Combined systemic inflammatory immune index and prognostic nutrition index as chemosensitivity and prognostic markers for locally advanced gastric cancer receiving neoadjuvant chemotherapy: a retrospective study.

BACKGROUND: The prognosis nutritional index (PNI) and the systemic inflammatory immunological index (SII) are characteristic indicators of the nutritional state and the systemic inflammatory response, respectively. However, there is an unknown combined effect of these indicators in the clinic. Therefore, the practicality of using the SII-PNI score to predict prognosis and tumor response of locally advanced gastric cancer (LAGC) following chemotherapy was the main focus of this investigation. METHODS: We retrospectively analyzed 181 patients with LAGC who underwent curative resection after neoadjuvant chemotherapy in a prospective study (NCT01516944). We divided these patients into tumour regression grade(TRG) 3 and non-TRG3 groups based on tumor response (AJCC/CAP guidelines). The SII and PNI were assessed and confirmed the cut-off values before treatment. The SII-PNI values varied from 0 to 2, with 2 being the high SII (≥ 471.5) as well as low PNI (≤ 48.6), a high SII or low PNI is represented by a 1 and neither is represented by a 0, respectively. RESULTS: 51 and 130 samples had TRG3 and non-TRG3 tumor responses respectively. Patients with TRG3 had substantially higher SII-PNI scores than those without TRG3 (p < 0.0001). Patients with greater SII-PNI scores had a poorer prognosis (p < 0.0001). The SII-PNI score was found to be an independent predictor of both overall survival (HR = 4.982, 95%CI: 1.890-10.234, p = 0.001) and disease-free survival (HR = 4.763, 95%CI: 1.994-13.903, p = 0.001) in a multivariate analysis. CONCLUSION: The clinical potential and accuracy of low-cost stratification based on SII-PNI score in forecasting tumor response and prognosis in LAGC is satisfactory.

Peritumoral MRI Radiomics Features Increase the Evaluation Efficiency for Response to Chemotherapy in Patients With Epithelial Ovarian Cancer.

BACKGROUND: It remains unclear whether extracting peritumoral volume (PTV) radiomics features are useful tools for evaluating response to chemotherapy of epithelial ovarian cancer (EOC). PURPOSE: To evaluate MRI radiomics signatures (RS) capturing subtle changes of PTV and their added evaluation performance to whole tumor volume (WTV) for response to chemotherapy in patients with EOC. STUDY TYPE: Retrospective. POPULATION: 219 patients aged from 15 to 79 years were enrolled. FIELD STRENGTH/SEQUENCE: 3.0 or 1.5T, axial fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), and contrast enhanced T1-weighted imaging (CE-T1WI). ASSESSMENT: MRI features were extracted from the four axial sequences and six different volumes of interest (VOIs) (WTV and WTV + PTV (WPTV)) with different peritumor sizes (PS) ranging from 1 to 5 mm. Those features underwent preprocessing, and the most informative features were selected using minimum redundancy maximum relevance and least absolute shrinkage and selection operator to construct the RS. The optimal RS, with the highest area under the curve (AUC) of receiver operating characteristic was then integrated with independent clinical characteristics through multivariable logistic regression to construct the radiomics-clinical model (RCM). STATISTICAL TESTS: Mann-Whitney U test, chi-squared test, DeLong test, log-rank test. P < 0.05 indicated a significant difference. RESULTS: All the RSs constructed on WPTV exhibited higher AUCs (0.720-0.756) than WTV (0.671). Of which, RS with PS = 2 mm displayed a significantly better performance (AUC = 0.756). International Federation of Gynecology and Obstetrics (FIGO) stage was identified as the exclusive independent clinical evaluation characteristic, and the RCM demonstrated higher AUC (0.790) than the RS, but without statistical significance (P = 0.261). DATA CONCLUSION: The radiomics features extracted from PTV could increase the efficiency of WTV radiomics for evaluating the chemotherapy response of EOC. The cut-off of 2 mm PTV was a reasonable value to obtain effective evaluation efficiency. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Impact of Low BMI and Nutritional Status on Quality of Life and Disease Outcome in Breast Cancer Patients: Insights From a Tertiary Cancer Center in India.

