidation to the decrease in glucose-dependent anabolism in target cells. One embodiment is a method for treating or inhibiting resistance to treatment associated with cellular stress in target cells or tissues, comprising: measuring functional metabolic alterations from glucose- and glycolysis-dependent anabolism to fatty acid uptake, with glucose- and glycolysis-dependent anabolism decreasing and fatty acid uptake and oxidation increasing, using chemical microscopy; determining metabolic indicators of resistance of target cells or tissues to treatment; administering at least one inhibitor of fatty acid oxidation; and administering at least one therapy. In an embodiment, if the functional metabolic alterations are the decrease in glucose- and glycolysis-dependent anabolism, and the increase in fatty acid uptake and fatty acid oxidation-dependent anabolism and energy metabolism, the metabolic indicators are correlated with resistance to treatment in target tissues. In an embodiment, the metabolic indicators of resistance to treatment in target cells further comprise the decrease in de novo lipogenesis.

[0054] Another embodiment is a method of treating or inhibiting resistance to a therapy associated with cellular stress in a target cell or tissue in a subject, the method comprising: measuring a functional metabolic shift from glucose and glycolysis dependent assimilation to fatty acid uptake in the target cell or tissue using chemical microscopy, in which glucose and glycolysis dependent assimilation is decreased and fatty acid uptake and oxidation is increased; and determining a metabolic indicator or a ratio of decreased glucose and glycolysis dependent assimilation to increased fatty acid uptake and oxidation, which indicates resistance to the therapy in the target cell or tissue; administering at least one fatty acid oxidation inhibitor to the subject; and administering at least one therapy to the subject.

[0055] In embodiments, measuring functional metabolic alterations using chemical microscopy includes measuring glucose and glycolysis derived anabolism in the target cells using chemical microscopy, measuring fatty acid uptake and oxidation in the target cells using chemical microscopy, and determining an alteration from glucose and glycolysis derived anabolism to fatty acid uptake and oxidation energy metabolism in the target cells.

[0056] A further embodiment is a method of treating or inhibiting resistance of cancer cells to a treatment associated with cellular stress in a subject, the method comprising measuring glucose-derived assimilation in the cancer cells using chemical microscopy, measuring fatty acid uptake in the cancer cells using chemical microscopy, and determining the ratio of fatty acid uptake to glucose assimilation in the cancer cells to obtain a metabolic indicator, administering a fatty acid oxidation inhibitor to the subject, and administering a therapy to the subject. In an embodiment, administration may be in any form effective for treatment.

[0057] In some embodiments, the metabolic indicator correlates with the level of resistance of the cancer cells to the treatment. In some embodiments, the correlation is linear.

[0058] In an embodiment, the method further comprises obtaining cancer cells from the subject to perform the assay. The subject may be any mammal, for example, a human. The cancer cells may be from any cancer. In an embodiment, the cancer is selected from ovarian cancer, prostate cancer, testicular cancer, bladder cancer, pancreatic cancer, lung cancer, breast cancer, esophageal cancer, head cancer, and neck cancer.

[0059] In embodiments herein, the treatment induces metabolic changes or alterations in cells. In some embodiments, the treatment is a cancer treatment. In embodiments, the cancer treatment is selected from chemotherapy, radiation therapy, immunotherapy, targeted therapy, hormone therapy, light or laser therapy, photodynamic therapy, and combinations thereof. In some embodiments, the cancer treatment is a chemotherapy selected from alkylating agents, such as platinum-based agents and nitrosoureas, antimetabolites, antitumor antibiotics, plant alkaloids, such as topoisomerase inhibitors and mitotic inhibitors, hormonal agents such as corticosteroids, biological response modifiers, and combinations thereof. In some embodiments, the chemotherapy is a platinum-based agent or therapeutic agent selected from cisplatin, carboplatin, oxaliplatin, nedaplatin, and combinations thereof. In embodiments, administration may be in any form effective for treatment.

[0060] In embodiments, fatty acid oxidation is inhibited in cancer cells, and the treatment induces cellular stress in cancer cells, thereby inhibiting resistance to the treatment.In embodiments, at least one fatty acid oxidation inhibitor is a small molecule inhibitor or a gene perturbation (e.g., gene deletion, gene overexpression, insertion mutation), or a combination thereof.In embodiments, the inhibitor of fatty acid oxidation is selected from etomoxir, oxyphenicin, perhexiline, mildronate, trimetazidine, and combinations thereof.

[0061] In summary, a novel means for rapid detection of resistance to therapy at single cell level and a novel strategy for treating tumors resistant to therapy are disclosed. Through large-area chemical microscopic imaging and subsequent single cell analysis, a stable metabolic change or switch from glucose and glycolysis-dependent anabolism and energy metabolism to fatty acid uptake and fatty acid beta-oxidation-dependent anabolism and energy metabolism is shown. By combining metabolic flux through isotope labeling and microscopic molecular imaging, resistant cells show increased uptake of exogenous fatty acids accompanied by decreased glucose uptake and de novo lipogenesis. By incorporating the measurement of glucose-derived anabolism and fatty acid uptake with microscopic images, a "metabolic index" can be determined, which is defined as the ratio of fatty acid uptake to glucose uptake. The metabolic index correlates with the level of resistance to therapy in target cells such as cancer cells and primary human cells. This correlation demonstrates the feasibility of using microscopic or spectroscopic imaging for rapid detection of resistance to therapy in cancer cells ex vivo.

[0062] Mechanistically, resistant cells exhibit a higher rate of fatty acid oxidation, which provides additional energy and promotes cell survival under cellular stress. Blocking fatty acid oxidation with small molecule inhibitors or genetic perturbation in combination therapy, such as platinum-based therapy, synergistically suppresses cell proliferation in vitro and growth in patient-derived xenograft models in vivo. This further provides a new treatment option for patients with tumors resistant to therapies such as platinum-based therapy, such as cisplatin-resistant cells, by targeting the fatty acid oxidation pathway.

[0063] The described technology is further illustrated by the following examples, which should in no way be construed as further limiting. EXAMPLES

[0064] material and method The studies described complied with all relevant ethical regulations. Animal studies were approved by the Institutional Animal Care and Use Committee (IACUC) of Northwestern University and were performed at the Developmental Therapeutics Core (DTC) of the Lurie Cancer Center.

[0065] Glucose-d7, Palmitic acid-d 31 (PA-d 31 ), and oleic acid-d 34 (OA-d 34 ) were purchased from Cambridge Isotope Laboratory. 17-Octadecynoic acid (ODYA), BMS309403, cisplatin, and etomoxir were purchased from Cayman Chemicals. For cisplatin treatment, 3.3 μM was used as the final concentration unless otherwise specified.

[0066] cell line The ovarian cancer cell lines used in this method include SKOV3, PEO1, OVCAR5, and COV362, and their cisplatin-resistant counterparts include SKOV3-cisR, PEO4, OVCAR5-cisR, and COV362-cisR. SKOV3 (Cat. No.: HTB-77), Mia Paca2 (Cat. No.: CRL-1420), MDA-MB-231 (Cat. No.: CRM-HTB-26), and A549 (Cat. No.: CCL-185) cells were purchased from American Type Culture Collection (ATCC, Manassas, VA). PEO1 (Cat. No.: 10032308) and PEO4 (Cat. No.: 10032309) were purchased from Sigma Aldrich. OVCAR5 cells were a generous gift from Dr. Marcus Peter (Northwestern University) and COV362 cells were from Dr. Kenneth Nephew (Indiana University). All cell lines were authenticated and tested negative for mycoplasma. Mia Paca2 and A549 are on the list of known misidentified cell lines maintained by the International Cell Line Authentication Committee, but their authentication was done by ATCC through STR profiling. The resistant cell lines SKOV3-cisR, COV362-cisR, and OVCAR5-cisR were generated by treating with three or four repeated or increasing doses of cisplatin for 24 h. Surviving cells were allowed to recover for 3–4 weeks before receiving the next treatment. Changes in resistance to platinum were measured using the half maximal inhibitory concentration (IC 50) values ​​were estimated

[40] . PEO1, PEO4, OVCAR5, and OVCAR5-cisR cells were cultured in RPMI 1640 medium supplemented with 2 mM L-glutamine, 10% FBS, and 100 units / mL penicillin / streptomycin. SKOV3, SKOV3-cisR, COV362, and COV362-cisR and Mia Paca2 cells were cultured in high glucose DMEM medium supplemented with 10% FBS and 100 units / mL penicillin / streptomycin. MDA-MB-231 and A549 cells were cultured in Leibovitz's L-15 medium and Kaighn's Modification of Ham's F-12 medium supplemented with 10% FBS and 100 units / mL penicillin / streptomycin, respectively. For development of CPT1a knockdown cell lines, cells were incubated with CPT1a or control shRNA lentiviral particles (Sigma Aldrich, TRCN0000036282) for 48 h and selected with 1 μg / ml puromycin for 1 week. All cells were cultured at 37°C in a humidified incubator with 5% CO2 supply.

