Background

There is limited research on how the opioid epidemic and consequent risk reduction policies have affected pain management among cancer patients. The purpose of this study was to analyze how the Opioid Safety Initiative (OSI) implemented at the Veterans Health Administration affected opioid prescribing patterns and opioid-related toxicity.

Methods

We performed an interrupted time series analysis of 42 064 opioid-naïve patients treated at the Veterans Health Administration for prostate, lung, breast, and colorectal cancer from 2011 to 2016. Segmented regression was used to evaluate the impact of the OSI on the incidence of any new opioid prescriptions, high-risk prescriptions, persistent use, and pain-related emergency department (ED) visits. We compared the cumulative incidence of adverse opioid events including an opioid-related admission or diagnosis of misuse before and after the OSI. All statistical tests were 2-sided.

Results

The incidence of new opioid prescriptions was 26.7% (95% confidence interval [CI] = 25.0% to 28.4%) in 2011 and increased to 50.6% (95% CI = 48.3% to 53.0%) by 2013 before OSI implementation (monthly rate of change: +3.3%, 95% CI = 1.3% to 4.2%, P < .001). After the OSI, there was a decrease in the monthly rate of change for new prescriptions (-3.4%, 95% CI = -3.9 to -2.9%, P < .001). The implementation of the OSI was associated with a decrease in the monthly rate of change of concomitant benzodiazepines and opioid prescriptions (-2.5%, 95% CI = -3.2% to -1.8%, P < .001), no statistically significant change in high-dose opioids (-1.2%, 95% CI = -3.2% to 0.9%, P = .26), a decrease in persistent opioid use (-5.7%, 95% CI = -6.8% to -4.7%, P < .001), and an increase in pain-related ED visits (+3.0%, 95% CI = 1.0% to 5.0%, P = .003). The OSI was associated with a decreased incidence of opioid-related admissions (3-year cumulative incidence: 0.9% [95% CI = 0.7% to 1.0%] vs 0.5% [95% CI = 0.4% to 0.6%], P < .001) and no statistically significant change in the incidence of opioid misuse (3-year cumulative incidence: 1.2% [95% CI = 1.0% to 1.3%] vs 1.2% [95% CI = 1.1% to 1.4%], P = .77).

Conclusions

The OSI was associated with a relative decline in the rate of new, persistent, and certain high-risk opioid prescribing as well as a slight increase in the rate of pain-related ED visits. Further research on patient-centered outcomes is required to optimize opioid prescribing policies for patients with cancer.

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.

Background

RNAs within extracellular vesicles (EVs) have potential as diagnostic biomarkers for patients with cancer and are identified in a variety of biofluids. Glioblastomas (GBMs) release EVs containing RNA into cerebrospinal fluid (CSF). Here we describe a multi-institutional study of RNA extracted from CSF-derived EVs of GBM patients to detect the presence of tumor-associated amplifications and mutations in epidermal growth factor receptor (EGFR).

Methods

CSF and matching tumor tissue were obtained from patients undergoing resection of GBMs. We determined wild-type (wt)EGFR DNA copy number amplification, as well as wtEGFR and EGFR variant (v)III RNA expression in tumor samples. We also characterized wtEGFR and EGFRvIII RNA expression in CSF-derived EVs.

Results

EGFRvIII-positive tumors had significantly greater wtEGFR DNA amplification (P = 0.02) and RNA expression (P = 0.03), and EGFRvIII-positive CSF-derived EVs had significantly more wtEGFR RNA expression (P = 0.004). EGFRvIII was detected in CSF-derived EVs for 14 of the 23 EGFRvIII tissue-positive GBM patients. Conversely, only one of the 48 EGFRvIII tissue-negative patients had the EGFRvIII mutation detected in their CSF-derived EVs. These results yield a sensitivity of 61% and a specificity of 98% for the utility of CSF-derived EVs to detect an EGFRvIII-positive GBM.

Conclusion

Our results demonstrate CSF-derived EVs contain RNA signatures reflective of the underlying molecular genetic status of GBMs in terms of wtEGFR expression and EGFRvIII status. The high specificity of the CSF-derived EV diagnostic test gives us an accurate determination of positive EGFRvIII tumor status and is essentially a less invasive "liquid biopsy" that might direct mutation-specific therapies for GBMs.

Purpose

Glioblastoma is a devastating, incurable disease with few known prognostic factors. Here, we present the first genome-wide survival and validation study for glioblastoma.

