Van Allen, Eliezer M

Abstract Purpose: Inhibitors of the Ataxia telangiectasia and Rad3-related (ATR) protein kinase, a critical component of the DNA damage repair response, have synergistic anti-cancer activity with platinum compounds in preclinical models. We therefore conducted a Phase 2 study of the ATR inhibitor berzosertib+carboplatin vs. docetaxel+carboplatin in metastatic castration-resistant prostate cancer. Patients and Methods: Patients previously treated with at least one androgen receptor pathway inhibitor and taxane underwent mandatory biopsy and were randomized 1:1 to receive Arm A (docetaxel 60 mg/m2+carboplatin AUC4 day 1; or carboplatin AUC5 if not a docetaxel candidate) or Arm B (berzosertib 90 mg/m2 days 2,9+carboplatin AUC5 day 1) every 21 days. The primary endpoint was overall response rate (ORR; ≥50% PSA decline or radiographic response). Results: Of 73 randomized patients, 65 received protocol treatment: 34 on Arm A (26 docetaxel+carboplatin; 8 carboplatin alone) and 31 on Arm B. Thirteen patients (38%) in Arm A and 20 (65%) in Arm B had ≥Grade 3 treatment-related adverse events (TrAEs). ORR was 15% in Arm A (5/34; 5/26[19%] with docetaxel+carboplatin) and 0% in Arm B (0/31). At interim analysis after 65 of planned 130 patients were treated, enrollment was halted due to futility. Homologous recombination repair deficiency status from tissue and blood did not correlate with clinical outcomes, but ATM-deficiency or high levels of phoshpo-KAP1 detected in tissue was associated with clinical benefit across both arms of the trial. Conclusions: Berzosertib+carboplatin demonstrated lower ORR and more frequent ≥grade 3 TrAEs compared to docetaxel+carboplatin in this heavily pre-treated, biomarker-unselected population. NCT03517969

PURPOSE Precision medicine has revolutionized oncology; however, tumor biomarkers are not reflective of the heterogeneous cancer population. We evaluated NRG Oncology prostate cancer (PCa) clinical trials for demographic differences among patients with optional biospecimen collection (BC) consent and biospecimen submission (BSub). METHODS Data from 19 NRG PCa clinical trials closed before 2015 were analyzed. Patients who consented to BC and completed BSub were evaluated by race, ethnicity, median income, area deprivation index (ADI; categorized as highest v lowest three quartiles), age at enrollment, site, and year of enrollment. T/chi-square tests were used for continuous/categorical variables, respectively, followed by logistic regression. RESULTS Of the 15,648 randomized patients eligible for BC, 11,796 (75%) had specimens submitted. In all, 4,598 (82.2%) of 5,597 eligible patients consented for optional BC in nine clinical trials with a separate BC consent process (consent rates by race/ethnicity: 74.1% Black, 72.8% Hispanic/Latino, 83.8% White). A smaller proportion of Black and Hispanic/Latino patients consented to optional BC compared with those who did not (12.1% v 19.5% Black, P < .0001; 3.5% v 5.8% Hispanic, P = .0006). In univariable logistic regression models, high ADI (more socioeconomic disadvantage) was associated with a decreased likelihood for optional BC consent (odds ratio [OR], 0.67 [95% CI, 0.55 to 0.82]; P = .02), but not a decreased likelihood for BSub (OR, 0.74 [95% CI, 0.53 to 1.04]; P = .08). Multivariable models demonstrated that Black/Hispanic/Latino patients were less likely to consent to optional BC, and Black patients were less likely to have BSub ( P < .05 for all). CONCLUSION White/non-Hispanic patients and those with less socioeconomic disadvantage were more likely to consent to optional BC, whereas Black patients were less likely to have BSub. Targeted solutions are needed to improve biorepository representation so that precision medicine approaches better reflect the cancer population.