[0067] Primary human cells Non-specific high grade serous ovarian tumor (HGSOC) and malignant ascites fluid specimens from ovarian cancer (OC) patients were obtained at the time of either pre- or perioperative cytoreductive surgery (interval debulking surgery) at Northwestern University School of Medicine under an IRB-approved protocol (STU00202468). All patients were prospectively followed and received platinum and taxane standard of care chemotherapy. Platinum resistance was defined as disease recurrence within 6 months of completing carboplatin-based chemotherapy, as assessed clinically by CA125 criteria or CT scan. Tumor tissue was enzymatically dissociated into a single-cell suspension and cultured as previously described [65, 66]. After centrifugation at 200 g for 5 min, 25,000 ascites-derived tumor cells were cultured as a monolayer in DMEM medium supplemented with 10% FBS and antibiotics, followed by stimulated Raman scattering (SRS) imaging.

[0068] In vivo experiments Platinum-resistant PDX models were developed as previously described

[67] . After passage of donor animals, fresh tumors (equal size) were implanted subcutaneously (SC) into 20 female 7-8 week old NSG mice (Jackson Labs, Cat. No.: JAX:00555). Tumor size was measured twice weekly using calipers and tumor volume was calculated as length × width. 2 The tumor volume was calculated according to the formula: 3 When tumor size reached 1500 mm, animals were randomized into four groups: vehicle, carboplatin alone (10 mg / kg, weekly intraperitoneal (ip) injection), etomoxir alone (40 mg / kg, daily ip injection), and a combination of carboplatin (10 mg / kg, weekly ip injection) and etomoxir (40 mg / kg, daily ip injection). Body weight and behavior were monitored twice weekly, and tumor size was determined to be 1500 mm. 3 Mice were sacrificed when the body weight exceeded 100 mg / kg / day or if a humane endpoint was reached prematurely.

[0069] Xenografts were grown in female (6–8 weeks old) athymic nude mice (Foxn1 nuTumors were obtained by intraperitoneal implantation of 2 million OVCAR5 cells in 100-mL xenografts (Envigo, Sigma-Aldrich ... Red blood cell lysis was performed using RBC lysis buffer (BioLegend) followed by DNase (Qiagen) treatment and filtration through a 40 μm cell strainer (Fisher Scientific) to recover single cell suspensions, which were tested ex vivo for responsiveness to cisplatin.

[0070] For all animal experiments, mice were housed at 21°C–23°C with a 12 / 12 dark / light cycle. Humidity in the habitat was 35%. The maximum tumor size was 1500 mm 3 Mice were sacrificed when the nutrient intake exceeded 100 mg / kg / day or if the humane endpoint was reached prematurely. Mouse diet was catalog number is7912 from Teklad / Envigo.

[0071] Large-area hyperspectral stimulated Raman scattering imaging Hyperspectral SRS imaging was performed in a laboratory-built system according to previously published methods [8]. The laser source was a femtosecond laser (InSight™ DeepSee™, Spectra-Physics™, Santa Clara, CA, USA) operating at 80 MHz with two synchronized output beams, a tunable pump beam ranging from 680 nm to 1300 nm, and a Stokes beam fixed at 1040 nm. The laser source was a 100-nm tunable pump beam with a wavelength of 1000 nm, and a Stokes beam fixed at 1040 nm. The 100-nm tunable pump beam was focused on the CH vibration region (2800–3050 cm). -1 The pump beam was tuned to 798 nm for imaging with a 300 nm AF 1000 nm laser. The Stokes beam was modulated at 2.3 MHz by an acousto-optic modulator (1205-C, Isomet®). After being combined, both beams were chirped by two 12.7 cm long SF57 glass rods and then sent to a laser scanning microscope. The powers of the pump and Stokes beams before observation were controlled at 20 mW and 200 mW, respectively. A 60x water immersion objective (NA=1.2, UPlanApo / IR™, Olympus) was used to focus the light onto the sample, and an oil condenser (NA=1.4, U-AAC, Olympus) was used to collect the signal. For hyperspectral SRS imaging, 50 image stacks were acquired at different pump-Stokes time delays, which were controlled by tuning the optical path difference between the pump and Stokes beams through a translation delay stage. The Raman shifts were calibrated using standards containing DMSO, oleic acid, and linoleic acid.

[0072] To achieve large-area mapping, the samples were fixed on a motorized stage (PH117, Prior Scientific). A laboratory-built LabView-based program was used to control the stage movement and image stitching. After a hyperspectral SRS image was acquired at the current position, the stage was moved to an adjacent position with partial overlap. Montage images consisting of 5 × 5 individual 400 × 400 pixel images were acquired for each region of interest. The size of the montage images is approximately 500 × 500 μm. The pixel dwell time was set to 10 μs. For each sample, at least three montage images were acquired with different regions of interest.

[0073] Single-cell analysis based on Spectral Phasor and CellProfiler™ The acquired large-area hyperspectral SRS images were segmented through spectral phasor analysis, modified from a previously published method

[39] . Spectral phasor was installed as a plugin in ImageJ. Images were converted to two-dimensional phasor plots based on the Fourier transform. Each dot on the phasor plot represents the SRS spectrum at a particular pixel. Pixels with similar spectral or chemical contents were clustered on the phasor plot. The "nuclear" and "lipid" images were generated by mapping corresponding clusters on the phasor plot back to two separate images.

[0074] Lipid analysis in single cells was performed via the software CellProfiler™

[68] . Nuclear maps and cell images were input into CellProfiler™ to outline individual cells. Lipid maps were input into CellProfiler™ to pick up lipid droplet (LD) particles. The lipid map was then masked onto the outlined cell map to label lipids. Morphological information of each cell and lipid analysis, including LD number and intensity in single cells, were measured and reported in the output results. Total lipid intensity in each cell was plotted as a histogram graph. For each sample, hundreds to thousands of cells were analyzed.

[0075] Isotope labeling and SRS imaging For labeling with glucose-d7, cells were seeded overnight on 35 mm glass-bottom dishes, and then the medium was replaced with glucose-free DMEM medium (Thermo Fisher Scientific, #11966025) + 10% FBS + P / S supplemented with 25 mM glucose-d7. FA or analogs (PA-d 31 , OA-d 34 For labeling with FA, FA or analogs were added to the culture medium at a final concentration of 100 μM, and cells were treated for 6 h. For quantitative SRS imaging, cells on glass-bottom dishes were fixed with 10% neutral buffered formalin for 30 min and washed three times with PBS. -1 We performed hyperspectral SRS imaging of cells in the Raman spectral range.

[0076] Measurement of reactive oxidizing species Cellular reactive oxidative species (ROS) were measured using a fluorescent probe, 2',7'-dichlorofluorescein diacetate (DCFDA) (Sigma Aldrich). Cells seeded on glass-bottom dishes were treated with or without 3.3 μM cisplatin for 3 h. DCFDA was added to the medium at a final concentration of 10 μM and incubated for 15 min. After washing three times with PBS, cells were immediately imaged on a confocal microscope (Zeiss LSM 700microscope) with 488 nm as the excitation source. The laser power was controlled at a low setting to avoid fluorescence quenching. Approximately 10 fields of images were acquired for each sample.

[0077] Fatty acid oxidation assay Fatty acid oxidation (FAO) was measured using a commercial kit (Abcam, #ab217602) following the provided protocol. Briefly, cells were seeded at 150k cells / well in 96-well plates. After overnight incubation, the medium was changed. 10 μL of extracellular O2 consumption reagent and 2 drops of high sensitivity mineral oil (pre-warmed at 37°C) were added to each well. Fluorescence was measured at excitation / emission = 380 / 650 nm in a plate reader at 2 min intervals for 180 min. Etomoxir was added to a final concentration of 40 μM to block FAO. Oxygen consumption rate (OCR) was calculated as Δ 蛍光強度 FAO rate is expressed as OCR / min / cell FAO =OCR total -OCR Etomoxir At least three replicates were included for each measurement.

