Genomic Landscape of Vinflunine Response in Metastatic Urothelial Cancer

Background and Aims: Metastatic urothelial carcinoma (mUC) remains an incurable disease with limited treatment options after platinum-based chemotherapy and immune checkpoint blockade (ICB). Vinflunine has shown a modest increase in overall survival and remains a therapeutic option for chemo- and immunotherapy refractory tumours. However, biomarkers that could identify responding patients to vinflunine and possible alternative therapies after failure to treatment are still missing. In this study, we aimed to identify potential genomic biomarkers of vinflunine response in mUC patient samples and potential management alternatives. Methods: Formalin-fixed paraffin-embedded samples of mUC patients (n = 23) from three university hospitals in Spain were used for genomic targeted-sequencing and transcriptome (using the Immune Profile panel by NanoString) analyses. Patients who received vinflunine after platinum-based chemotherapy failure were classified in non-responders (NR: progressive disease ≤ 3 months; n= 11) or responders (R: response ≥ 6 months; n = 12). Results: Genomic characterization revealed that the most common alteration, TP53 mutations, had comparable frequency in R (6/12; 50%) and NR (4/11; 36%). Non-synonymous mutations in KTM2C (4/12; 33.3%), PIK3CA (3/12; 25%) and ARID2 (3/12; 25%) were predominantly associated with response. No significant difference was observed in tumour mutational burden (TMB) between R and NR patients. The NR tumours showed increased expression of diverse immune-related genes and pathways, including various interferon gamma-related genes. We also identified increased MAGEA4 expression as a potential biomarker of non-responding tumours to vinflunine treatment. Conclusions: Our data may help to identify potential genomic biomarkers of response to vinflunine. Moreover, tumours refractory to vinflunine showed immune signatures potentially associated with response to ICB. Extensive validation studies, including longitudinal series, are needed to corroborate these findings.

Prognostic Genomic Predictive Biomarkers for Early-Stage Lung Cancer Patients

Aims: Our goal is to find predictive genomic biomarkers in order to identify subgroups of early-stage lung cancer patients that are most likely to benefit from adjuvant chemotherapy with surgery (ACT). Background: Receiving ACT appears to have a better prognosis for more severe early-stage non-small cell lung cancer patients than surgical resection only. However, not all patients benefit from chemotherapy. Objective: Preliminary studies suggest that the application of ACT is associated with a better prognosis for more severe NSCLC patients compared to those who only underwent surgical resection. Given the immense personal and financial costs associated with ACT, finding the patients who are most likely to benefit from ACT is paramount. Thus, the purpose of this research is to utilize gene expression and clinical data from lung cancer patients to find treatment-associated genomic biomarkers. Methods: To investigate the treatment effect, a modified-covariate regularized Cox regression model with lasso penalty is implemented using National Cancer Institute gene expression data to find genomic biomarkers. Results: This research utilized an independent validation dataset involving 318 lung cancer patients to validate the models. In the validation set with 318 patients, the modified covariate Cox model with lasso penalty were able to show patients who followed their predicted recommendation (either ACT for low-risk group or OBS for the high-risk group, n = 171) have higher survival benefits than 147 patients who did not follow the recommendations (p < .0001). Conclusion: Based on validation data, patients who follow our predicted recommendation by genomic biomarkers selected from the proposed model will likely benefit from ACT.

FRL: An Integrative Feature Selection Algorithm Based on the Fisher Score, Recursive Feature Elimination, and Logistic Regression to Identify Potential Genomic Biomarkers