Abstract Categorical variants, or sets of genomic alterations constrained by shared properties, are pervasive across clinical, regulatory, and research domains in the biomedical ecosystem, yet their inconsistent and non-computable representation hinders data interoperability and clinical interpretation. We surveyed genomic knowledgebases spanning regulatory approvals and the biomedical literature and found that categorical variants underpin a substantial proportion of clinical genomics knowledge, but are largely described using incompatible bespoke models. To address this, we developed the GA4GH Categorical Variation Representation Specification (Cat-VRS), a constraint-based framework that provides a unified computable representation for both precise and intentionally broad categories across molecular and systemic variant domains. Cat-VRS enables harmonization of genomic knowledgebases, computable category-based search, and automated matching between assayed variants and categorical entities in clinical and research contexts. By providing a principled, extensible model for categorical variation, Cat-VRS enables computable reasoning over genomic variant categories and establishes a foundation for the standardized representation and exchange of genomic knowledge.

SUMMARY Ewing sarcoma (EwS) is a group of bone and soft tissue cancers in children and young adults. Since EwS cells have pronounced sensitivity to radiation and chemotherapy-induced DNA damage, the role of the oncoprotein, EWS-FLI1, in DNA repair is likely. Here, we demonstrate that EWS-FLI1 causes a defect in microhomology-mediated end-joining (MMEJ) repair. EWSR1 is a splicing factor that promotes the faithful splicing of the POLQ pre-mRNA, required for the expression of POLΘ, a critical protein in the MMEJ pathway. Expression of EWS-FLI1, or loss of EWSR1, causes exon 25 skipping of the POLQ transcript, decreased POLΘ expression, impaired MMEJ, and cellular sensitivity to inhibitors of the Fanconi Anemia (FA), NHEJ, or HR pathways, through the mechanism of synthetic lethality. Knockdown of EWS-FLI1 expression restores POL0 mitotic foci and increases MMEJ activity. Inhibitors of the FA, NHEJ, or HR therefore may provide a targeted therapy for patients with EwS. Highlights - Ewing sarcoma tumors have a deficiency in POLθ expression and a corresponding loss of MMEJ activity - EWSR1 is a splicing factor that interacts with other splicing factors such as FUBP1 and KHSRP/FUBP2 to accurately splice the POLQ mRNA. - The EWS-FLI1 fusion oncoprotein, or loss of EWSR1, causes a splicing defect, leading to exon25 skipping of the POLQ pre-mRNA and loss of POLθ expression - The MMEJ deficiency of EwS cells results in cellular sensitivity to inhibitors of Fanconi Anemia, Homologous Recombination or Non-Homologous End-Joining - Exon 25 skipping of POLQ mRNA is a predictive biomarker for HR inhibitors in human cancers

Importance Artificial intelligence (AI) tools are rapidly integrating into cancer care. Understanding stakeholder views on ethical issues associated with the implementation of AI in oncology is critical to optimal deployment. Objective To evaluate oncologists’ views on the ethical domains of the use of AI in clinical care, including familiarity, predictions, explainability (the ability to explain how a result was determined), bias, deference, and responsibilities. Design, Setting, and Participants This cross-sectional, population-based survey study was conducted from November 15, 2022, to July 31, 2023, among 204 US-based oncologists identified using the National Plan &amp;amp; Provider Enumeration System. Main Outcomes and Measures The primary outcome was response to a question asking whether participants agreed or disagreed that patients need to provide informed consent for AI model use during cancer treatment decisions. Results Of 387 surveys, 204 were completed (response rate, 52.7%). Participants represented 37 states, 120 (63.7%) identified as male, 128 (62.7%) as non-Hispanic White, and 60 (29.4%) were from academic practices; 95 (46.6%) had received some education on AI use in health care, and 45.3% (92 of 203) reported familiarity with clinical decision models. Most participants (84.8% [173 of 204]) reported that AI-based clinical decision models needed to be explainable by oncologists to be used in the clinic; 23.0% (47 of 204) stated they also needed to be explainable by patients. Patient consent for AI model use during treatment decisions was supported by 81.4% of participants (166 of 204). When presented with a scenario in which an AI decision model selected a different treatment regimen than the oncologist planned to recommend, the most common response was to present both options and let the patient decide (36.8% [75 of 204]); respondents from academic settings were more likely than those from other settings to let the patient decide (OR, 2.56; 95% CI, 1.19-5.51). Most respondents (90.7% [185 of 204]) reported that AI developers were responsible for the medico-legal problems associated with AI use. Some agreed that this responsibility was shared by physicians (47.1% [96 of 204]) or hospitals (43.1% [88 of 204]). Finally, most respondents (76.5% [156 of 204]) agreed that oncologists should protect patients from biased AI tools, but only 27.9% (57 of 204) were confident in their ability to identify poorly representative AI models. Conclusions and Relevance In this cross-sectional survey study, few oncologists reported that patients needed to understand AI models, but most agreed that patients should consent to their use, and many tasked patients with choosing between physician- and AI-recommended treatment regimens. These findings suggest that the implementation of AI in oncology must include rigorous assessments of its effect on care decisions as well as decisional responsibility when problems related to AI use arise.

Introduction Artificial Intelligence (AI) is an emerging technology that promises advancements across blood cancer discovery and care delivery. There are already FDA-approved uses of AI in hematology and oncology but few data available about its use or utility. Concerns have been raised over AI's potential biases, “explainability,” and who is responsible for its use. Oncologists' familiarity with AI and perspectives on these issues are not well known and are critical to understanding the ethical deployment of AI in blood cancer care. Methods From January to July 2023, we performed a national cross-sectional survey of practicing oncologists. A draft survey instrument was developed by a team with content and methodological expertise. Iterative refinement occurred through cognitive debriefing with 5 practicing hematologist/oncologists. The final instrument included 24 questions covering domains of AI familiarity, predictions, explainability, and responsibilities. A random sample of US oncologists was identified using the National Plan & Provider Enumeration System. Paper surveys were mailed with $25 incentivizing gift cards. Reminder letters and phone calls were used for non-responders with an electronic survey option provided. Responses were analyzed descriptively. X2 testing assessed bivariate associations between responses and participant practice setting, with odds ratios (OR) reported. Results Of 399 mailed surveys, 12 were undeliverable and 201 were completed for a response rate of 51.9%. Question missingness was &lt;1%. Participants were 63.2% White, 16.9% Indian Asian, 10.0% Eastern Asian or Pacific Islander, and 4.5% Black; 6.0% were Hispanic. They represented 37 states. The majority were male (63.7%), 40-59 years old (54.2%), and had no specific training in AI (52.2%); 68.2% had at least 11 years in practice and 29.9% practiced at a primary academic hospital. Questions assessing overall familiarity with and predictions related to AI are shown in Table 1. Those at primary academic hospitals were more likely to be familiar with radiology models (OR 2.82, p&lt;0.001) but not pathology, prognostic, or clinical decision models (all p&gt;0.05). They were more likely to predict that AI would improve side effect management (OR 2.01, p=0.03) and end-of-life decision making (OR 2.07, p=0.02). High proportions of respondents thought that AI prognostic (67.1%) and clinical decision models (62.1%) needed to be explainable by clinicians and researchers to be used in clinic. When posed with a scenario where an FDA-approved AI model selects a different regimen than the oncologist planned to propose (Figure 1), the largest proportion reported they would present both options and let the patient decide (37.3%). Of those recommending a regimen, respondents from primary academic hospitals more frequently recommend the AI-generated regimen (56.1%), while respondents from elsewhere recommended their original regimen (69.0%; OR 2.85, p=0.007). Acceptability of some direct-to-patient AI model applications is shown in Table 1. A majority of respondents (90.5%) thought that AI developers should be responsible for medico-legal problems caused by AI use; 47.8% thought that physicians using the tool should be responsible. Though most (76.1%) agreed that it was oncologists' responsibility to protect patients from biased AI tools, few (27.3%) were confident in their ability to identify how representative the data used in an AI model was relative to the population with that cancer (including 66.7% of those who agreed it was oncologists' responsibility). More respondents thought that patients should need to consent to the use of AI tools in cancer treatment decisions (81.1%) than cancer diagnosis (55.7%); those from primary academic hospitals were less likely to respond that patients should consent for treatment or diagnosis (OR 0.43, p=0.02 and OR 0.44, p=0.009). Conclusions In this national survey of oncologists' views on AI, there was optimism about its impact on care with notable caveats related to end-of-life care, patient trust, and care disparities. Despite many approving use of AI models too complex for patients to explain, a substantial proportion deferred to patient choice when AI and physician recommendations differed. There was also dissonance between accepting responsibility to protect patients from bias and respondents' self-perceived ability to do so.