Experimental design

Cox regressions for survival with 314,635 inherited autosomal single-nucleotide polymorphisms (SNP) among 315 San Francisco Adult Glioma Study patients for discovery and three independent validation data sets [87 Mayo Clinic, 232 glioma patients recruited from several medical centers in Southeastern United States (GliomaSE), and 115 The Cancer Genome Atlas patients] were used to identify SNPs associated with overall survival for Caucasian glioblastoma patients treated with the current standard of care, resection, radiation, and temozolomide (total n = 749). Tumor expression of the gene that contained the identified prognostic SNP was examined in three separate data sets (total n = 619). Genotype imputation was used to estimate hazard ratios (HR) for SNPs that had not been directly genotyped.

Results

From the discovery and validation analyses, we identified a variant in single-stranded DNA-binding protein 2 (SSBP2) on 5q14.1 associated with overall survival in combined analyses (HR, 1.64; P = 1.3 × 10(-6)). Expression of SSBP2 in tumors from three independent data sets also was significantly related to patient survival (P = 5.3 × 10(-4)). Using genotype imputation, the SSBP2 SNP rs17296479 had the strongest statistically significant genome-wide association with poorer overall patient survival (HR, 1.79; 95% CI, 1.45-2.22; P = 1.0 × 10(-7)).

Conclusion

The minor allele of SSBP2 SNP rs17296479 and the increased tumor expression of SSBP2 were statistically significantly associated with poorer overall survival among glioblastoma patients. With further confirmation, previously unrecognized inherited variations influencing survival may warrant inclusion in clinical trials to improve randomization. Unaccounted for genetic influence on survival could produce unwanted bias in such studies.

Glioma incidence is highest in non-Hispanic Whites, and to date, glioma genome-wide association studies (GWAS) to date have only included European ancestry (EA) populations. African Americans and Hispanics in the US have varying proportions of EA, African (AA) and Native American ancestries (NAA). It is unknown if identified GWAS loci or increased EA is associated with increased glioma risk. We assessed whether EA was associated with glioma in African Americans and Hispanics. Data were obtained for 832 cases and 675 controls from the Glioma International Case-Control Study and GliomaSE Case-Control Study previously estimated to have <80% EA, or self-identify as non-White. We estimated global and local ancestry using fastStructure and RFMix, respectively, using 1,000 genomes project reference populations. Within groups with ≥40% AA (AFR_≥0.4 ), and ≥15% NAA (AMR_≥0.15 ), genome-wide association between local EA and glioma was evaluated using logistic regression conditioned on global EA for all gliomas. We identified two regions (7q21.11, p = 6.36 × 10^-4 ; 11p11.12, p = 7.0 × 10^-4 ) associated with increased EA, and one associated with decreased EA (20p12.13, p = 0.0026) in AFR_≥0.4 . In addition, we identified a peak at rs1620291 (p = 4.36 × 10^-6 ) in 7q21.3. Among AMR_≥0.15 , we found an association between increased EA in one region (12q24.21, p = 8.38 × 10^-4 ), and decreased EA in two regions (8q24.21, p = 0. 0010; 20q13.33, p = 6.36 × 10^-4 ). No other significant associations were identified. This analysis identified an association between glioma and two regions previously identified in EA populations (8q24.21, 20q13.33) and four novel regions (7q21.11, 11p11.12, 12q24.21 and 20p12.13). The identifications of novel association with EA suggest regions to target for future genetic association studies.

Following publication of the original article [1], the authors reported an error in the spelling of one of the author names. In this Correction the incorrect and correct author names are indicated and the author name has been updated in the original publication. The authors also reported an error in the Methods section of the original article. In this Correction the incorrect and correct versions of the affected sentence are indicated. The original article has not been updated with regards to the error in the Methods section.

Background

Standard therapy for glioblastoma includes surgery, radiotherapy, and temozolomide. This Phase 3 trial evaluates the addition of an autologous tumor lysate-pulsed dendritic cell vaccine (DCVax^®-L) to standard therapy for newly diagnosed glioblastoma.

Methods

After surgery and chemoradiotherapy, patients were randomized (2:1) to receive temozolomide plus DCVax-L (n = 232) or temozolomide and placebo (n = 99). Following recurrence, all patients were allowed to receive DCVax-L, without unblinding. The primary endpoint was progression free survival (PFS); the secondary endpoint was overall survival (OS).

Results

For the intent-to-treat (ITT) population (n = 331), median OS (mOS) was 23.1 months from surgery. Because of the cross-over trial design, nearly 90% of the ITT population received DCVax-L. For patients with methylated MGMT (n = 131), mOS was 34.7 months from surgery, with a 3-year survival of 46.4%. As of this analysis, 223 patients are ≥ 30 months past their surgery date; 67 of these (30.0%) have lived ≥ 30 months and have a Kaplan-Meier (KM)-derived mOS of 46.5 months. 182 patients are ≥ 36 months past surgery; 44 of these (24.2%) have lived ≥ 36 months and have a KM-derived mOS of 88.2 months. A population of extended survivors (n = 100) with mOS of 40.5 months, not explained by known prognostic factors, will be analyzed further. Only 2.1% of ITT patients (n = 7) had a grade 3 or 4 adverse event that was deemed at least possibly related to the vaccine. Overall adverse events with DCVax were comparable to standard therapy alone.

