Brain networks involved in cancer treatment response: insights from 18F-FDG PET scans

Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the metabolic and structural brain networks in patients with MCI.Methods: We analyzedmagnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) data of 137 patients with MCI and 80 healthy controls (HCs). The HC group data comes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores.Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions (left globus pallidus, right calcarine fissure and its surrounding cortex, left lingual gyrus) by scanning the hubs. The volume of gray matter atrophy in the left globus pallidus was significantly positively correlated with comprehension of spoken language (p = 0.024) and word-finding difficulty in spontaneous speech item scores (p = 0.007) in the ADAS-cog. Glucose intake in the three key brain regions was significantly negatively correlated with remembering test instructions items in ADAS-cog (p = 0.020, p = 0.014, and p = 0.008, respectively).Conclusion: Structural brain networks showed more changes than metabolic brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

In the clinical management of patients with locally advanced rectal cancer (LARC), the early identification of poor and good responders after neoadjuvant chemoradiotherapy (N-CRT) is essential. Therefore, we developed and validated predictive models including MRI findings from the structured report template, clinical and radiomics parameters to differentiate between poor and good responders in patients with locally advanced rectal cancer who underwent neoadjuvant chemoradiotherapy. Preoperative multiparametric MRI from 183 patients with locally advanced rectal cancer (122 in the training cohort, 61 in the validation cohort) was included in this retrospective study. After preprocessing, radiomic features were extracted and two methods of feature selection was applied to reduce the number of radiomics features. Logistic regression (LR) and random forest (RF) machine learning classifiers were trained to identify good responders from poor responders. Multivariable logistic regression analysis was used to incorporate the radiomic signature and clinical risk factors into a nomogram. Classifier performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). For the differentiation of poor and good responders, the radiomics model with an LR classifier achieved AUCs of 0.869 and 0.842 for the training and validation cohorts, respectively. The nomogram showed the highest reproducibility and prognostic ability in the training and validation cohorts, with AUCs of 0.923 (95% confidence interval, 0.872-0.975) and 0.898 (0.819-0.978), respectively. Additionally, the nomogram achieved significant risk stratification of patients in respect to progression free survival (PFS). The nomogram accurately differentiated good and poor responders in patients with LARC undergoing N-CRT, and showed significant performance for predicting PFS.

e17577 Background: Cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy improves survival in ovarian cancer patients. Currently, no biomarkers exist to select for patients who best benefit from HIPEC. We compared the slicing landscape of ovarian cancer tumors of good and poor HIPEC responders in a Phase I clinical trial, to identify potential predictive biomarkers. Methods: A total of 35 ovarian cancer patients enrolled in a Phase I HIPEC trial (NCT01970722), in which nine patients had paired pre-and post treatment tissue samples collected. Pre-HIPEC tumor samples had RNA isolated, and whole-transcriptome library construction performed. FASTQ files from tumor samples were aligned to GRCH38 and ran through SplAdder (PMID:26873928) to identify alternative splicing events. Outlier splice events were ranked by mean log-likelihood score and converted to amino acid sequences using Bisbee (PMID:34031440). The top 15 outlier events were run through NetMHCpan (PMID:32406916) and DeepHLApan (PMID:31736974) to predict putative neoantigens. Results: Among nine HIPEC-treated ovarian cancer patients with available RNAseq data, five were considered good responders (≥12months PFS), and four as poor responders (<12 months PFS). Alternative splicing analysis identified 519splice events as outliers between good and poor responders, with 250reported to be novel. Of the 250 novel splicing events, we identified a novel protein coding 3’ end splice event in CPNE1 which was alternatively spliced in at a Percent-Spliced-In (PSI) of 99% in three out four poor responders. The same splice event was alternatively spliced at a PSI of 30.3% in four out of five good responders. CPNE1 plays a role in calcium mediated intracellular processes, and is involved in the TNF-alpha receptor signaling pathway, thus playing an important role in cell proliferation, differentiation, apoptosis, and modulation of immune responses and induction of inflammation. The encoded novel peptide from the CPNE1 splice event produced six predicted strong binding sites based on a binding score>0.80 and an immunogenic score>0.80. Conclusions: CPNE1 was identified as a differentially spliced event in poor HIPEC responders with ovarian cancer. Poor HIPEC responders had higher immunogenic score. Alternative splicing analysis may be a promising method to determine potential biomarkers for HIPEC treatment in ovarian cancer patients. Clinical trial information: NCT01970722 .

Use of the flare-up protocol with high dose human follicle stimulating hormone and human menopausal gonadotropins for in vitro fertilization in poor responders

Plasma catecholamine metabolites in Schizophrenics: Evidence for the two-subtype concept

589 Background: Neoadjuvant radio/chemoradiotherapy (CRT) is a treatment milestone for LARC. The importance of immune response in CRT efficacy is increasingly realised. However immune cell changes associated with poor responders and their modulation with immune-CRT combinations is unclear. Methods: Matched archival pre-CRT biopsies and post-CRT resection specimens from patients (pts) treated with neoadjuvant CRT were retrieved. Delta-TCD (tumor cell density, estimated using quantitative point counting on virtual tissue H&E) and k-means clustering method were used to classify pts into good, intermediate and poor responders. Baseline expression and CRT-induced changes in 770 immune-related genes (plus 30 DNA damage response genes) were evaluated using NanoString Technologies. Results: At least 70 pts treated with short/long course radiotherapy (SCRT/LCRT) and matched tissues available were identified. To date, 27 pts evaluable for deltaTCD and gene expression were clustered into good (n:10), intermediate (n:7) and poor (n:10) responders. The expression of 14% (91/636) of immune genes was significantly affected by CRT (Bonferroni t-test, q-value < 0.05) overall, with significant increase in innate immunity and decrease in adaptive immunity across all pts (CIBERSORT and SSGSEA analyses). Between good and poor responders there were 6% (39/636) and 2% (15/636) of genes significantly affected by CRT (Bonferroni t-test, q-value < 0.05), respectively. CRT-induced increased CD8+ T cells expression in poor responders compared to good responders was seen. Increased baseline expression of resistance genes (including PD-L1, IDO1 and IL2RA) were seen in poor versus good responders. Validation with quantitative multiplex-immunofluorescence (Vectra) and correlation with SCRT/LCRT and time to surgery are on-going. Conclusions: The expression of immune-related genes is significantly modified by CRT in LARC. With the caveat of small numbers, we identified differentially expressed immune targets at baseline which may justify immune-CRT combinations in neoadjuvant setting in selected pts to modulate the CRT effect and ultimately increase response.

Using the immune system to fight cancer has garnered tangible success, but some treatments, like neoadjuvant chemotherapy (NAC), modulate the immune microenvironment. Recent studies show that the spatial organization of tumor infiltrating lymphocytes (TIL) have greater predictive value than TIL density. The effect of NAC on immune composition and spatial distribution is not fully understood but new insight could help to guide its use in combination with immune therapy and identify patients with potential to derive benefit. We spatially profiled 84 RNA targets (GeoMx®) in a cohort of 12 NAC-treated breast cancer patients (4 Luminal, 4 HER2+ and 4 triple negative), none of whom achieved a pathological complete response. Matched pre- and post-treatment tissue samples were analyzed together with regions of interest (tumor center, invasive margin and TIL aggregates) identified using CD3, CD20, Syto83 and pan-cytokeratin for stromal/tumor segmentation. NAC decreases overall gene expression in breast tumors with the biggest declines seen in tumor promoting (CCND1, AKT1, CTNNB1, EPCAM, VEGFA, KRT and MKI67) and some inflammatory (CXCL10, STAT1 and STAT2) genes (p-value <0.05; other immune related transcripts showed little variation). Expression was compared between patients with a good response (<20% tumor cellularity) and those with a poor response (>50% cellularity). Poor responders expressed higher levels of tumor promoting genes pre-NAC, which remained high after treatment (KRT p=0.023, CTNNB1 p=0.031). No differences were detected in immune genes in the stroma based on patient responsiveness; however, higher antigen presentation and inflammatory gene transcripts were found at the tumor margins of good responders. Post-NAC differences between the margin and center decrease in good responders paralleled by a shift towards higher or equal expression of some inflammatory markers at the tumor center. Poor responders maintain high expression of all immune markers at the margin. A higher number of aggregates (mean n=5 vs n=1.3) were detected in good compared to poor responders together with more tertiary lymphoid structures (mean n=2.4 vs n=0.3) and distinguished by higher immune gene expression (CD8 p=0.046, CCL5 p=0.054, NKG7 p=0.022). NAC induces changes in other cells in the tumor microenvironment while targeting tumor cells. Our data show that spatial analysis of gene expression comparing good and poor responders (without a pathological complete response) reveal that tumor cells in the latter retain expression of tumor promoting genes while the immune compartment remains excluded. Good responders are characterized by a decrease in tumor promoting genes in parallel with lymphoid aggregates, including TLS, of active immune cells in the stroma and at the tumor center. These findings suggest that tailoring adjuvant treatment between good and poor responding patients might be warranted. Citation Format: Noémie Thomas, Soizic Garaud, Mireille Langouo, Ioannis Zerdes, Doïna Sofronii, Anaïs Boisson, Theodoros Foukakis, Alexandre De Wind, Roberto Salgado, Ahmad Awada, Karen Willard-Gallo. Spatial organization of the immune microenvironment after neoadjuvant treatment of breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2045.