This study investigates the impact of Body Mass Index (BMI) on Quality of Life (QoL) and treatment outcomes in breast cancer (BC) patients, particularly focusing on underweight individuals with compromised nutritional status. A nonrandomized prospective study comprising 121 newly diagnosed patients across various BMI categories utilized FACT-B & FACIT-Sp-12 questionnaires. Follow-ups occurred at baseline, during (3rd and 6th), and after (12th month) anthracycline-taxane chemotherapy, either sequentially or concomitantly. Patients with low BMI (<18.5 kg/m2; 53.7%) exhibited significantly poorer QoL, marked by compromised nutritional indicators (low MUAC and SFT). Repeated measures ANOVA identified significant correlations between BMI groups in functional, social, and emotional QoL aspects (p < 0.05), with no notable differences in other domains. A Chi-square (ꭓ2) test underscored a significant link between BMI and treatment response (p < 0.0001), showing higher rates of non-responders among underweight patients (p = 4.259e-14). The study advocates pretreatment consultation with a dietitian as standard care for Indian BC patients, offering complimentary nutritional support for improved QoL outcomes and treatment responses.

Prospective validation of the role of PET/CT in detecting disease after neoadjuvant chemotherapy in advanced ovarian cancer.

OBJECTIVES: The study aimed to compare the diagnostic accuracies of 2-[18F]FDG PET/CT and contrast-enhanced CT (ceCT) after neoadjuvant chemotherapy (NACT) in advanced ovarian cancer (OC). MATERIALS AND METHODS: This study consisted historical observational cohort and prospective validation cohort. Patients with newly diagnosed stage III-IV OC scheduled for NACT were recruited, with imaging performed after three to six cycles of NACT before interval debulking surgery. Nineteen regions in the abdominopelvic cavity were scored for the presence and absence of disease, referenced to the intra-operative findings or histological specimens. Diagnostic metrics were compared using McNemar's test. RESULTS: In the historical cohort (23 patients, age 58 ± 13), 2-[18F]FDG PET had an overall accuracy (Acc) 82%, sensitivity (Sen) 38%, specificity (Spe) 97%, positive predictive value (PPV) 79% and negative predictive value (NPV) 82%; ceCT had an overall Acc 86%, Sen 64%, Spe 93%, PPV 75% and NPV 89%. In the prospective cohort (46 patients, age 59 ± 9), 2-[18F] FDG PET had an overall Acc 87%, Sen 48%, Spe 98%, PPV 84% and NPV 88%; ceCT had an overall Acc 89%, Sen 66%, Spe 95%, PPV 77% and NPV 91%. No significant difference was demonstrated between the two imaging modalities (p > 0.05). High false-negative rates were observed in the right subdiaphragmatic space, omentum, bowel mesentery and serosa. High omental metabolic uptake after NACT was associated with histological non-responders (p < 0.05). CONCLUSION: 2-[18F]FDG PET/CT had no additional value over ceCT with comparable diagnostic accuracy in detecting disease after NACT in advanced OC. CLINICAL RELEVANCE STATEMENT: 2-[18F]FDG PET/CT is not superior to contrast-enhanced CT in determining disease after neoadjuvant chemotherapy in advanced ovarian cancer; contrast-enhanced CT should be suffice for surgical planning before interval debulking surgery. KEY POINTS: • Additional value of 2-[18F]FDG PET/CT over contrast-enhanced CT is undefined in detecting disease after neoadjuvant chemotherapy. • 2-[18F]FDG PET/CT has comparable diagnostic accuracy compared to contrast-enhanced CT. • Contrast-enhanced CT will be suffice for surgical planning after neoadjuvant chemotherapy.

Preoperative chemotherapy response and survival in patients with colorectal cancer peritoneal metastases.

Treatment guidelines provided by PRODIGE-7 recommend perioperative systemic chemotherapy before cytoreductive surgery (CRS) for colorectal cancer peritoneal metastases (CRPM). Toxicity with multimodal treatment needs to be better defined. Chemotherapy response and impact on survival have not been reported. We assessed CRPM patients who received systemic oxaliplatin/irinotecan before CRS (preoperative) with Mitomycin C (35 mg/m2, 90 min) or Oxaliplatin (368 mg/m2, 30 min) heated intraperitoneal chemotherapy (HIPEC). Secondary analysis was performed from a prospective database. Overall survival (OS) in chemotherapy responders (R) and nonresponders (NR) was compared. Toxicity was assessed by rate of adverse events (AEs). From April 2005 to April 2021, 436 patients underwent CRS + HIPEC; 125 (29%) received preoperative chemotherapy. The 112 (90%) received oxaliplatin (64, 57%) or irinotecan (48, 43%). R, defined as complete (CR) or partial response on preoperative imaging and/or postoperative histology, was seen in 71, 63% (53.8-72.3); 16, 14% (8.4-22.2) had CR. Median OS in R versus NR was 43.7 months (37.9-49.4) versus 23.9 (16.3-31.4) p = 0.007, HR 0.51 (0.31-0.84). OS multivariable analysis showed HR 0.48 (0.25-0.95), p = 0.03 for chemotherapy response corrected by peritoneal cancer index, completeness of cytoreduction score. CRS led to 21% grade 3-4 AEs versus 4% for preoperative chemotherapy. HIPEC grade 3-4 AEs were 0.5%. Preoperative chemotherapy response is an independent predictor for OS in CRPM.