Conclusions

Addition of DCVax-L to standard therapy is feasible and safe in glioblastoma patients, and may extend survival. Trial registration Funded by Northwest Biotherapeutics; Clinicaltrials.gov number: NCT00045968; https://clinicaltrials.gov/ct2/show/NCT00045968?term=NCT00045968&rank=1 ; initially registered 19 September 2002.

While molecular subgrouping has revolutionized medulloblastoma classification, the extent of heterogeneity within subgroups is unknown. Similarity network fusion (SNF) applied to genome-wide DNA methylation and gene expression data across 763 primary samples identifies very homogeneous clusters of patients, supporting the presence of medulloblastoma subtypes. After integration of somatic copy-number alterations, and clinical features specific to each cluster, we identify 12 different subtypes of medulloblastoma. Integrative analysis using SNF further delineates group 3 from group 4 medulloblastoma, which is not as readily apparent through analyses of individual data types. Two clear subtypes of infants with Sonic Hedgehog medulloblastoma with disparate outcomes and biology are identified. Medulloblastoma subtypes identified through integrative clustering have important implications for stratification of future clinical trials.

Telomerase reverse transcriptase (TERT) promoter mutations were recently shown to drive telomerase activity in various cancer types, including medulloblastoma. However, the clinical and biological implications of TERT mutations in medulloblastoma have not been described. Hence, we sought to describe these mutations and their impact in a subgroup-specific manner. We analyzed the TERT promoter by direct sequencing and genotyping in 466 medulloblastomas. The mutational distributions were determined according to subgroup affiliation, demographics, and clinical, prognostic, and molecular features. Integrated genomics approaches were used to identify specific somatic copy number alterations in TERT promoter-mutated and wild-type tumors. Overall, TERT promoter mutations were identified in 21 % of medulloblastomas. Strikingly, the highest frequencies of TERT mutations were observed in SHH (83 %; 55/66) and WNT (31 %; 4/13) medulloblastomas derived from adult patients. Group 3 and Group 4 harbored this alteration in <5 % of cases and showed no association with increased patient age. The prognostic implications of these mutations were highly subgroup-specific. TERT mutations identified a subset with good and poor prognosis in SHH and Group 4 tumors, respectively. Monosomy 6 was mostly restricted to WNT tumors without TERT mutations. Hallmark SHH focal copy number aberrations and chromosome 10q deletion were mutually exclusive with TERT mutations within SHH tumors. TERT promoter mutations are the most common recurrent somatic point mutation in medulloblastoma, and are very highly enriched in adult SHH and WNT tumors. TERT mutations define a subset of SHH medulloblastoma with distinct demographics, cytogenetics, and outcomes.

Purpose

Medulloblastoma comprises four distinct molecular subgroups: WNT, SHH, Group 3, and Group 4. Current medulloblastoma protocols stratify patients based on clinical features: patient age, metastatic stage, extent of resection, and histologic variant. Stark prognostic and genetic differences among the four subgroups suggest that subgroup-specific molecular biomarkers could improve patient prognostication.

Patients and methods

Molecular biomarkers were identified from a discovery set of 673 medulloblastomas from 43 cities around the world. Combined risk stratification models were designed based on clinical and cytogenetic biomarkers identified by multivariable Cox proportional hazards analyses. Identified biomarkers were tested using fluorescent in situ hybridization (FISH) on a nonoverlapping medulloblastoma tissue microarray (n = 453), with subsequent validation of the risk stratification models.

Results

Subgroup information improves the predictive accuracy of a multivariable survival model compared with clinical biomarkers alone. Most previously published cytogenetic biomarkers are only prognostic within a single medulloblastoma subgroup. Profiling six FISH biomarkers (GLI2, MYC, chromosome 11 [chr11], chr14, 17p, and 17q) on formalin-fixed paraffin-embedded tissues, we can reliably and reproducibly identify very low-risk and very high-risk patients within SHH, Group 3, and Group 4 medulloblastomas.

Conclusion

Combining subgroup and cytogenetic biomarkers with established clinical biomarkers substantially improves patient prognostication, even in the context of heterogeneous clinical therapies. The prognostic significance of most molecular biomarkers is restricted to a specific subgroup. We have identified a small panel of cytogenetic biomarkers that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials.