Background Precision medicine in breast cancer demands markers sensitive to early treatment response. Aerobic glycolysis (AG) upregulates lactate dehydrogenase A (LDH-A) with elevated lactate production; however, existing approaches for lactate quantification are either invasive or impractical clinically. Methods Thirty female patients (age 39–78 years, 15 grade II and 15 grade III) with invasive ductal carcinoma were enrolled. Lactate concentration was quantified from freshly excised whole tumours with double quantum filtered (DQF) magnetic resonance spectroscopy (MRS), and Nottingham Prognostic Index (NPI), LDH-A and proliferative marker Ki-67 were assessed histologically. Results There was a significantly higher lactate concentration ( t = 2.2224, p = 0.0349) in grade III (7.7 ± 2.9 mM) than in grade II (5.5 ± 2.4 mM). Lactate concentration was correlated with NPI ( ρ = 0.3618, p = 0.0495), but not with Ki-67 ( ρ = 0.3041, p = 0.1023) or tumour size ( r = 0.1716, p = 0.3645). Lactate concentration was negatively correlated with LDH-A ( ρ = −0.3734, p = 0.0421). Conclusion Our results showed that lactate concentration in whole breast tumour from DQF MRS is sensitive to tumour grades and patient prognosis.
Objectives Despite improved survival due to new treatments, the 10-year survival rate in patients with breast cancer is approximately 75%. Lymphovascular invasion (LVI), a prognostic marker independent from histological grade and stage, can only be fully determined at final histological examination. Lipid composition is deregulated in tumour via de novo lipogenesis, with alteration in lipogenic genes in LVI. We hypothesise alteration in lipid composition derived from novel non-invasive spectroscopy method is associated with LVI positivity. Methods Thirty female patients (age 39–78) with invasive ductal carcinoma were enrolled, with 13 LVI negative and 17 LVI positive. Saturated, monounsaturated, polyunsaturated fatty acids and triglycerides (SFA, MUFA, PUFA and TRG) were quantified from ex vivo breast tumours freshly excised from patients on a 3 T clinical MRI scanner, and proliferative activity marker Ki-67 and serotonin derived histologically. Results There were significantly lower MUFA (p = 0.0189) in LVI positive (median: 0.37, interquartile range (IQR): 0.25–0.64) than negative (0.63, 0.49–0.96). There were significantly lower TRG (p = 0.0226) in LVI positive (1.32, 0.95–2.43) than negative (2.5, 1.92–4.15). There was no significant difference in SFA (p = 0.6009) or PUFA (p = 0.1641). There was no significant correlation between lipid composition against Ki-67 or serotonin, apart from a borderline negative correlation between PUFA and serotonin (r = - 0.3616, p = 0.0496). Conclusion Lipid composition might provide a biomarker to study lymphovascular invasion in breast cancer. Key Points • Monounsaturated fatty acids in lymphovascular invasion (LVI) positive invasive breast carcinoma were significantly lower than that in LVI negative. • Triglycerides in LVI positive invasive breast carcinoma were significantly lower than that in LVI negative. • Lipid composition from MR spectroscopy reflects the rate of de novo lipogenesis and provides a potential biomarker independent from histological grade and stage.
Polyunsaturated fatty acid (PUFA), a key marker in breast cancer, is non-invasively quantifiable using multiple quantum coherence (MQC) magnetic resonance spectroscopy (MRS) at the expense of losing half of the signal. Signal combination for phased array coils provides potential pathways to enhance the signal to noise ratio (SNR), with current algorithms developed for conventional brain MRS. Since PUFA spectra and the biochemical environment in the breast deviate significantly from those in the brain, we set out to identify the optimal algorithm for PUFA in breast cancer. Combination algorithms were compared using PUFA spectra from 17 human breast tumour specimens, 15 healthy female volunteers, and 5 patients with breast cancer on a clinical 3 T MRI scanner. Adaptively Optimised Combination (AOC) yielded the maximum SNR improvement in specimens (median, 39.5%; interquartile range: 35.5–53.2%, p < 0.05), volunteers (82.4 ± 37.4%, p < 0.001), and patients (median, 61%; range: 34–105%, p < 0.05), while independent from voxel volume (rho = 0.125, p = 0.632), PUFA content (rho = 0.256, p = 0.320) or water/fat ratio (rho = 0.353, p = 0.165). Using AOC, acquisition in patients is 1.5 times faster compared to non-noise decorrelated algorithms. Therefore, AOC is the most suitable current algorithm to improve SNR or accelerate the acquisition of PUFA MRS from breast in a clinical setting.