Multitask machine learning-based tumor-associated collagen signatures predict peritoneal recurrence and disease-free survival in gastric cancer.

BACKGROUND: Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machine learning-based tumor-associated collagen signatures (TACS), which are composed of quantitative collagen features derived from multiphoton imaging, to simultaneously predict peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS). METHODS: Among 713 consecutive patients, with 275 in training cohort, 222 patients in internal validation cohort, and 216 patients in external validation cohort, we developed and validated a multitask machine learning model for simultaneously predicting peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS). The accuracy of the model for prediction of peritoneal recurrence and prognosis as well as its association with adjuvant chemotherapy were evaluated. RESULTS: The TACSPR and TACSDFS were independently associated with peritoneal recurrence and disease-free survival in three cohorts, respectively (all P < 0.001). The TACSPR demonstrated a favorable performance for peritoneal recurrence in all three cohorts. In addition, the TACSDFS also showed a satisfactory accuracy for disease-free survival among included patients. For stage II and III diseases, adjuvant chemotherapy improved the survival of patients with low TACSPR and low TACSDFS, or high TACSPR and low TACSDFS, or low TACSPR and high TACSDFS, but had no impact on patients with high TACSPR and high TACSDFS. CONCLUSIONS: The multitask machine learning model allows accurate prediction of peritoneal recurrence and survival for GC and could distinguish patients who might benefit from adjuvant chemotherapy.

Machine Learning Developed a MYC Expression Feature-Based Signature for Predicting Prognosis and Chemoresistance in Pancreatic Adenocarcinoma.

MYC has been identified to profoundly influence a wide range of pathologic processes in cancers. However, the prognostic value of MYC-related genes in pancreatic adenocarcinoma (PAAD) remains unclarified. Gene expression data and clinical information of PAAD patients were obtained from The Cancer Genome Atlas (TCGA) database (training set). Validation sets included GSE57495, GSE62452, and ICGC-PACA databases. LASSO regression analysis was used to develop a risk signature for survival prediction. Single-cell sequencing data from GSE154778 and CRA001160 datasets were analyzed. Functional studies were conducted using siRNA targeting RHOF and ITGB6 in PANC-1 cells. High MYC expression was found to be significantly associated with a poor prognosis in patients with PAAD. Additionally, we identified seven genes (ADGRG6, LINC00941, RHOF, SERPINB5, INSYN2B, ITGB6, and DEPDC1) that exhibited a strong correlation with both MYC expression and patient survival. They were then utilized to establish a risk model (MYCsig), which showed robust predictive ability. Furthermore, MYCsig demonstrated a positive correlation with the expression of HLA genes and immune checkpoints, as well as the chemotherapy response of PAAD. RHOF and ITGB6, expressed mainly in malignant cells, were identified as key oncogenes regulating chemosensitivity through EMT. Downregulation of RHOF and ITGB6 reduced cell proliferation and invasion in PANC-1 cells. The developed MYCsig demonstrates its potential in enhancing the management of patients with PAAD by facilitating risk assessment and predicting response to adjuvant chemotherapy. Additionally, our study identifies RHOF and ITGB6 as novel oncogenes linked to EMT and chemoresistance in PAAD.

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.

BACKGROUND: Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate. OBJECTIVE: This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer. METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer. RESULTS: A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort. CONCLUSIONS: Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

Multimodal prediction of neoadjuvant treatment outcome by serial FDG PET and MRI in women with locally advanced breast cancer.

PURPOSE: To investigate combined MRI and 18F-FDG PET for assessing breast tumor metabolism/perfusion mismatch and predicting pathological response and recurrence-free survival (RFS) in women treated for breast cancer. METHODS: Patients undergoing neoadjuvant chemotherapy (NAC) for locally-advanced breast cancer were imaged at three timepoints (pre, mid, and post-NAC), prior to surgery. Imaging included diffusion-weighted and dynamic contrast-enhanced (DCE-) MRI and quantitative 18F-FDG PET. Tumor imaging measures included apparent diffusion coefficient, peak percent enhancement (PE), peak signal enhancement ratio (SER), functional tumor volume, and washout volume on MRI and standardized uptake value (SUVmax), glucose delivery (K1) and FDG metabolic rate (MRFDG) on PET, with percentage changes from baseline calculated at mid- and post-NAC. Associations of imaging measures with pathological response (residual cancer burden [RCB] 0/I vs. II/III) and RFS were evaluated. RESULTS: Thirty-five patients with stage II/III invasive breast cancer were enrolled in the prospective study (median age: 43, range: 31-66 years, RCB 0/I: N = 11/35, 31%). Baseline imaging metrics were not significantly associated with pathologic response or RFS (p > 0.05). Greater mid-treatment decreases in peak PE, along with greater post-treatment decreases in several DCE-MRI and 18F-FDG PET measures were associated with RCB 0/I after NAC (p < 0.05). Additionally, greater mid- and post-treatment decreases in DCE-MRI (peak SER, washout volume) and 18F-FDG PET (K1) were predictive of prolonged RFS. Mid-treatment decreases in metabolism/perfusion ratios (MRFDG/peak PE, MRFDG/peak SER) were associated with improved RFS. CONCLUSION: Mid-treatment changes in both PET and MRI measures were predictive of RCB status and RFS following NAC. Specifically, our results indicate a complementary relationship between DCE-MRI and 18F-FDG PET metrics and potential value of metabolism/perfusion mismatch as a marker of patient outcome.

Costimulatory molecule expression profile as a biomarker to predict prognosis and chemotherapy response for patients with small cell lung cancer.

Owing to the paucity of specimens, progress in identifying prognostic and therapeutic biomarkers for small cell lung cancer (SCLC) has been stagnant for decades. Considering that the costimulatory molecules are essential elements in modulating immune responses and determining therapeutic response, we systematically revealed the expression landscape and identified a costimulatory molecule-based signature (CMS) to predict prognosis and chemotherapy response for SCLCs for the first time. We found T cell activation was restrained in SCLCs, and costimulatory molecules exhibited widespread abnormal genetic alterations and expression. Using a LASSO Cox regression model, the CMS was built with a training cohort of 77 cases, which successfully divided patients into high- or low-risk groups with significantly different prognosis and chemotherapy benefit (both P < 0.001). The CMS was well validated in an independent cohort containing 131 samples with qPCR data. ROC and C-index analysis confirmed the superior predictive performance of the CMS in comparison with other clinicopathological parameters from different cohorts. Importantly, the CMS was confirmed as a significantly independent prognosticator for clinical outcomes and chemotherapy response in SCLCs through multivariate Cox analysis. Further analysis revealed that low-risk patients were characteristic by an activated immune phenotype with distinct expression of immune checkpoints. In summary, we firstly uncovered the expression heterogeneity of costimulatory molecules in SCLC and successfully constructed a novel predictive CMS. The identified signature contributed to more accurate patient stratification and provided robust prognostic value in estimating survival and the clinical response to chemotherapy, allowing optimization of treatment and prognosis management for patients with SCLC.

Genetic Variants in Histone Modification Regions Predict Clinical Outcomes of Pemetrexed Chemotherapy in Lung Adenocarcinoma.

OBJECTIVE: This study was conducted to investigate the association between genetic variants in histone modification regions and clinical outcomes of PEM chemotherapy in patients with lung adenocarcinoma. METHODS: Potentially functional SNPs were selected using integrated analysis of ChIP-seq and RNA-seq. The associations of 279 SNPs with chemotherapy response and overall survival (OS) were analyzed in 314 lung adenocarcinoma patients who underwent PEM chemotherapy. RESULTS: Among the SNPs investigated, 18 were significantly associated with response to chemotherapy, while 28 with OS. Of these SNPs, rs549794A>G in an enhancer which is expected to regulate the expression of ribosomal protein S3 (RPS3) gene was significantly associated with both worse response to chemotherapy and worse OS (adjusted odds ratio = 0.59, 95% CI = 0.36-0.97, p = 0.04; adjusted hazard ratio = 1.44, 95% CI = 1.09-1.91, p = 0.01, respectively). Previous studies suggested that RPS3, a multi-functional protein with various extraribosomal activities, may play a role in chemotherapy resistance. Therefore, it is postulated that rs549794-induced change in the expression level of RPS3 may affect the response to PEM chemotherapy and consequently the survival outcomes in lung adenocarcinoma patients. CONCLUSION: This study suggests that genetic variants in the histone modification regions may be useful for the prediction of clinical outcomes of PEM chemotherapy in advanced lung adenocarcinoma.

Hormetic and synergistic effects of cancer treatments revealed by modelling combinations of radio - or chemotherapy with immunotherapy.

BACKGROUND: Radio/chemotherapy and immune systems provide examples of hormesis, as tumours can be stimulated (or reduced) at low radio/chemical or antibody doses but inhibited (or stimulated) by high doses. METHODS: Interactions between effector cells, tumour cells and cytokines with pulsed radio/chemo-immunotherapy were modelled using a pulse differential system. RESULTS: Our results show that radio/chemotherapy (dose) response curves (RCRC) and/or immune response curves (IRC) or a combination of both, undergo homeostatic changes or catastrophic shifts revealing hormesis in many parameter regions. Some mixed response curves had multiple humps, posing challenges for interpretation of clinical trials and experimental design, due to a fuzzy region between an hormetic zone and the toxic threshold. Mixed response curves from two parameter bifurcation analyses demonstrated that low-dose radio/chemotherapy and strong immunotherapy counteract side-effects of radio/chemotherapy on effector cells and cytokines and stimulate effects of immunotherapy on tumour growth. The implications for clinical applications were confirmed by good fits to our model of RCRC and IRC data. CONCLUSIONS: The combination of low-dose radio/chemotherapy and high-dose immunotherapy is very effective for many solid tumours. The net benefit and synergistic effect of combined therapy is conducive to the treatment and inhibition of tumour cells.

Pathologic response to neoadjuvant chemotherapy in ovarian cancer and its association with outcome: A surrogate marker of survival.

OBJECTIVE: We aimed to validate whether pathologic response (pR) to neoadjuvant chemotherapy (NACT) using a three-tier chemotherapy response score (CRS) is associated with clinical outcome in ovarian cancer (OC) and could be used as surrogate marker for survival. METHODS: We conducted a retrospective study of OC patients with FIGO stage III/IV disease who received NACT and graded response as no or minimal (CRS 1), partial (CRS 2), or complete/near-complete (CRS 3) pR using tissue specimens obtained from omentum. Uni- and multivariate survival analyses were performed accounting for age, FIGO stage, debulking and BRCA status as well as neoadjuvant use of bevacizumab. RESULTS: CRSs 1, 2 and 3 were found in 41(31%), 62 (47%) and 30 (22%) of the 133 examined cases. Response to NACT was associated with significantly improved progression-free (PFS, p < 0.001) and overall survival (OS, p = 0.011). Complete/ near-complete pathologic response (CRS3) was associated with improved PFS (median 24.8 vs. 12.5 months, unadjusted HR 0.28 [95%CI 0.15-0.54], p < 0.001; adjusted hazard ration (aHR) 0.31 [95% CI 0.14-0.72], p = 0.007) and OS (median 63.3 vs. 32.1 months, unadjusted HR 0.27 [95%CI 0.10-0.68], p = 0.006; aHR 0.32 [95% CI 0.09-1.11], p = 0.072) when compared to no or minimal response (CRS1). CONCLUSIONS: We validate a three-tier CRS for assessment of pathologic response to NACT in OC and demonstrate its prognostic independence of BRCA status or neoadjuvant bevacizumab use. Improving pR rates may be a useful goal of NACT in OC with the expectation of improved survival. The CRS may be a useful endpoint in clinical trials in OC.

Radiotranscriptomics of non-small cell lung carcinoma for assessing high-level clinical outcomes using a machine learning-derived multi-modal signature.

BACKGROUND: Multi-omics research has the potential to holistically capture intra-tumor variability, thereby improving therapeutic decisions by incorporating the key principles of precision medicine. The purpose of this study is to identify a robust method of integrating features from different sources, such as imaging, transcriptomics, and clinical data, to predict the survival and therapy response of non-small cell lung cancer patients. METHODS: 2996 radiomics, 5268 transcriptomics, and 8 clinical features were extracted from the NSCLC Radiogenomics dataset. Radiomics and deep features were calculated based on the volume of interest in pre-treatment, routine CT examinations, and then combined with RNA-seq and clinical data. Several machine learning classifiers were used to perform survival analysis and assess the patient's response to adjuvant chemotherapy. The proposed analysis was evaluated on an unseen testing set in a k-fold cross-validation scheme. Score- and concatenation-based multi-omics were used as feature integration techniques. RESULTS: Six radiomics (elongation, cluster shade, entropy, variance, gray-level non-uniformity, and maximal correlation coefficient), six deep features (NasNet-based activations), and three transcriptomics (OTUD3, SUCGL2, and RQCD1) were found to be significant for therapy response. The examined score-based multi-omic improved the AUC up to 0.10 on the unseen testing set (0.74 ± 0.06) and the balance between sensitivity and specificity for predicting therapy response for 106 patients, resulting in less biased models and improving upon the either highly sensitive or highly specific single-source models. Six radiomics (kurtosis, GLRLM- and GLSZM-based non-uniformity from images with no filtering, biorthogonal, and daubechies wavelets), seven deep features (ResNet-based activations), and seven transcriptomics (ELP3, ZZZ3, PGRMC2, TRAK1, ATIC, USP7, and PNPLA2) were found to be significant for the survival analysis. Accordingly, the survival analysis for 115 patients was also enhanced up to 0.20 by the proposed score-based multi-omics in terms of the C-index (0.79 ± 0.03). CONCLUSIONS: Compared to single-source models, multi-omics integration has the potential to improve prediction performance, increase model stability, and reduce bias for both treatment response and survival analysis.

Skip metastases in appendicular Ewing sarcoma: relationship to distant metastases at diagnosis, chemotherapy response and overall survival.

OBJECTIVE: To identify in appendicular Ewing sarcoma (ES), if skip metastases (SM) are associated with distant metastases at presentation, response to neoadjuvant chemotherapy and overall outcome. MATERIALS AND METHOD: Patients with appendicular ES from 2007 to 2021 who had whole-bone MRI to identify SM were included in the study. Patient demographics included age/gender, bone involved, the presence of SM, longitudinal tumour length, presence of extra-osseous disease and its axial depth if present from whole-bone MRI and lung metastases and distant bone metastases from staging studies. Response to neoadjuvant chemotherapy from resection specimens and overall survival were noted. Comparison of these factors between patients with and without SM was undertaken. RESULTS: Ninety-five patients (66 males; 29 females: mean age 15.8 years; range 5-48 years) were included. On whole-bone MRI, 80 (84.2%) patients had no SM and 15 (15.8%) patients had one or more SM. Of patients without a SM, lung metastases were present in 16 (21%), distant bone metastases in 7 (11%), while 51 (75%) had a good response to chemotherapy compared with 7 (50%), 3 (27%), and 10 (77%), respectively, in patients with a SM. SM were significantly associated with lung metastases (p = 0.02), but not with distant skeletal metastases (p = 0.13), chemotherapy response (p = 0.88), tumour length (p = 0.47), presence of (p = 0.15) or axial depth of extra-osseous disease (p = 0.4). SM were associated with a significantly poorer survival (p = 0.007) and three times greater risk of death during follow-up. CONCLUSIONS: In appendicular ES, the identification of a SM is associated with the presence of lung metastases at presentation and poorer survival.

Prediction of response to preoperative neoadjuvant chemotherapy in extremity high-grade osteosarcoma using X-ray and multiparametric MRI radiomics.

PURPOSE: This study aims to evaluate the value of applying X-ray and magnetic resonance imaging (MRI) models based on radiomics feature to predict response of extremity high-grade osteosarcoma to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: A retrospective dataset was assembled involving 102 consecutive patients (training dataset, n = 72; validation dataset, n = 30) diagnosed with extremity high-grade osteosarcoma. The clinical features of age, gender, pathological type, lesion location, bone destruction type, size, alkaline phosphatase (ALP), and lactate dehydrogenase (LDH) were evaluated. Imaging features were extracted from X-ray and multi-parametric MRI (T1-weighted, T2-weighted, and contrast-enhanced T1-weighted) data. Features were selected using a two-stage process comprising minimal-redundancy-maximum-relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression. Logistic regression (LR) modelling was then applied to establish models based on clinical, X-ray, and multi-parametric MRI data, as well as combinations of these datasets. Each model was evaluated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). RESULTS: AUCs of 5 models using clinical, X-ray radiomics, MRI radiomics, X-ray plus MRI radiomics, and combination of all were 0.760 (95% CI: 0.583-0.937), 0.706 (95% CI: 0.506-0.905), 0.751 (95% CI: 0.572-0.930), 0.796 (95% CI: 0.629-0.963), 0.828 (95% CI: 0.676-0.980), respectively. The DeLong test showed no significant difference between any pair of models (p > 0.05). The combined model yielded higher performance than the clinical and radiomics models as demonstrated by net reclassification improvement (NRI) and integrated difference improvement (IDI) values, respectively. This combined model was also found to be clinically useful in the decision curve analysis (DCA). CONCLUSION: Modelling based on combination of clinical and radiomics data improves the ability to predict pathological responses to NAC in extremity high-grade osteosarcoma compared to the models based on either clinical or radiomics data.

Copy number variations and protein expression of Cyclin E1 and Epithelial cell transforming sequence 2 genes predict the chemotherapeutic response in patients with serous ovarian carcinoma.

Background & Objectives: Serous ovarian carcinoma (SOC) is characterized by extreme genomic instability, chromosomal rearrangements and copy number variations (CNVs) leading to the development of early metastasis and chemo-resistance. The present study was designed to observe the role of CNVs of Cyclin E1 (CCNE1) and Epithelial cell transforming sequence- 2 (ECT2) genes and their encoded proteins in predicting the chemotherapeutic response in SOC patients. Methods: This observational analytical study was conducted at University of Health Sciences, Lahore, Pakistan from December 2019 till June 2022.The study included twenty-five SOC patients with resectable ovarian tumors and twenty-five control subjects. The patients were followed-up for six months for their response to chemotherapy. The CNVs in CCNE1 and ECT-2 genes were determined by real time PCR while serum levels of encoded proteins were determined in controls and cases, before and after six months of treatment, through ELISA. The response to chemotherapy was categorized as sensitive or resistant based on serum CA-125 levels and radiological scans. Results: The copy number variations in CCNE1 and ECT2 genes showed association with the clinic-pathological characteristics and chemotherapy response. Statistically significant difference was found between the mean pre-chemotherapy protein levels of CCNE1 in cases than controls (p-value <0.001) and between the mean pre and post-chemotherapy protein levels of CCNE1 and ECT2 (p-value <0.001) in SOC patients. Conclusion: The copy number variations of CCNE1 and ECT2 genes and their protein expression are positively associated with chemotherapeutic response in SOC patients.

Non-invasive intravoxel incoherent motion MRI in prediction of histopathological response to neoadjuvant chemotherapy and survival outcome in osteosarcoma at the time of diagnosis.

BACKGROUND: Early prediction of response to neoadjuvant chemotherapy (NACT) is important to aid personalized treatment in osteosarcoma. Diffusion-weighted Intravoxel Incoherent Motion (IVIM) MRI was used to evaluate the predictive value for response to NACT and survival outcome in osteosarcoma. METHODS: Total fifty-five patients with biopsy-proven osteosarcoma were recruited prospectively, among them 35 patients were further analysed. Patients underwent 3 cycles of NACT (Cisplatin + Doxorubicin) followed by surgery and response adapted adjuvant chemotherapy. Treatment outcomes were histopathological response to NACT (good-response ≥ 50% necrosis and poor-response < 50% necrosis) and survival outcome (event-free survival (EFS) and overall survival (OS)). IVIM MRI was acquired at 1.5T at baseline (t0), after 1-cycle (t1) and after 3-cycles (t2) of NACT. Quantitative IVIM parameters (D, D*, f & D*.f) were estimated using advanced state-of-the-art spatial penalty based IVIM analysis method bi-exponential model with total-variation penalty function (BETV) at 3 time-points and histogram analysis was performed. RESULTS: Good-responders: Poor-responders ratio was 13 (37%):22 (63%). EFS and OS were 31% and 69% with 16.27 and 25.9 months of median duration respectively. For predicting poor-response to NACT, IVIM parameters showed AUC = 0.87, Sensitivity = 86%, Specificity = 77% at t0, and AUC = 0.96, Sensitivity = 86%, Specificity = 100% at t1. Multivariate Cox regression analysis showed smaller tumour volume (HR = 1.002, p = 0.001) higher ADC-25th-percentile (HR = 0.047, p = 0.005) & D-Mean (HR = 0.1, p = 0.023) and lower D*-Mean (HR = 1.052, p = 0.039) were independent predictors of longer EFS (log-rank p-values: 0.054, 0.0034, 0.0017, 0.0019 respectively) and non-metastatic disease (HR = 4.33, p < 10-3), smaller tumour-volume (HR = 1.001, p = 0.042), lower D*-Mean (HR = 1.045, p = 0.056) and higher D*.f-skewness (HR = 0.544, p = 0.048) were independent predictors of longer OS (log-rank p-values: < 10-3, 0.07, < 10-3, 0.019 respectively). CONCLUSION: IVIM parameters obtained with a 1.5T scanner along with novel BETV method and their histogram analysis indicating tumour heterogeneity were informative in characterizing NACT response and survival outcome in osteosarcoma.