ImportanceSystematic data on the association between anticancer therapies and thromboembolic events (TEEs) in patients with COVID-19 are lacking.ObjectiveTo assess the association between anticancer therapy exposure within 3 months prior to COVID-19 and TEEs following COVID-19 diagnosis in patients with cancer.Design, Setting, and ParticipantsThis registry-based retrospective cohort study included patients who were hospitalized and had active cancer and laboratory-confirmed SARS-CoV-2 infection. Data were accrued from March 2020 to December 2021 and analyzed from December 2021 to October 2022.ExposureTreatments of interest (TOIs) (endocrine therapy, vascular endothelial growth factor inhibitors/tyrosine kinase inhibitors [VEGFis/TKIs], immunomodulators [IMiDs], immune checkpoint inhibitors [ICIs], chemotherapy) vs reference (no systemic therapy) in 3 months prior to COVID-19.Main Outcomes and MeasuresMain outcomes were (1) venous thromboembolism (VTE) and (2) arterial thromboembolism (ATE). Secondary outcome was severity of COVID-19 (rates of intensive care unit admission, mechanical ventilation, 30-day all-cause mortality following TEEs in TOI vs reference group) at 30-day follow-up.ResultsOf 4988 hospitalized patients with cancer (median [IQR] age, 69 [59-78] years; 2608 [52%] male), 1869 had received 1 or more TOIs. Incidence of VTE was higher in all TOI groups: endocrine therapy, 7%; VEGFis/TKIs, 10%; IMiDs, 8%; ICIs, 12%; and chemotherapy, 10%, compared with patients not receiving systemic therapies (6%). In multivariable log-binomial regression analyses, relative risk of VTE (adjusted risk ratio [aRR], 1.33; 95% CI, 1.04-1.69) but not ATE (aRR, 0.81; 95% CI, 0.56-1.16) was significantly higher in those exposed to all TOIs pooled together vs those with no exposure. Among individual drugs, ICIs were significantly associated with VTE (aRR, 1.45; 95% CI, 1.01-2.07). Also noted were significant associations between VTE and active and progressing cancer (aRR, 1.43; 95% CI, 1.01-2.03), history of VTE (aRR, 3.10; 95% CI, 2.38-4.04), and high-risk site of cancer (aRR, 1.42; 95% CI, 1.14-1.75). Black patients had a higher risk of TEEs (aRR, 1.24; 95% CI, 1.03-1.50) than White patients. Patients with TEEs had high intensive care unit admission (46%) and mechanical ventilation (31%) rates. Relative risk of death in patients with TEEs was higher in those exposed to TOIs vs not (aRR, 1.12; 95% CI, 0.91-1.38) and was significantly associated with poor performance status (aRR, 1.77; 95% CI, 1.30-2.40) and active/progressing cancer (aRR, 1.55; 95% CI, 1.13-2.13).Conclusions and RelevanceIn this cohort study, relative risk of developing VTE was high among patients receiving TOIs and varied by the type of therapy, underlying risk factors, and demographics, such as race and ethnicity. These findings highlight the need for close monitoring and perhaps personalized thromboprophylaxis to prevent morbidity and mortality associated with COVID-19–related thromboembolism in patients with cancer.