[0078] NADPH and ATP assays NADP / NADPH and ADP / ATP were measured by using commercially available kits (Abcam, #ab65349 and #ab65313). For NADPH measurement, cells (approximately 1 × 10 6Cells) were pelleted and extracted using NADPH / NADP extraction buffer. Total NADP / NADPH was measured directly using an assay kit, and NADPH alone was measured after decomposing NADP by heating at 60°C for 30 min. Absorbance at 450 nm was measured by a plate reader (Molecular Devices, SpectraMax i3x). For ATP measurements, cells were plated in 96-well plates. ATP was measured directly and total ATP+ADP was measured by converting ADP to ATP. Luminescence signal was measured by a plate reader. At least three replicates were included for each measurement.

[0079] Cell viability assay Cell viability was measured by MTS assay (Abcam, #ab197010) or CellTiter-Glo™ assay (Promega, #G7570). Cells were seeded overnight in 96-well plates at a density of 2000-5000 cells per well. Treatments were applied to cells at the indicated concentrations for 72 hours. Cell viability was measured by incubating with MTS reagent for 4 hours and reading absorbance at 490 nm or with CellTier-Glo™ reagent for 10 minutes and reading luminescence by a plate reader. Six replicates were used for each group.

[0080] Glucose uptake assay Glucose uptake was measured using the fluorescent glucose analog 2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl)amino]-D-glucose (2-NBDG) (Cayman Chemicals). Cells seeded on glass-bottom dishes were incubated with 100 μM 2-NBDG for 2 h. Fluorescence images were taken by confocal microscopy (Zeiss® LSM 700 microscope) using a 488 nm laser as the excitation source. Approximately 10 fields of images were acquired for each sample.

[0081] Seahorse® Analysis for OCR and ECAR Measurements Cell lines were plated at 6 × 10 per well in Seahorse® XF96 Cell Culture Microplates (Agilent). 4 (OVCAR5 vs.) or 4×10 4 (vs. PEO). After overnight incubation at 37°C for cell attachment, OCR and extracellular acidification rate (ECAR) were measured by Seahorse® XFe96 Analyzer (Agilent). Measurement time was 30 s after 3 min mixing and 30 s waiting time. The first three cycles were used for basal respiration measurement. The effects of mitochondrial respiration inhibitors, 4 μM oligomycin, 4 μM FCCP, 25 μM rotenone, 50 μM antimycin A, 26.4 μM cisplatin, or 40 μM etomoxir, on OCR and ECAR of OC cells were measured. Basal respiration, ATP production, and maximum respiration reduction were calculated according to the manufacturer's instructions.

[0082] Reverse transcription PCR (RT-PCR) Total RNA from ovarian cell lines was extracted via RNeasy® Mini Kit (Qiagen Inc.) and reverse transcribed by iScript® cDNA Synthesis Kit (Bio-Rad). RT-PCR was performed by StepOne Plus RT-PCR (Applied Biosystems) using Power SYBR Green Master Mix (Thermo Fisher Scientific). Primer sequences, SEQ ID NOs: 1-24, are listed in Table 1. All procedures were according to the manufacturer's instructions.

[0083] Table 1. Primer sequences used for RT-PCR measurements [Table 1]

[0084] RT-PCR reactions generated melting curves and cycle threshold values ​​(Ct) were recorded for genes of interest and housekeeping control genes (PPIA). Relative RNA expression levels were calculated as ΔCt and normalized by subtracting the Ct value of the target gene from the Ct value of the control gene. Results are shown as mean ± SD. Measurements were performed in biological triplicates, with each biological replicate containing three technical replicates.

[0085] Western blot Proteins were extracted from cell cultures with RIPA lysis buffer (Sigma Aldrich) containing protease and phosphatase inhibitor cocktail and sample reducing agent (Thermo Fisher Scientific). Proteins were separated in Bolt™ Bis-Tris Plus gels (Thermo Fisher Scientific) via gel electrophoresis and transferred to PVDF membranes (Bio-Rad). After blocking in 5% nonfat milk (Bio-Rad) for 1 h at room temperature, the membrane was incubated with primary antibodies (CPT1a (1:1000) (Proteintech Catalog No.: 15184-1-AP; RRID: AB_2084676) and GapDH (1:2000) (Proteintech; Catalog No.: 60004-1-Ig; RRID: AB_2107436; Clone No.: 1E6D9) overnight at 4 °C, followed by incubation with secondary anti-mouse antibody (1:10000) (Proteintech; Catalog No.: SA00001-1; RRID: AB_2722565) for 1 h at room temperature. Protein bands were developed by ECL reagent (Thermo Fisher Scientific) and detected by ChemiDoc MP Imaging System (Bio-Rad). Band intensity was determined using ImageJ. Full scan blots are in the source data file (not shown).

[0086] RNA sequencing analysis RNA-seq data from OVCAR-5 and SKOV-3 cisplatin-resistant vs. parental cells were downloaded from Gene Expression Omnibus with accession ID: GSE148003

[40] . Raw data were normalized with the R package edgeR

[69] . Overlapping genes in the hallmark fatty acid metabolism gene set between OVCAR5-cisR vs. parental cells and SKOV3-cisR vs. parental cells were used to generate heatmaps using the R package heatmap. Specifically, a heatmap of hierarchical clustering was generated for OVCAR5-cisR vs. parental cells by using normalized counts. The same gene order after hierarchical clustering was applied to generate the heatmap for SKOV3-cisR vs. parental cells.

[0087] Quantification and statistical analysis All data are presented as mean ± SD unless otherwise specified. Statistical significance was analyzed using a two-tailed Student's t-test. All experiments were repeated at least three times. N indicates the sample size for each experiment. P<0.05 was considered statistically different. Statistical parameters can be found in the figure legends. Data were analyzed and qualified by ImageJ, MATLAB, and Microsoft Excel. The origin was used for figure creation.

[0088] Data availability The RNA-seq data used in this paper are available in the Gene Expression Omnibus under the accession ID: GSE148003 [https: / / www.ncbi.nlm.nih.gov / geo / query / acc.cgi?acc=GSE148003].

[0089] Example 1 High-throughput SRS imaging reveals lipid accumulation in platinum-resistant ovarian cancer cells

[0090] To identify altered lipid metabolism in cisplatin-resistant cells, we established a high-throughput single-cell analysis approach that combines large-area hyperspectral SRS scanning of 200–500 cells per group with spectral phasor segmentation and CellProfiler™ analysis. As shown in Figure 1A, we acquired stacks of large-area hyperspectral SRS images containing hundreds of individual cells in each field of view. SRS spectra were extracted at each pixel from the image stack. We then segmented the hyperspectral SRS images via a spectral phasor algorithm to generate maps of subcellular compartments corresponding to nuclei and lipids (mostly in lipid droplets) based on their spectral similarity

[39] . The nuclei maps were then input into CellProfiler™ to guide the identification of the edges of each of the individual cells from the raw whole-cell images. After delineating the individual cells, the lipid maps were mapped to the corresponding cells. Lipids were color-coded based on the color of their parent cells. Finally, a quantitative characterization of lipids in terms of integrated intensity, mean intensity, area, and lipid droplet size in each individual cell was performed. The image area is 500 μm x 500 μm.

[0091] To explore the lipid metabolism signature of cisplatin-resistant OC cells, isogenic pairs of cisplatin-resistant cells from three OC cell lines, including SKOV3, OVCAR5, and COV362, were cultured using IC 50 Resistance to cisplatin in these cell lines was verified by a repeated assay measuring the cisplatin dose response. All resistant cell lines showed IC 50 showed a 2- to 3-fold increase in IL-1 expression (Figure 1B-E). Furthermore, isogenic PEO1 / PEO4 cell lines derived from the same patient were studied at the time of platinum-sensitive (PEO1) and platinum-resistant relapse (PEO4)

[41] .