Pre-clinical and clinical studies on the role of RBM3 in muscle-invasive bladder cancer: longitudinal expression, transcriptome-level effects and modulation of chemosensitivity.

BACKGROUND: The response to neoadjuvant cisplatin-based chemotherapy (NAC) in muscle-invasive bladder cancer (MIBC) is impaired in up to 50% of patients due to chemoresistance, with no predictive biomarkers in clinical use. The proto-oncogene RNA-binding motif protein 3 (RBM3) has emerged as a putative modulator of chemotherapy response in several solid tumours but has a hitherto unrecognized role in MIBC. METHODS: RBM3 protein expression level in tumour cells was assessed via immunohistochemistry in paired transurethral resection of the bladder (TURB) specimens, cystectomy specimens and lymph node metastases from a consecutive cohort of 145 patients, 65 of whom were treated with NAC. Kaplan-Meier and Cox regression analyses were applied to estimate the impact of RBM3 expression on time to recurrence (TTR), cancer-specific survival (CSS), and overall survival (OS) in strata according to NAC treatment. The effect of siRNA-mediated silencing of RBM3 on chemosensitivity was examined in RT4 and T24 human bladder carcinoma cells in vitro. Cellular functions of RBM3 were assessed using RNA-sequencing and gene ontology analysis, followed by investigation of cell cycle distribution using flow cytometry. RESULTS: RBM3 protein expression was significantly higher in TURB compared to cystectomy specimens but showed consistency between primary tumours and lymph node metastases. Patients with high-tumour specific RBM3 expression treated with NAC had a significantly reduced risk of recurrence and a prolonged CSS and OS compared to NAC-untreated patients. In high-grade T24 carcinoma cells, which expressed higher RBM3 mRNA levels compared to RT4 cells, RBM3 silencing conferred a decreased sensitivity to cisplatin and gemcitabine. Transcriptomic analysis revealed potential involvement of RBM3 in facilitating cell cycle progression, in particular G1/S-phase transition, and initiation of DNA replication. Furthermore, siRBM3-transfected T24 cells displayed an accumulation of cells residing in the G1-phase as well as altered levels of recognised regulators of G1-phase progression, including Cyclin D1/CDK4 and CDK2. CONCLUSIONS: The presented data highlight the potential value of RBM3 as a predictive biomarker of chemotherapy response in MIBC, which could, if prospectively validated, improve treatment stratification of patients with this aggressive disease.

Effects of neoadjuvant hyperthermic intraperitoneal chemotherapy on chemotherapy response score and recurrence in high-grade serous ovarian cancer patients with advanced disease: A multicentre retrospective cohort study.

OBJECTIVE: To investigate whether the combination of neoadjuvant hyperthermic intraperitoneal chemotherapy (NHIPEC) plus intravenous neoadjuvant chemotherapy (IV NACT) has superior efficacy to IV NACT alone. DESIGN: Retrospective cohort study. SETTING: Two tertiary referral university hospitals. POPULATION: Patients with ovarian cancer who received NACT-interval debulking surgery (IDS) between 2012 and 2020. METHODS: The tumour response to NACT was evaluated with the chemotherapy response score (CRS) system. Survival outcomes were compared. MAIN OUTCOME MEASURES: CRS 3, progression-free survival (PFS), and overall survival (OS). RESULTS: In total, 127 patients were included, and 46 received NHIPEC plus IV NACT. The addition of NHIPEC was independently associated with an increased likelihood of CRS 3 (p = 0.033). Patients who received NHIPEC + IV NACT had significantly improved PFS compared with those who received IV NACT alone (median PFS: 22 versus 16 months, p < 0.001). The use of NHIPEC was identified as an independent predictor of PFS (p < 0.0001). OS did not differ significantly between treatment groups (p = 0.062), although a trend favouring NHIPEC was noted. Incidence of grade 3-4 adverse events and the surgical complexity score of IDS were similar between the two groups. CONCLUSIONS: Compared with IV NACT alone, the combination of NHIPEC and IV NACT resulted in improved tumour response and longer PFS. The addition of NHIPEC did not increase the risk of adverse effects or affect the complexity of IDS.