Accurate screening on cancer biomarkers contributes to health assessment, drug screening, and targeted therapy for precision medicine. The rapid development of high-throughput sequencing technology has identified abundant genomic biomarkers, but most of them are limited to single-cancer analysis. Based on the combination of Fisher score, Recursive feature elimination, and Logistic regression (FRL), this paper proposes an integrative feature selection algorithm named FRL to explore potential cancer genomic biomarkers on cancer subsets. Fisher score is initially used to calculate the weights of genes to rapidly reduce the dimension. Recursive feature elimination and Logistic regression are then jointly employed to extract the optimal subset. Compared to the current differential expression analysis tool GEO2R based on the Limma algorithm, FRL has greater classification precision than Limma. Compared with five traditional feature selection algorithms, FRL exhibits excellent performance on accuracy (ACC) and F1-score and greatly improves computational efficiency. On high-noise datasets such as esophageal cancer, the ACC of FRL is 30% superior to the average ACC achieved with other traditional algorithms. As biomarkers found in multiple studies are more reliable and reproducible, and reveal stronger association on potential clinical value than single analysis, through literature review and spatial analyses of gene functional enrichment and functional pathways, we conduct cluster analysis on 10 diverse cancers with high mortality and form a potential biomarker module comprising 19 genes. All genes in this module can serve as potential biomarkers to provide more information on the overall oncogenesis mechanism for the detection of diverse early cancers and assist in targeted anticancer therapies for further developments in precision medicine.

Using real-world outcomes to evaluate the predictive power of tissue-assessed genomic biomarkers for taxane versus novel hormonal therapy (NHT) outcomes in metastatic castration-resistant prostate cancer (mCRPC).

5054 Background: No established genomic biomarkers exist for guiding treatment decisions between novel hormonal therapy (NHT) vs taxane chemotherapy in mCRPC. However, specific alterations in AR have been associated to decreased responsiveness to NHT in this setting. Leveraging routine comprehensive genomic profiling (CGP) testing of mCRPC tissue samples, we hypothesized that patients (pts) with AR amplification ( ARamp) would have better outcomes on taxanes over NHT. Methods: Pts were selected from Flatiron Health (FH)-Foundation Medicine (FMI) clinico-genomic database (CGDB), a nationwide deidentified electronic health record database linked to CGP. Data originated from approximately 280 US cancer clinics (̃800 sites). CGP results (including analysis of AR and 15 other genomic biomarkers) were obtained from mCRPC tumor tissue collected up to 180 days before or 30 days after initiation of new systemic therapy between 1/1/11 - 6/30/20, and linked to PSA response, time to next therapy (TTNT) and overall survival (OS). Multivariable treatment interaction models were adjusted for drug assignment imbalances (line of therapy, prior NHT or taxane use, PSA, alkaline phosphatase, hemoglobin, albumin, years to CRPC, biopsy site) using inverse probability of treatment weighting via propensity scores. Results: Among 5754 evaluable mCRPC lines of therapy, 180 receiving NHT and 179 receiving taxanes met inclusion criteria, 359 total from 308 unique patients. Pts with ARamp vs no ARamp on NHT had worse PSA response (median +57.3% vs. -31.4%, p = 0.002), TTNT (HR: 2.03, p < 0.001), and OS (HR: 2.28, p < 0.001), but had no difference in outcomes on taxanes. Multivariable interaction Cox models found ARamp independently associated to better TTNT on taxanes vs. NHT (HR: 0.48, p = 0.010), similar to pts with RB1 alterations (HR: 0.46, p = 0.027). Consistent treatment interactions were seen with OS for ARamp (HR: 0.53, p = 0.025) and RB1 (HR: 0.32, p = 0.024). While CDK12 was not predictive, it independently associated with worse OS overall (HR: 2.25, p = 0.0011). In the 55 pts who received NHT followed by taxane immediately after, ARamp pre-NHT was associated with better TTNT on the subsequent taxane than on the initial NHT (HR: 0.40, p = 0.028). Of these, 33 had PSA responses evaluable, and ARamp pre-NHT was significantly associated with better PSA decline on the subsequent taxane, despite disadvantage of first progressing on NHT (OR: 10.9, p = 0.021). Conclusions: Genomic biomarkers routinely identified with CGP such as ARamp may aid in identifying mCRPC pts who are to obtain greater benefit from taxane chemotherapy instead of NHT. Prospective efforts are needed to further validate the utility of CGP for assisting treatment decisions for mCRPC patients.