[0092] Using a high-throughput imaging analysis platform, we analyzed lipid metabolism in these four pairs of cisplatin-resistant and parental ovarian cancer cell lines. Comparison of SRS images of sensitive PEO1 and cisplatin-resistant PEO4 cells showed an increase in lipid intensity in PEO4 cells, but with a large cell-to-cell variation (Figure 1F). The integrated lipid intensity in individual cells was quantitatively analyzed and plotted in histograms. The histograms showed two distinct subpopulations in each cell line, a lipid-poor subpopulation and a lipid-rich subpopulation, indicating metabolic heterogeneity within the same group. While lipid-poor cells predominated in the PEO1 cell line, PEO4 cells showed a dramatic increase in the lipid-rich subpopulation and a decrease in the lipid-poor subpopulation (Figure 1G). Single-cell analysis revealed an even more obvious increase in the lipid-rich subpopulation and a decrease in the lipid-poor subpopulation in SKOV3-cisR cells compared to SKOV3 (Figures 1H and 1I). Moreover, similar lipid content changes were observed in two other pairs of cell lines, OVCAR5 vs. OVCAR5-cisR (Figure 1J) and COV362 vs. COV362-cisR (Figure 1K), confirming that cisplatin-resistant cells have higher levels of lipid accumulation. Furthermore, after acute treatment with cisplatin, a significant increase in lipid-rich subpopulations and a decrease in lipid-poor subpopulations were found in SKOV3 cells (Figure 1L), while no obvious changes in lipid distribution patterns were detected in SKOV3-cisR cells (Figure 1M), confirming that lipid-rich cells are more resistant to cisplatin treatment.

[0093] To determine whether lipid accumulation also occurs in vivo in platinum-treated tumors, SRS imaging of lipids was performed in OVCAR5 xenografts collected from mice treated weekly for 3 weeks with saline or carboplatin. Tumor growth was inhibited by carboplatin treatment (Figure 1N). However, cells isolated from xenografts remaining after carboplatin treatment showed increased resistance to carboplatin in in vitro treatment compared to cells isolated from saline-treated tumors (Figure 1O). The results shown in Figure 1P-Q indicate heterogeneous lipid accumulation and higher lipid amounts in carboplatin-treated tumors compared to platinum-sensitive tumors. Taken together, increased lipid content was a metabolic hallmark of cisplatin-resistant ovarian cancer cells.

[0094] Example 2 Increased fatty acid uptake, but not de novo lipogenesis, contributes to high levels of lipid content in cisplatin-resistant ovarian cancer cells

[0095] To identify the cause of the increased lipid content in cisplatin-resistant ovarian cancer cells, the contributions of de novo lipogenesis and fatty acid uptake, respectively, were investigated. The level of lipogenesis was examined by feeding cells with deuterium-labeled glucose-d7 using a stable isotope probing technique

[35] . Newly synthesized macromolecules (mainly lipids) were stimulated with deuterium-labeled glucose-d7 at 2050 cm, which covers the vibrational frequency of the C-D bond. -1 ~2350cm -1 The CD signals were imaged by hyperspectral SRS microscopy at Raman shifts of 100-1500 nm. SRS images showed a weaker CD signal in cisplatin-resistant PEO4 cells than in parental PEO1 cells (Figure 2A). Quantitative analysis confirmed a significant decrease in both signal intensity and relative area fraction in PEO4 cells when compared to PEO1 cells (Figure 2B), indicating a decrease in glucose-derived anabolism and de novo lipogenesis in cisplatin-resistant cells. Using a similar approach, fatty acid uptake was measured using deuterium-labeled palmitic acid-d 31 (PA-d 31) fed cells. In contrast to glucose-d7 fed cells, PA-d 31 The CD signal in PEO4 cells fed with PA-d was stronger than that in PEO1 cells (Figure 2C). Quantitative analysis revealed a significant increase in both signal intensity and relative area ratio (Figure 2D). 31 ), as well as the unsaturated fatty acid oleic acid-d 34 (OA-d 34 ) uptake in PEO4 cells was examined compared to PEO1 cells. 34 Uptake was significantly increased (Figures 2E and 2F). These results indicate that the increase in fatty acid uptake is not specific to certain fatty acids, but rather reflects a general upregulation of fatty acid uptake pathways.

[0096] To verify whether the observed phenomenon was cell type specific, we repeated the measurements in SKOV3 and SKOV3-cisR cells. Consistently, SRS images and quantitative analysis showed a significant reduction in glucose-d7-derived CD signals in SKOV3-cisR cells when compared to parental SKOV3 cells (Figure 2G), as well as a significant reduction in the PA-d signal in SKOV3-cisR cells. 31 Signal (Figure 2H) and OA-d 34 The results showed an increase in the signal (Figure 2I). Moreover, the same trend was observed in two other pairs of cell lines, OVCAR5 vs. OVCAR5-cisR (Figure 2J) and COV362 vs. COV362-cisR (Figure 2K). Furthermore, inhibition of de novo lipogenesis by the FASN inhibitor C-75 did not affect the increase in lipid content in SKOV3-cisR compared to SKOV3, indicating that the increase in lipid amount in cisplatin-resistant cells is independent of de novo lipogenesis (Figures 2L and 2M). These data collectively suggest a metabolic shift from glucose-derived anabolism to fatty acid uptake in cisplatin-resistant ovarian cancer cells.

[0097] Example 3 Metabolic parameters as predictors of cisplatin resistance

[0098] Having demonstrated a decrease in glucose-derived anabolism and an increase in fatty acid uptake in cisplatin-resistant ovarian cancer cells, we investigated whether this metabolic feature could be used to differentiate cisplatin-resistant cancer cells from cisplatin-sensitive cancer cells. To quantitatively characterize resistance to cisplatin, we measured the IC of cisplatin in various cell lines. 50 Dose and glucose-d7, PA-d 31 , or OA-d 34 The CD intensities (shown as area percentages) of these compounds were calculated (Table 2). In Table 2 below, glucose-d7 (G-d7), PA-d 31 , and OA-d 34 Quantification is shown as the average area percentage of CD signal from the total cell area.

[0099] Table 2. Glucose-d7, PA-d in four pairs of parental and cisplatin-resistant ovarian cancer cells 31 , and OA-d 34 , as well as IC of cisplatin. 50 Summary of quantitative results. [Table 2]

[0100] Interestingly, the CD intensity from glucose-d7 was significantly higher than the IC 50 was found to be negatively correlated with PA-d (Figure 3A). 31 Strength is IC 50 To integrate the two measurements into one, we used the PA-d 31 / (PA-d 31 The ratio of β-glucose to β-d7 was used to obtain a dimensionless number ranging from 0 to 1. This ratio was defined as the "metabolic index." This index is a measure of the IC 50 (Figure 3C), providing the ability to detect and quantitatively determine resistance to cisplatin in cancer cells.

[0101] Realizing the value of this metabolic imaging method for detecting cisplatin resistance at the single cell level, we applied hyperspectral SRS imaging to simultaneously measure glucose-derived assimilation and fatty acid uptake in the same cells cultured with Raman probes for fatty acids and glucose. Specifically, glucose-d7 was used to track glucose assimilation [35, 36]. Instead of using deuterium-labeled fatty acids to track fatty acid uptake, we used a fatty acid analog, 17-octadecynoic acid (ODYA). ODYA has an endogenous C≡C at one end of the FA chain, which has a C bond at 2100 cm. -1 This produces a strong Raman peak near the nucleus (Figure 3D). The unique Raman spectrum of ODYA allows for the spectral separation of C≡C-labeled fatty acids (from fatty acid incorporation) from CD-labeled macromolecules derived from glucose-d7 (Figure 3D). To test this in a biological environment, we performed hyperspectral SRS imaging in cells fed both ODYA and glucose-d7. Two signals with characteristic spectra were observed, one from the C≡C-labeled fatty acids and the other from the CD-derived glucose-d7 (Figure 3E).

[0102] We then applied this approach to image OVCAR5 and OVCAR5-cisR cells. The two components C≡C and CD are expressed using the spectral phasor algorithm 39The C≡C and CD signals were segmented from the raw SRS images based on the CTAB-based ... 50 A linear correlation was established between (Figure 3J).

[0103] To further validate metabolic indices as predictors of platinum resistance in clinically relevant samples, this method was applied to primary ovarian cancer cells obtained from de-identified consented patients for whom data on resistance / response to platinum were available. Patient characteristics are shown in Table 3 below. Tumor specimens were obtained at the time of cytoreductive surgery either preoperatively (n=4) or after neoadjuvant chemotherapy (n=7). Platinum resistance was defined as disease recurring within 6 months of completing chemotherapy with carboplatin, as assessed clinically by CA125 criteria or CT scan. (n=11 patients).

[0104] Table 3: Patient characteristics of primary cells used for metabolic index calculations. [Table 3]

[0105] As shown in Figure 3K, in ovarian cancer cells isolated from patients with platinum-sensitive disease, the signal from ODYA was observed in only a portion of the cells, whereas the signal from glucose-d7 was relatively strong. In ovarian cancer cells isolated from cisplatin-resistant tumors, the ODYA signal was more evenly distributed in the imaged cells, whereas the signal from glucose-d7 was weaker. Quantitative analysis showed that metabolic indices were higher in samples from four patients with resistant tumors when compared with cancer cells from seven patients with sensitive disease (Figure 3L). The histogram of metabolic indices data showed a clear separation between the sensitive and resistant groups (Figure 3M). Receiver operating characteristic (ROC) analysis yielded a threshold of 0.412, with high sensitivity as 1, high specificity as 1, and high AUC (area under the curve) as 1, suggesting that the metabolic indices have a high probability of successfully distinguishing platinum-sensitive from platinum-resistant ovarian cancer cells (Figure 3N). This study demonstrates the clinical applicability of metabolic imaging to predict response / resistance to platinum.

[0106] Example 4 Fatty acid uptake contributes to cisplatin resistance

[0107] Since cisplatin-resistant ovarian cancer cells have been found to take up more fatty acids, we investigated whether fatty acid uptake was a cause or consequence of cisplatin resistance. First, we tested whether modulating exogenous fatty acid availability would affect endogenous lipid amounts in cisplatin-resistant ovarian cancer cells. OVCAR5-cisR cells were cultured in lipid-depleted medium or normal medium supplemented with 1% lipid mixture for 24 h, and then lipid amounts were examined by SRS microscopy. Lipid depletion significantly reduced intracellular lipid amounts, whereas lipid supplementation increased intracellular lipid amounts (Figures 4A and 4B). A similar phenomenon was observed in the SKOV3-cisR cell line (Figures 4C and 4D). These observations further support that fatty acid uptake, instead of de novo lipogenesis, was the major source of lipid accumulation in cisplatin-resistant ovarian cancer cells.

[0108] We next investigated whether modulating the availability of exogenous lipids affected the resistance of cancer cells to cisplatin. In OVCAR5-cisR (Figure 4E), PEO4 (Figure 4F), and SKOV3-cisR cells (Figure 4G), lipid depletion increased sensitivity to cisplatin, whereas lipid supplementation slightly decreased sensitivity to cisplatin. To confirm whether ovarian cancer cells upregulate glucose metabolism in fatty acid-depleted environments, we measured glucose uptake in SKOV3 and SKOV3-cisR cells cultured in normal or lipid-depleted medium using the fluorescent glucose analog 2-NBDG. Glucose uptake remained similar in lipid-sufficient and lipid-deficient environments (Figure 4H and 4I). Thus, resistance to cisplatin can be alleviated by modulating the availability of exogenous fatty acids.

[0109] Fatty acid uptake is a cellular process facilitated by multiple fatty acid transporters / carriers, including CD36, FATP, and FABP [42, 43]. One of the key proteins reported to be upregulated in ovarian cancer is fatty acid binding protein 4 (FABP4) [12, 44]. We evaluated whether the increase in fatty acid uptake in cisplatin-resistant ovarian cancer cells is regulated through the upregulation of FABP4 and showed very low FABP4 mRNA levels in OVCAR5 and OVCAR5-cisR cells, suggesting that FABP4 likely does not play a major role in mediating the increase in fatty acid uptake in cisplatin-resistant OVCAR5 cells. We then investigated the expression of a panel of other fatty acid uptake regulator genes, including CD36, FATP1-6, FABP5, and GOT2 (FABP(PM))

[45] , and found that the expression of FABP5 and FABP(PM) was higher than the other genes (Figure 4J). Repeated experiments confirmed significant upregulation of FABP5 and FABP(PM) in resistant OVCAR5-cisR (Figure 4K-L) and PEO4 cells (Figure 4M) compared with their parental cells, suggesting that FABP5 and FABP(PM) may mediate fatty acid uptake in cisplatin-resistant cells. Furthermore, cisplatin treatment induced a sharp increase in FABP5 and FABP(PM) expression in cisplatin-sensitive cells OVCAR5 (Figure 4N), further supporting the involvement of FABP5 and FABP(PM) in cisplatin resistance-related fatty acid uptake. In contrast, the mRNA expression level of glucose transporter GLUT1 was decreased in resistant SKOV3-cisR compared with parental cells (Figure 4O). In OVCAR5 cells, GLUT1 expression did not change significantly after cisplatin treatment, suggesting that downregulation of GLUT1 may be an adaptive change in cisplatin-resistant cells rather than an acute response to cisplatin treatment (Figure 4P). To exclude the possibility that the increase in fatty acid uptake was caused by changes in membrane fluidity at high concentrations of exogenous fatty acids, SRS imaging of fatty acid uptake was performed in mice treated with lower concentrations of PA-d. 31and OA-d 34 , which showed a similar trend of increased fatty acid uptake in the resistant cells (Figure 4Q-T). These data support that the increased lipid uptake in cisplatin-resistant ovarian cancer cells is transporter-mediated and likely results from adaptive metabolic reprogramming in response to cisplatin treatment.

[0110] We next tested whether BMS309403 (BMS), a potent inhibitor of FABP, could suppress fatty acid uptake in cisplatin-resistant cells

[46] . Treatment with BMS suppressed the uptake of PA-d in OVCAR5-cisR cells. 31 BMS significantly reduced fatty acid uptake (Figure 4U and 4V). Suppression of fatty acid uptake by BMS was also observed in SKOV3-cisR cells in a dose-dependent manner (Figure 4W and 4X). Furthermore, inhibition of fatty acid uptake by BMS reduced resistance to cisplatin in multiple resistant cell lines, including PEO4 (Figure 4Y), SKOV3-cisR (Figure 4Z), and OVCAR5-cisR cells (Figure 4AA), whereas the effect of BMS was less evident in sensitive cell lines (Figure 4BB-Figure 4DD). These results support the deregulation of fatty acid uptake in the development of cisplatin resistance in OC cells.

[0111] Example 5 Increased fatty acid oxidation rate contributes to cisplatin resistance

[0112] Considering that one major function of lipids is energy production through fatty acid oxidation, we investigated whether fatty acid oxidation was increased in cisplatin-resistant cancer cells. By measuring OCR in parental and resistant cancer cells, OVCAR5-cisR cells showed a much higher level of oxygen consumption than OVCAR5 cells (Figure 5A). Etomoxir, an inhibitor of CPT1, which transports fatty acids to mitochondria for fatty acid oxidation, was used to test whether the increase in oxidation rate results from fatty acid oxidation. Etomoxir did not induce obvious changes in oxygen consumption in OVCAR5 cells (Figure 5B), but significantly decreased oxygen consumption in OVCAR5-cisR cells (Figure 5C). Quantification of OCR confirmed that OCR was significantly decreased after etomoxir treatment in OVCAR5-cisR cells, but not in OVCAR5 cells (Figure 5D). Fatty acid oxidation was also measured by Seahorse® FAO assay to evaluate etomoxir-induced mitochondrial respiration changes. As shown in Figure 5E, OVCAR5-cisR cells had an overall higher OCR than parental cells and showed a clear decrease in OCR after treatment with etomoxir. In contrast, the OCR of OVCAR5 cells was less sensitive to etomoxir treatment. The etomoxir-induced decrease in basal respiration, ATP production, and maximum respiration in resistant OVCAR5-cisR cells was significantly higher than that in sensitive OVCAR5 cells, suggesting a significant upregulation of fatty acid oxidation in resistant cells (Figure 5F). Furthermore, Seahorse® measurements of OCR in PEO1 and PEO4 cells confirm a significant increase in fatty acid oxidation rates in PEO4 cells compared to PEO1 cells (Figure 5G). These data indicate that FAO is significantly increased in cisplatin-resistant cells.

[0113] To test whether increased fatty acid oxidation contributes to cisplatin resistance, we examined the response to etomoxir in cisplatin-resistant and parental cells. Higher sensitivity to etomoxir treatment in cisplatin-resistant cell lines was observed in paired cell lines, including PEO1 and PEO4 (Figure 5H), OVCAR5 and OVCAR5-cisR (Figure 5I), and COV362 and COV362-cisR (Figure 5J), when compared to their parental cell lines, indicating a higher dependency on fatty acid oxidation in cisplatin-resistant cells. We next tested whether etomoxir treatment could reduce resistance to cisplatin. The dose-response curves to cisplatin were significantly shifted to the left by etomoxir treatment in PEO4 (Figure 5K), OVCA5-cisR (Figure 5L), and COV362-cisR cells (Figure 5M), supporting the possibility that etomoxir resensitizes resistant ovarian cancer cells to cisplatin. This observation was further confirmed by shRNA-mediated knockdown of CPT1a in OVCAR5-cisR cells (Figure 5N and 5O). Knockdown of CPT1a in OVCAR5-cisR cells increased their sensitivity to cisplatin treatment compared to the control group (Figure 5P). Interestingly, CPT1a mRNA and protein levels were similar in OVCAR5 and OVCAR5-cisR cells (Figure 5Q and 5R). This indicates that enhanced fatty acid oxidation in cisplatin-resistant ovarian cancer is likely the result of increased activation (not upregulation) of CPT1a.

[0114] To determine whether the functional alterations were caused by transcriptional reprogramming, we compared resistant and parental OVCAR5 and SKOV3 cells by RNA sequencing. Heatmaps show hierarchical clustering of genes related to FA metabolism, indicating a clear separation between sensitive / resistant cells (Figure 5S and 5T). In both cell lines, several FAO-related genes, including CRAT, PPARA, ACOT8, HSD17B10, ACADVL, ACOX1 and DECR1, were upregulated in resistant cells, whereas FA synthesis-related genes, such as ME1, NSDHL, DHCR24, FASN, ELOVL5, ALDH3A2, ACSL4 and SERINC1, were downregulated (Figure 5S and 5T).

[0115] These transcriptomic findings indicate increased fatty acid oxidation activity and decreased de novo fatty acid synthesis in cisplatin-resistant ovarian cancer.

[0116] Using a patient-derived xenograft (PDX) model that was rendered platinum-resistant by repeated exposure to carboplatin in vivo as previously described

[40] , we tested whether interference with fatty acid oxidation could sensitize ovarian tumors to platinum in vivo. To avoid cisplatin-induced toxicity, cisplatin was replaced by the second-generation drug carboplatin. Single-agent treatment with carboplatin or etomoxir induced a slight decrease in tumor growth, whereas combination treatment caused a significant inhibition of tumor growth (Figure 5U). Body weight remained stable in all groups, suggesting that the combination treatment was well tolerated (Figure 5V). These data collectively support the development of a combination of platinum with fatty acid oxidation inhibitors for the treatment of platinum-resistant cancers.

[0117] Example 6 FAO promotes cancer cell survival under cisplatin-induced oxidative stress

[0118] In addition to DNA adduct formation, cisplatin is known to cause cytotoxicity by inducing oxidative stress [47–49]. Excessive oxidative stress can inhibit glycolysis by inactivating key glycolytic enzymes such as pyruvate kinase M2 (PKM2) and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) [15, 50]. Since NADPH is one of the precursors of lipogenesis, increased reactive oxidative species (ROS) oxidize intracellular NADPH and thus suppress de novo lipogenesis

[51] . Thus, fatty acid uptake and oxidation can promote cancer cell survival under cisplatin-induced oxidative stress by replenishing free fatty acids and ATP, the deficiency of which is caused by the decrease in de novo lipogenesis and glycolysis under oxidative stress. As a test, oxidative stress levels were examined by measuring intracellular ROS using a fluorescent probe, 2',7'-dichlorodihydrofluorescein diacetate (H2DCFDA). Using confocal microscopy, OVCAR5-cisR cells exhibited a much stronger fluorescent signal than OVCAR5 cells (Figure 6A-6B). A similar trend of increased ROS in PEO4 cells compared to PEO1 cells was also observed (Figure 6C-6D). Furthermore, we analyzed the changes in ROS in OVCAR5 and OVCAR5-cisR cells treated with cisplatin, which showed that cisplatin treatment induced a significant increase in ROS production in both cell lines (Figure 6E).

[0119] We next investigated whether the reduced form of intracellular NADPH was depleted in cisplatin-resistant cells. +The ratio was significantly lower in cisplatin-resistant PEO4 (Figure 6F) and OVCAR5-cisR cells (Figure 6G) when compared to PEO1 and OVCAR5 cells, respectively. The reduction in NADPH levels corroborates the SRS images showing reduced de novo lipogenesis in cisplatin-resistant cells. Changes in glycolysis were analyzed by measuring ECAR by Seahorse®. Cisplatin treatment rapidly reduced the ECAR rate in both PEO1 and PEO4 cells (Figure 6H), reaching a significant reduction within approximately 30 min of treatment (Figure 6I). In contrast, cisplatin treatment also induced a slight increase in OCR in PEO1 and PEO4 cells (Figures 6J and 6K). Consistent with the observed reduction in glycolysis, glucose uptake, measured by 2-NBDG under confocal microscopy, was reduced in cisplatin-treated OVCAR5 cells (Figures 6L and 6M). Furthermore, OVCAR5-cisR cells took up much less 2-NBDG than OVCAR5 cells (Figures 6L and 6M), implying a reduced reliance on glucose metabolism in cisplatin-resistant OC cells.

[0120] When glycolysis was suppressed by increased oxidative stress, ATP production appeared to be impaired in cisplatin-resistant or cisplatin-treated cells. Cellular ATP / ADP levels were measured and showed significantly lower ATP / ADP ratios in both PEO4 (Figure 6N) and OVCAR5-cisR cells (Figure 6O) when compared with PEO1 and OVCAR5 cells, respectively. Furthermore, acute treatment with cisplatin reduced the ATP / ADP ratio in OVCAR5 cells but not in OVCAR5-cisR cells. Palmitic acid supplementation significantly increased ATP levels in OVCAR5-cisR cells but not in OVCAR5 cells (Figure 6P). Taken together, these data suggest that cisplatin-resistant OC cells undergo metabolic reprogramming from glucose-dependent to fatty acid-dependent metabolism. This may be relevant to the propensity of ovarian tumors to grow and spread in adipocyte-rich microenvironments. Adipocytes provide fatty acids as an energy source for ovarian cancer cells

[12] and have been reported to undergo increased lipolysis in response to cisplatin treatment

[52] . Figure 6Q shows that glycolysis and lipogenesis are inhibited by cisplatin-induced oxidative stress, limiting the production of energy as well as the synthesis of free fatty acids. To survive and proliferate under cisplatin-induced oxidative stress, cancer cells upregulate fatty acid uptake and oxidation as an alternative pathway for energy production.

[0121] Example 7 Cisplatin treatment induces a transient metabolic shift toward increased fatty acid uptake in multiple cancer types

[0122] With the understanding that the increased FA uptake in cisplatin-resistant ovarian cancer cells is likely a stable metabolic adaptation to cisplatin-induced oxidative stress, we investigated whether the same metabolic shift occurs in other types of cancer upon cisplatin treatment. Platinum is widely used across malignancies. Therefore, we selected several representative cancer cell lines, including MIA PaCa-2 pancreatic cancer, A549 lung cancer, and MD-MBA231 breast cancer, to test whether acute cisplatin treatment alters fatty acid uptake rates. IC responses to cisplatin in these three cell lines 50 The final concentration of 6.6 μM was selected as the final treatment concentration, and no significant cell death was induced at that dose (Figure 7A-7C). The results showed that treatment with 6.6 μM cisplatin significantly reduced the PA-d 31 and OA-d 34 The results show that cisplatin treatment significantly increased the uptake of PA-d in A549 (Figure 7D and 7E) and OA (Figure 7F). The fold increase in PA uptake was more significant than that of OA (Figure 7F), suggesting that PA may be a preferred source of FAs for cells under cisplatin-induced oxidative stress. Similarly, we found that treatment with cisplatin also significantly increased the uptake of PA-d in A549 (Figure 7G, 7H, and 7I) and MD-MBA231 cells (Figure 7J, 7K, and 7L). 31 and OA-d 34 We observed that FAO induces a significant increase in uptake. These findings are broadly applicable to multiple types of cisplatin-resistant cancers. Figure 8 shows a cellular metabolic switch from glycolysis to fatty acid oxidation with a decrease in glucose uptake, glycolysis and de novo lipogenesis, while fatty acid uptake and oxidation are increased. This indicates a central metabolic shift or alteration in anabolic and energy metabolism in resistant cells. Inhibition of FAO resensitizes cisplatin-resistant OC cells to cisplatin treatment both in vitro and in vivo, laying the groundwork for novel combination therapies of FAO inhibitors with cancer treatments such as chemotherapy platinum agents.

[0123] References All references and publications cited herein, along with the examples and figures, are hereby incorporated by reference in their entirety, including the following: 1.Koppenol, WH, Bounds, PL and Dang, CV, Otto Warburg's contributions to current concepts of cancer metabolism. Nat Rev Cancer, 11, 325-337 (2011). 2.Ward, Patrick S. and Thompson, Craig B., Metabolic Reprogramming: A Cancer Hallmark Even Warburg Did Not Anticipate. Cancer Cell, 21, 297-308 (2012). 3.Martinez-Outschoorn, UE, Peiris-Pages, M., Pestell, RG, Sotgia, F. and Lisanti, MP, Cancer metabolism: a therapeutic perspective. Nat. Rev. Clin. Oncol., 14, 11-31 (2017). 4.Kim, J. and DeBerardinis, RJ, Mechanisms and Implications of Metabolic Heterogeneity in Cancer. Cell Metab., 30, 434-446 (2019). 5.Faubert, B., Solmonson, A. and DeBerardinis, RJ, Metabolic reprogramming and cancer progression. Science, 368, eaaw5473 (2020). 6.Pascual, G. et al., Targeting metastasis-initiating cells through the fatty acid receptor CD36. Nature, 541, 41-45 (2017). 7.Bergers, G. and Fendt, S. M., The metabolism of cancer cells during metastasis. Nat Rev Cancer, 21, 162-180 (2021). 8.Li, J. et al. Lipid desaturation Is a metabolic marker and therapeutic target of ovarian cancer stem cells. Cell Stem Cell, 20, 303-314 e305 (2017). 9.Chae, Y.C. and Kim, J.H. Cancer stem cell metabolism: target for cancer therapy. BMB Reports, 51, 319-326 (2018). 10.Sousa, C.M. et al. Pancreatic stellate cells support tumour metabolism through autophagic alanine secretion. Nature, 536, 479-483 (2016). 11.Miranda, F. et al. Salt-Inducible Kinase 2 Couples Ovarian Cancer Cell Metabolism with Survival at the Adipocyte-Rich Metastatic Niche. Cancer Cell, 30, 273-289 (2016). 12.Nieman, K.M. et al. Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nature Medicine, 17, 1498-1503 (2011). 13.Schug, Zachary T. et al. Acetyl-CoA Synthetase 2 Promotes Acetate Utilization and Maintains Cancer Cell Growth under Metabolic Stress. Cancer Cell, 27, 57-71 (2015). 14.Peck, B. et al. Inhibition of fatty acid desaturation is detrimental to cancer cell survival in metabolically compromised environments. Cancer Metab, 4, 016-0146 (2016). 15.Mullarky, E. and Cantley, L.C. in Innovative Medicine. (eds. K. Nakao, N. Minato and S. Uemoto) 3-23 (Springer Japan, Tokyo; 2015). 16.Holohan, C., Van Schaeybroeck, S., Longley, D.B. and Johnston, P.G. Cancer drug resistance: an evolving paradigm. Nat Rev Cancer, 13, 714-726 (2013). 17.Zhao, Y., Butler, E.B. and Tan, M. Targeting cellular metabolism to improve cancer therapeutics. Cell Death Dis, 4, e532 (2013). 18.Rahman, M. and Hasan, M. Cancer Metabolism and Drug Resistance. Metabolites, 5, 571 (2015). 19.Lin, J. et al. The roles of glucose metabolic reprogramming in chemo- and radio-resistance. J. Exp. Clin. Cancer Res., 38, 019-1214 (2019). 20.Wagner, W., Ciszewski, W.M. and Kania, K.D. L- and D-lactate enhance DNA repair and modulate the resistance of cervical carcinoma cells to anticancer drugs via histone deacetylase inhibition and hydroxycarboxylic acid receptor 1 activation. Cell Commun Signal, 13, 015-0114 (2015). 21.Cao, Y. Adipocyte and lipid metabolism in cancer drug resistance. J. Clin. Invest., 129, 3006-3017 (2019). 22.Tadros, S. et al. De Novo Lipid Synthesis Facilitates Gemcitabine Resistance through Endoplasmic Reticulum Stress in Pancreatic Cancer. Cancer Res., 77, 5503 (2017). 23.Iwamoto, H. et al. Cancer Lipid Metabolism Confers Antiangiogenic Drug Resistance. Cell Metab., 28, 104-117.e105 (2018). 24.Cotte, A.K. et al. Lysophosphatidylcholine acyltransferase 2-mediated lipid droplet production supports colorectal cancer chemoresistance. Nature Communications, 9, 322 (2018). 25.Rehman, S.K. et al. Colorectal Cancer Cells Enter a Diapause-like DTP State to Survive Chemotherapy. Cell, 184, 226-242 e221 (2021). 26.Rottenberg, S., Disler, C. and Perego, P. The rediscovery of platinum-based cancer therapy. Nat Rev Cancer, 21, 37-50 (2021). 27.Wang, D. and Lippard, S.J. Cellular processing of platinum anticancer drugs. Nat. Rev. Drug Discov., 4, 307-320 (2005). 28.Cheng, J.X. and Xie, X.S. Vibrational spectroscopic imaging of living systems: An emerging platform for biology and medicine. Science, 350, aaa8870 (2015). 29.Hu, F., Shi, L. and Min, W. Biological imaging of chemical bonds by stimulated Raman scattering microscopy. Nat Methods, 16, 830-842 (2019). 30.Liao, C.S. and Cheng, J.X. In situ and in vivo molecular analysis by coherent Raman scattering microscopy. Annu. Rev. Anal. Chem., 9, 69-93 (2016). 31.Zhang, C., Zhang, D. and Cheng, J.X. Coherent Raman scattering microscopy in biology and medicine. Annu. Rev. Biomed. Eng., 17, 415-445 (2015). 32.Yue, S. et al. Cholesteryl ester accumulation induced by PTEN loss and PI3K / AKT activation underlies human prostate cancer aggressiveness. Cell Metabolism, 19, 393-406 (2014). 33.Li, J. et al. Abrogating cholesterol esterification suppresses growth and metastasis of pancreatic cancer. Oncogene, 35, 6378-6388 (2016). 34.Zhang, L. et al. Spectral tracing of deuterium for imaging glucose metabolism. Nat. Biomed. Eng., 3, 402-413 (2019). 35.Li, J. and Cheng, J.X. Direct visualization of de novo lipogenesis in single living cells. Sci. Rep., 4, 6807 (2014). 36.Long, R. et al. Two-color vibrational imaging of glucose metabolism using stimulated Raman scattering. Chem Commun (Camb), 54, 152-155 (2018). 37.Huang, K.C., Li, J., Zhang, C., Tan, Y. and Cheng, J.X. Multiplex stimulated raman scattering Imaging cytometry reveals lipid-rich protrusions in cancer cells under stress condition. iScience, 23, 100953 (2020). 38.Du, J. et al. Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells. Nat. Commun., 11, 4830 (2020). 39.Fu, D. and Xie, X.S. Reliable cell segmentation based on spectral phasor analysis of hyperspectral stimulated Raman scattering imaging data. Anal Chem, 86, 4115-4119 (2014). 40.Wang, Y. et al. Frizzled-7 identifies platinum-tolerant ovarian cancer cells susceptible to ferroptosis. Cancer Res., 81, 384-399 (2021). 41.Langdon, S.P. et al. Characterization and properties of nine human ovarian adenocarcinoma cell lines. Cancer Res., 48, 6166-6172 (1988). 42.McArthur, M.J. et al. Cellular uptake and intracellular trafficking of long chain fatty acids. Journal of Lipid Research, 40, 1371-1383 (1999). 43.Su, X. and Abumrad, N.A. Cellular fatty acid uptake: a pathway under construction. Trends Endocrinol. Metab., 20, 72-77 (2009). 44.Mukherjee, A. et al. Adipocyte-Induced FABP4 Expression in Ovarian Cancer Cells Promotes Metastasis and Mediates Carboplatin Resistance. Cancer Res, 80, 1748-1761 (2020). 45.Doege, H. and Stahl, A. Protein-mediated fatty acid uptake: novel insights from in vivo models. Physiology (Bethesda), 21, 259-268 (2006). 46.Furuhashi, M. et al. Treatment of diabetes and atherosclerosis by inhibiting fatty-acid-binding protein aP2. Nature, 447, 959-965 (2007). 47.Belotte, J. et al. The role of oxidative stress in the development of cisplatin resistance in epithelial ovarian cancer. Reprod. Sci., 21, 503-508 (2014). 48.Johnson, S.W. et al. Relationship between platinum-DNA adduct formation and removal and cisplatin cytotoxicity in cisplatin-sensitive and -resistant human ovarian cancer cells. Cancer Res., 54, 5911-5916 (1994). 49.van de Vaart, P.J. et al. Intraperitoneal cisplatin with regional hyperthermia in advanced ovarian cancer: pharmacokinetics and cisplatin-DNA adduct formation in patients and ovarian cancer cell lines. Eur. J. Cancer, 34, 148-154 (1998). 50.Ghanbari Movahed, Z., Rastegari-Pouyani, M., Mohammadi, M.H. and Mansouri, K. Cancer cells change their glucose metabolism to overcome increased ROS: One step from cancer cell to cancer stem cell? Biomed. Pharmacother., 112, 108690 (2019). 51.Currie, E., Schulze, A., Zechner, R., Walther, T.C. and Farese, R.V., Jr. Cellular fatty acid metabolism and cancer. Cell Metab, 18, 153-161 (2013). 52.Eckert, M.A., Orozco, C., Xiao, J., Javellana, M. and Lengyel, E. The Effects of Chemotherapeutics on the Ovarian Cancer Microenvironment. Cancers (Basel), 13 (2021). 53.Wang, D. and Lippard, S.J. Cellular processing of platinum anticancer drugs. Nat Rev Drug Discov, 4, 307-320 (2005). 54.Galluzzi, L. et al. Molecular mechanisms of cisplatin resistance. Oncogene, 31, 1869-1883 (2012). 55.Shen, D.W., Pouliot, L.M., Hall, M.D. and Gottesman, M.M. Cisplatin resistance: a cellular self-defense mechanism resulting from multiple epigenetic and genetic changes. Pharmacol Rev 64, 706-721 (2012). 56.Itoh, T. et al. Cisplatin induces production of reactive oxygen species via NADPH oxidase activation in human prostate cancer cells. Free Radical Research, 45, 1033-1039 (2011). 57.Marullo, R. et al. Cisplatin induces a mitochondrial-ROS response that contributes to cytotoxicity depending on mitochondrial redox status and bioenergetic functions. PLoS One, 8, e81162 (2013). 58.Yu, W. et al. Cisplatin generates oxidative stress which is accompanied by rapid shifts in central carbon metabolism. Sci. Rep., 8, 4306 (2018). 59.Zaugg, K. et al. Carnitine palmitoyltransferase 1C promotes cell survival and tumor growth under conditions of metabolic stress. Genes & Development, 25, 1041-1051 (2011). 60.Ma, Y. et al. Fatty acid oxidation: An emerging facet of metabolic transformation in cancer. Cancer Lett., 435, 92-100 (2018). 61.Melone, M.A.B. et al. The carnitine system and cancer metabolic plasticity. Cell Death Dis., 9, 228 (2018). 62.van der Vusse, G.J., van Bilsen, M., Glatz, J.F., Hasselbaink, D.M. and Luiken, J.J. Critical steps in cellular fatty acid uptake and utilization. Molecular and Cellular Biochemistry, 239, 9-15 (2002). 63.Koundouros, N. and Poulogiannis, G. Reprogramming of fatty acid metabolism in cancer. Br J Cancer, 122, 4-22 (2020). 64.Snaebjornsson, M.T., Janaki-Raman, S. and Schulze, A. Greasing the Wheels of the Cancer Machine: The Role of Lipid Metabolism in Cancer. Cell Metab, 31, 62-76 (2020). 65.Condello, S. et al. [beta]-Catenin-regulated ALDH1A1 is a target in ovarian cancer spheroids. Oncogene, 34, 2297-2308 (2015). 66.Condello, S. et al. Tissue tranglutaminase regulates interactions between ovarian cancer stem cells and the tumor niche. Cancer Res., 78, 2990-3001 (2018). 67.Dong, R. et al. Histologic and molecular analysis of patient derived xenografts of high-grade serous ovarian carcinoma. J. Hematol. Oncol., 9, 92 (2016). 68.Carpenter, A.E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biology, 7, R100 (2006). 69.Robinson, M.D., McCarthy, D.J. and Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139-140 (2010).