5569 Background: Hyperthermic intraperitoneal chemotherapy (HIPEC) is associated with improved overall survival in Stage III epithelial ovarian cancer (EOC) patients. We set out to evaluate the gene signatures associated with HIPEC response in EOC patients. Methods: Ninety-one EOC patients who underwent HIPEC with pre-operative tumor samples at City of Hope (51) and CHU Lyon (40) were identified between 2014 and 2022. RNA isolation was performed from formalin-fixed paraffin-embedded samples, followed by Whole-transcriptome library construction. Following exclusion of non-high grade serous (HGS) samples, and quality control steps, twenty-four samples were excluded. Progression-free survival (PFS) was used to define HIPEC response. Cut-off PFS values were used to distinguish good vs poor responders in primary EOC patients (18 months, based on KGOG, CARCINO-HIPEC trials), and recurrent EOC patients (12 months, based on MSK, CHIPOR HIPEC trials). Differential Gene Expression Analysis comparing good and poor HIPEC responders identified significantly changed genes. Pathway analysis was conducted using gene set enrichment analysis (GSEA) against Hallmark. Results: A total of sixty HGS tumor samples with available survival data were analyzed. 63.3% were primary EOC, 36.7% recurrent EOC. Germline BRCA mutations affected 21.7% of patients. With a median follow up of 31.9 months, median PFS was 29.3 (95%CI: 15.3, 63.5) months in primary EOC patients and 26.0 (95%CI: 14.7, 37.1) months in recurrent patients. Median OS was not reached in either group. 60.0% had a recurrence. Thirty-eight patients were identified as good responders, with a median PFS of 37.1 mos. (95%CI: 26.4, NR); 18 patients were identified as poor responders, with median PFS of 11.4 months (95%CI: 7.5, 14.2). Differential gene expression analysis between good and poor responders revealed 29 significantly upregulated 35 downregulated genes in HIPEC responders. Top upregulated genes in HIPEC responders include MAPK signaling pathway genes (RIB2, ETV5, CAPN8, IGFR1), in addition to CCND1 and CEACAM1. In HIPEC responders, the top-ranking gene sets in the transcriptional signature included Notch, KRAS, and Wnt/beta-catenin signaling pathways. In poor HIPEC responders, the DNA damage repair associated pathways E2F targets and G2M checkpoint, were activated. Similar transcriptomic pathway signatures were observed in Non-recurrent versus Recurrent HIPEC patients: Non-recurrent tumors were enriched with Notch signaling, while Recurrent tumors were enriched with E2F target and G2M checkpoint pathways. Conclusions: Good HIPEC response is characterized by transcriptional signatures consistent with Type I EOC characteristics of PI3K/RAS/Notch signaling. Recurrence after HIPEC in HGS ovarian cancer is higher in patients with E2F/G2M transcriptional signatures.

Objective:To assess mid-luteal estradiol (E2) levels in poor and good responders and determine its effect on the outcome after intracytoplasmic sperm injection (ICSI).Methods:The current study was carried out in females who underwent ICSI from June 2011 to September 2013 in “Islamabad Clinic Serving Infertile Couples”. They were categorized into good and poor responders on the basis of female age ≤40 years, basal follicle stimulating hormone ≤12 mIU/ml, and antral follicle count >5, respectively. Their mid-luteal E2 measured on the day of embryo transfer was stratified into groups (A-E) on the basis of 20th, 40th, 60th and 80th percentile values. The outcome was categorized into non-pregnant with beta human chorionic Gonadotrophin (hCG) 5-25 m IU/ml, and clinical pregnancy with beta hCG>25 m IU/ml.Results:The conception rate was 12% (63/513) in poor responders and 72% (237/329) in good responders respectively. The mid-luteal E2 levels were higher in conception as compared to non-conception cycles (p<0.001) in good and poor responders.Conclusion:Maximum pregnancies in poor and good responders (53% and 98% respectively) with mid-luteal E2 levels above 80th percentiles confirm the role of the increase in mid-luteal E2 for augmentation in conception rate of females after ICSI.

BackgroundTraditionally rheumatoid arthritis (RA) trials classify patients as responders and non-responders; they ignore the potential range of treatment responses. Group Based Trajectory Models (GBTMs) provide a more refined approach. They identify patient subgroups with similar outcome trajectories. We used GBTMs to classify patients into subgroups of varying responses and explore factors associated with different responses to intensive treatment in a secondary analysis of intensive treatment in the TITRATE clinical trial.MethodsThe TITRATE trial enrolled 335 patients with RA: 168 patients were randomised to receive intensive management, which comprised monthly assessments including measures of the disease activity score for 28 joints (DAS28), treatment escalation when patients were not responding sufficiently and psychosocial support; 163 of these patients completed the trial. We applied GBTMs to monthly DAS28 scores over one year to these patients who had received intensive management. The control group had standard care and were assessed every 6 months; they had too few DAS28 scores for applying GBTMs.ResultsGBTMs identified three distinct trajectories in the patients receiving intensive management: good (n = 40), moderate (n = 76) and poor (n = 47) responders. Baseline body mass index (BMI), disability, fatigue and depression levels were significantly different between trajectory groups. Few (10%) good responders were obese, compared to 38% of moderate, and 43% of poor responders (P = 0.002). Few (8%) good responders had depression, compared to 14% moderate responders, and 38% poor responders (P < 0.001). The key difference in treatments was using high-cost biologics, used in only 5% of good responders but 30% of moderate and 51% of poor responders (P < 0.001). Most good responders had endpoint remissions and low disability, pain, and fatigue scores; few poor responders achieved any favourable outcomes.ConclusionGBTMs identified three trajectories of disease activity progression in patients receiving intensive management for moderately active RA. Baseline variables like obesity and depression predicted different treatment responses. Few good responders needed biologic drugs; they responded to conventional DMARDs alone. GBTMs have the potential to facilitate precision medicine enabling patient-oriented treatment strategies based on key characteristics.RegistrationTITRATE Trial ISRCTN 70160382.

To investigate if MRI-assessed tumour volumetry correlates with histological tumour response to neoadjuvant chemotherapy (NACT) and subsequent chemoradiotherapy (CRT) in locally advanced rectal cancer (LARC). Data from 69 prospectively enrolled patients with LARC receiving NACT followed by CRT and radical surgery were analysed. Whole-tumour volumes were contoured in T2 weighted MR images obtained pre-treatment (VPRE), after NACT (VNACT) and after the full course of NACT followed by CRT (VCRT). VPRE, VNACT and tumour volume changes relative to VPRE, ΔVNACT and ΔVCRT were calculated and correlated to histological tumour regression grade (TRG). 61% of good histological responders (TRG 1-2) to NACT followed by CRT were correctly predicted by combining VPRE < 10.5 cm(3), ΔVNACT > -78.2% and VNACT < 3.3 cm(3). The highest accuracy was found for VNACT, with 55.1% sensitivity given 100% specificity. The volume regression after completed NACT and CRT (VCRT) was not significantly different between good and poor responders (TRG 1-2 vs TRG 3-5). MRI-assessed small tumour volumes after NACT correlated with good histological tumour response (TRG 1-2) to the completed course of NACT and CRT. Furthermore, by combining tumour volume measurements before, during and after NACT, more good responders were identified. MRI volumetry may be a tool for early identification of good and poor responders to NACT followed by CRT and surgery in LARC in order to aid more individualized, multimodal treatment.

Joint Disease and the Potential for Improved Joint Health in Inhibitor Patients Who Have a Good Response to aPCC Prophylaxis: Data From the Profeiba Study

Background: Imbalances in the intestinal microbiome are closely associated with the occurrence and development of cancer, and can affect tumorigenesis by influencing the inflammatory response, regulating the immune system, producing specific metabolites, and participating in tumor signaling pathways. Methods: This study investigated the relationships among intestinal microbial dynamics, metabolite profiles, and neoadjuvant chemotherapy (NAC) outcomes in patients with breast cancer. Patients were stratified by Miller-Payne (MP) grade into good (MP 4–5) or poor (MP 1–3) responders. Fecal samples from patients (pre- and post-NAC) were analyzed via 16S rRNA sequencing and untargeted metabolic analysis. Results: After neoadjuvant chemotherapy, the species diversity and abundance of the intestinal microbiome significantly decreased, and these trends were not correlated with neoadjuvant chemotherapy efficacy. Fusobacterium abundance remained significantly higher in poor responders than good responders post-NAC, thus suggesting its association with chemoresistance. The Firmicutes/Bacteroidetes ratio was lower in patients with breast cancer than healthy controls, and was correlated with the therapeutic response: this ratio rose post-NAC but remained suboptimal in poor responders. Untargeted metabolomics identified upregulated amino acids (Thr-Thr and histidine) in poor responders and elevated lipids (C17-sphinganine) in good responders. ROC (receiver operating characteristic curve) analysis validated these metabolites (AUC &gt;0.7) as predictive biomarkers. KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis highlighted enrichment in mTOR signaling, endocrine resistance, and estrogen signaling pathways. Conclusions: These findings underscore the intestinal microbiome’s potential as a predictor of NAC efficacy and a therapeutic target. Modulating Fusobacterium or metabolite pathways may enhance chemotherapy response.

Objective:To assess the effects of tumour necrosis factor (TNF) antagonist therapy on B lymphocyte stimulator (BLyS) expression in patients with rheumatoid arthritis (RA).Methods:Blood from 38 patients with RA from a...