A novel combined miRNA and methylation marker panel (miMe) for prediction of prostate cancer outcome after radical prostatectomy.

Improved prognostic biomarkers are needed to guide personalized prostate cancer (PC) treatment decisions. Due to the prominent molecular heterogeneity of PC, multimarker panels may be more robust. Here, 25 selected top-candidate miRNA and methylation markers for PC were profiled by qPCR in malignant radical prostatectomy (RP) tissue specimens from 198 PC patients (Cohort 1, training). Using GLMnet, we trained a novel multimarker model (miMe) comprising nine miRNAs and three methylation markers that predicted postoperative biochemical recurrence (BCR) independently of the established clinicopathological CAPRA-S nomogram in Cox multivariate regression analysis in Cohort 1 (HR [95% CI]: 1.53 [1.26-1.84], p < 0.001). This result was successfully validated in two independent RP cohorts (Cohort 2, n = 159: HR [95% CI]: 1.35 [1.06-1.73], p = 0.015. TCGA, n = 350: HR [95% CI]: 1.34 [1.01-1.77], p = 0.04). Notably, in CAPRA-S low-risk patients, a high miMe score was associated with >6 times higher risk of BCR, suggesting that miMe may help identify PC patients at high risk of progression despite favorable clinicopathological factors postsurgery. Finally, miMe was a significant predictor of cancer-specific survival (p = 0.019, log-rank test) in a merged analysis of 357 RP patients. In conclusion, we trained, tested and validated a novel 12-marker panel (miMe) that showed significant independent prognostic value in three RP cohorts. In the future, combining miMe score with existing clinical nomograms may improve PC risk stratification and thus help guide treatment decisions.

Radiotherapy to reinvigorate immunotherapy activity after acquired resistance in metastatic non-small-cell lung cancer: A pooled analysis of two institutions prospective phase II single arm trials.

AIM: The current work aimed to investigate the clinical benefit of radiotherapy in patients with metastatic non-small cell lung cancer (NSCLC) developing acquired resistance to immune checkpoint inhibitors. METHOD: We report on a pooled, two-institution, phase II single-arm prospective cohort study. The study included patients with stage IV NSCLC who showed progression of one or more measurable lesions under anti-PD-(L)1 inhibition alone, after initially having achieved at least stable disease. Hypofractionated radiotherapy (hRT) of one to four metastases was performed, while one or more lesions were kept untreated. Following hRT, treatment with immune checkpoint inhibitors was continued unchanged until further evidence of tumor progression or unacceptable toxicity. Primary endpoint of the pooled analysis was progression-free survival (PFS), secondary endpoints included overall survival (OS) and toxicity. RESULTS: A total of 48 patients were enrolled: mean age was 67.1 ± 9.3 years, 50 % were male and 72.9 % were PD-L1 positive. Immunotherapy was in 95.8 % of patients the first or second line therapy at time of enrollment. hRT was performed to one (93.8 % of cases) or more lesions (median total dose: 27.5 Gy, median 6.5 Gy/fraction). Forty-five patients (93.8 %) were able to continue immunotherapy for a median of 6.2 months following hRT. Median PFS was 4.4 months, with 62.5 % disease control at three months and 37.5 % at six months. Median OS was 14.9 months. Severe adverse events (grade ≥ 2) were reported in 12 cases (25 %), of which none were radiotherapy-related and four were immunotherapy-related. Salvage therapy consisted of chemotherapy (48.8 %) or repeated irradiation (21.9 %). No further tumor treatment was performed in 29.3 % of patients. CONCLUSIONS: The current pooled analysis is a prospective evaluation of the role of radiation therapy for metastatic NSCLC in the setting of newly acquired immunotherapy resistance. Hypofractionated radiotherapy can support the outcome of immune checkpoint inhibitors and thus allow continuation of treatment for a relevant amount of time despite initial tumor progression.

Spontaneous activity of the mitochondrial apoptosis pathway drives chromosomal defects, the appearance of micronuclei and cancer metastasis through the Caspase-Activated DNAse.

Micronuclei are DNA-containing structures separate from the nucleus found in cancer cells. Micronuclei are recognized by the immune sensor axis cGAS/STING, driving cancer metastasis. The mitochondrial apoptosis apparatus can be experimentally triggered to a non-apoptotic level, and this can drive the appearance of micronuclei through the Caspase-activated DNAse (CAD). We tested whether spontaneously appearing micronuclei in cancer cells are linked to sub-lethal apoptotic signals. Inhibition of mitochondrial apoptosis or of CAD reduced the number of micronuclei in tumor cell lines as well as the number of chromosomal misalignments in tumor cells and intestinal organoids. Blockade of mitochondrial apoptosis or deletion of CAD reduced, while experimental activation CAD, STING-dependently, enhanced aggressive growth of tumor cells in vitro. Deletion of CAD from human cancer cells reduced metastasis in xenograft models. CAD-deficient cells displayed a substantially altered gene-expression profile, and a CAD-associated gene expression 'signature' strongly predicted survival in cancer patients. Thus, low-level activity in the mitochondrial apoptosis apparatus operates through CAD-dependent gene-induction and STING-activation and has substantial impact on metastasis in cancer.

Hierarchical Classification of Cancers of Unknown Primary Using Multi-Omics Data.

A cancer of unknown primary (CUP) is a metastatic cancer for which standard diagnostic tests fail to locate the primary cancer. As standard treatments are based on the cancer type, such cases are hard to treat and have very poor prognosis. Using molecular data from the metastatic cancer to predict the primary site can make treatment choice easier and enable targeted therapy. In this article, we first examine the ability to predict cancer type using different types of omics data. Methylation data lead to slightly better prediction than gene expression and both these are superior to classification using somatic mutations. After using 3 data types independently, we notice some differences between the classes that tend to be misclassified, suggesting that integrating the data might improve accuracy. In light of the different levels of information provided by different omics types and to be able to handle missing data, we perform multi-omics classification by hierarchically combining the classifiers. The proposed hierarchical method first classifies based on the most informative type of omics data and then uses the other types of omics data to classify samples that did not get a high confidence classification in the first step. The resulting hierarchical classifier has higher accuracy than any of the single omics classifiers and thus proves that the combination of different data types is beneficial. Our results show that using multi-omics data can improve the classification of cancer types. We confirm this by testing our method on metastatic cancers from the MET500 dataset.