Claims

1. An assay for determining the resistance of target cells or tissues collected from a subject to a treatment related to cellular stress, measuring a change in functional metabolism in the target cells or tissues by chemical microscopy, and determining a metabolic index of resistance to the treatment in the target cells or tissues, An assay comprising:

2. The assay according to claim 1, wherein the change in functional metabolism includes a change from glucose and glycolysis-dependent assimilation and energy metabolism to fatty acid uptake and fatty acid oxidation-dependent assimilation and energy metabolism.

3. The assay according to claim 1, wherein when the change in functional metabolism is a decrease in glucose and glycolysis-dependent assimilation and an increase in fatty acid uptake and fatty acid oxidation-dependent assimilation and energy metabolism, the metabolic index correlates with the resistance of the target cells or tissues to the treatment.

4. The assay according to claim 3, wherein when the change in metabolism is a decrease in de novo lipid synthesis in the target cells or tissues, the metabolic index further correlates with the resistance of the target cells or tissues to the treatment.

5. The assay according to claim 1, wherein the metabolic index is the ratio of the increase in fatty acid uptake and oxidation to the decrease in glucose-dependent assimilation.

6. Measuring a change in functional metabolism by chemical microscopy is measuring the assimilation derived from glucose and glycolysis in the target cells or tissues, measuring the fatty acid uptake and oxidation in the target cells or tissues, and determining the change from glucose assimilation in the target cells or tissues to fatty acid uptake and oxidative energy metabolism, the assay according to claim 1.

7. Measuring de novo lipidogenesis in the target cell or tissue, and further comprising determining alterations in fatty acid uptake and oxidative energy metabolism from glucose and glycolysis-dependent assimilation and de novo lipidogenesis in the target cell or tissue, the assay according to claim 6.

8. The assay according to claim 1, wherein the chemical microscopy method comprises Raman microscopy or infrared microscopy.

9. The assay according to claim 1, wherein the target cell induces cellular stress in the target cell or tissue.

10. The assay according to claim 1, wherein the cellular stress in the target cell or tissue is oxidative stress, metabolic stress, hypoxic stress, nutritional stress, heat stress, genotoxic stress, or a combination thereof.

11. The assay according to claim 1, wherein the target cell or tissue is a cancer cell and the treatment is a cancer treatment.

12. The assay according to claim 11, wherein the cancer treatment is selected from chemotherapy, radiotherapy, immunotherapy, targeted therapy, hormone therapy, light or laser therapy, photodynamic therapy, and combinations thereof.

13. The assay according to claim 12, wherein the chemotherapy is treatment with a platinum-based therapeutic agent selected from cisplatin, carboplatin, oxaliplatin, nedaplatin, and combinations thereof.

14. A pharmaceutical composition for treating or inhibiting resistance to a treatment related to cellular stress in a target cell or tissue in a subject, based on the metabolic index of resistance to the treatment in the target cell or tissue determined by performing the assay according to claim 1, for administration when administering at least one therapy to the subject, A pharmaceutical composition comprising at least one fatty acid oxidation inhibitor.

15. The metabolic indicator of resistance to a treatment related to cellular stress, determined by performing the assay according to Claim 1, measures the assimilation derived from glucose and glycolysis in the target cell or tissue, and measures the fatty acid uptake and oxidation in the target cell or tissue, and is determined by an assay including measuring the alteration of functional metabolism by chemical microscopy, The pharmaceutical composition according to Claim 14, wherein the metabolic indicator is the ratio of the increase in fatty acid uptake and oxidative energy metabolism to the decrease in assimilation derived from glucose and glycolysis in the target cell or tissue.

16. The pharmaceutical composition according to Claim 14, wherein the target cell is a cancer cell and the treatment is a cancer treatment.

17. The pharmaceutical composition according to Claim 14, wherein determining the metabolic indicator of resistance to the treatment in the target cell or tissue further includes a decrease in de novo lipogenesis.

18. The pharmaceutical composition according to Claim 14, wherein fatty acid oxidation is inhibited in the target cell or tissue, and the treatment induces cellular stress in the target cell or tissue, thereby inhibiting resistance to the treatment.

19. The pharmaceutical composition according to Claim 14, wherein the at least one fatty acid oxidation inhibitor is selected from etomoxir, oxyphenisatin, perhexiline, mildronate, trimetazidine, and combinations thereof.

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