Objective. To investigate the potential structural and metabolic role of skeletal muscle in systemic lupus erythematosus (SLE)-related fatigue.Methods. A case-control, multimodal magnetic resonance imaging (MRI) study was conducted. Cases were patients with inactive SLE who reported chronic fatigue. Controls were age-and sex-matched healthy members of the general population. Patients were clinically characterized and then underwent a 3T whole-body MRI scan. Resting and dynamic 31 P MRI spectroscopy of the calf muscles was applied, from which phosphocreatine (PCr) recovery halftime, a marker of mitochondrial dysfunction, was computed. In addition, microstructural sequences (T1-weighted anatomic images, T2 mapping, and diffusion tensor imaging) were acquired. Descriptive statistics evaluated group differences and within-case physical fatigue correlations were explored.Results. Of the 37 recruits (mean age 43.8 years, 89.2% female), cases (n = 19) reported higher levels of physical fatigue, pain, depression, and sleep disturbance compared to the control group (P < 0.0001). PCr was greater (P = 0.045) among cases (mean ± SD 33.0 ± 9.0 seconds) compared to controls (mean ± SD 27.1 ± 6.6 seconds). No microstructural group differences were observed. Within cases, physical fatigue did not correlate with PCr (r = -0.28, P = 0.25).Conclusion. We report preliminary data demonstrating greater skeletal muscle mitochondrial dysfunction among fatigued patients with SLE compared to healthy controls.
Background Response guided treatment in breast cancer is highly desirable, but the effectiveness is only established based on residual cellularity from histopathological analysis after surgery. Tubule formation, a key component of grading score, is directly associated with cellularity, with significant implications on prognosis. Peri-tumoural lipid composition, a potential marker, can be rapidly mapped across the entire breast using novel method of chemical shift-encoded imaging, enabling the quantification of spatial distribution. We hypothesise that peri-tumoural spatial distribution of lipid composition is sensitive to tumour cellular differentiation and proliferative activity. Methods Twenty whole tumour specimens freshly excised from patients with invasive ductal carcinoma (9 Score 2 and 11 Score 3 in tubule formation) were scanned on a 3 T clinical scanner (Achieva TX, Philips Healthcare). Quantitative lipid composition maps were acquired for polyunsaturated, monounsaturated, and saturated fatty acids (PUFA, MUFA, SFA). The peri-tumoural spatial distribution (mean, skewness, entropy and kurtosis) of each lipid constituent were then computed. The proliferative activity marker Ki-67 and tumour-infiltrating lymphocytes (TILs) were assessed histologically. Results For MUFA, there were significant differences between groups in mean (p = 0.0119), skewness (p = 0.0116), entropy (p = 0.0223), kurtosis (p = 0.0381), and correlations against Ki-67 in mean (ρ = -0.5414), skewness (ρ = 0.6045) and entropy (ρ = 0.6677), and TILs in mean (ρ = -0.4621). For SFA, there were significant differences between groups in mean (p = 0.0329) and skewness (p = 0.0111), and correlation against Ki-67 in mean (ρ = 0.5910). For PUFA, there was no significant difference in mean, skewness, entropy or kurtosis between the groups. Conclusions There was an association between peri-tumoural spatial distribution of lipid composition with tumour cellular differentiation and proliferation. Peri-tumoural lipid composition imaging might have potential in non-invasive quantitative assessment of patients with breast cancer for treatment planning and monitoring.
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