Importance The effectiveness of triplet therapy compared with androgen pathway inhibitor (API) doublets in a heterogeneous patient population with metastatic castration-sensitive prostate cancer (mCSPC) is unknown. Objective To assess the comparative effectiveness of contemporary systemic treatment options for patients with mCSPC across clinically relevant subgroups. Data Sources For this systematic review and meta-analysis, Ovid MEDLINE and Embase were searched from each database’s inception (MEDLINE, 1946; Embase, 1974) through June 16, 2021. Subsequently, a “living” auto search was created with weekly updates to identify new evidence as it became available. Study Selection Phase 3 randomized clinical trials (RCTs) assessing first-line treatment options for mCSPC. Data Extraction and Synthesis Two independent reviewers extracted data from eligible RCTs. The comparative effectiveness of different treatment options was assessed with a fixed-effect network meta-analysis. Data were analyzed on July 10, 2022. Main Outcomes and Measures Outcomes of interest included overall survival (OS), progression-free survival (PFS), grade 3 or higher adverse events, and health-related quality of life. Results This report included 10 RCTs with 11 043 patients and 9 unique treatment groups. Median ages of the included population ranged from 63 to 70 years. Current evidence for the overall population suggests that the darolutamide (DARO) triplet (DARO + docetaxel [D] + androgen deprivation therapy [ADT]; hazard ratio [HR], 0.68; 95% CI, 0.57-0.81), as well as the abiraterone (AAP) triplet (AAP + D + ADT; HR, 0.75; 95% CI, 0.59-0.95), are associated with improved OS compared with D doublet (D + ADT) but not compared with API doublets. Among patients with high-volume disease, AAP + D + ADT may improve OS compared with D + ADT (HR, 0.72; 95% CI, 0.55-0.95) but not compared with AAP + ADT, enzalutamide (E) + ADT, and apalutamide (APA) + ADT. For patients with low-volume disease, AAP + D + ADT may not improve OS compared with APA + ADT, AAP + ADT, E + ADT, and D + ADT. Conclusions and Relevance The potential benefit observed with triplet therapy must be interpreted with careful accounting for the volume of disease and the choice of doublet comparisons used in the clinical trials. These findings suggest an equipoise to how triplet regimens compare with API doublet combinations and provide direction for future clinical trials.

ImportanceProstate cancer (PCa) is marked by disparities in clinical outcomes by race, ethnicity, and age. Equitable enrollment in clinical trials is fundamental to promoting health equity.ObjectiveTo evaluate disparities in the inclusion of racial and ethnic minority groups and older adults across PCa clinical trials.Data SourcesMEDLINE, Embase, and ClinicalTrials.gov were searched to identify primary trial reports from each database's inception through February 2021. Global incidence in age subgroups and US population-based incidence in racial and ethnic subgroups were acquired from the Global Burden of Disease and Surveillance, Epidemiology, and End Results 21 incidence databases respectively.Study SelectionAll phase 2/3 randomized PCa clinical trials were eligible for age disparity analyses. Trials recruiting exclusively from the US were eligible for primary racial and ethnic disparity analyses.Data Extraction and SynthesisThis study was reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines. Data were pooled using a random-effects model.Main Outcomes and MeasuresEnrollment incidence ratios (EIRs), trial proportions (TPs) of participants 65 years or older or members of a racial and ethnic subgroup divided by global incidence in the corresponding age group, or US population–based incidence in the corresponding racial and ethnic subgroup, were calculated. Meta-regression was used to explore associations between trial characteristics and EIRs and trends in EIRs during the past 3 decades.ResultsOf 9552 participants among trials reporting race, 954 (10.8%) were African American/Black, 80 (1.5%) were Asian/Pacific Islander, and 8518 (78.5) were White. Of 65 US trials, 45 (69.2%) reported race and only 9 (13.8%) reported data on all 5 US racial categories. Of 286 global trials, 75 (26.2%) reported the enrollment proportion of older adults. Outcomes by race and age were reported in 2 (3.1%) and 41 (15.0%) trials, respectively. Black (EIR, 0.70; 95% CI, 0.59-0.83) and Hispanic (EIR, 0.70; 95% CI, 0.59-0.83) patients were significantly underrepresented in US trials. There was no disparity in older adult representation (TP, 21 143 [71.1%]; EIR, 1.00; 95% CI, 0.95-1.05). The representation of Black patients was lower in larger trials (meta-regression coefficient, −0.06; 95% CI, −0.10 to −0.02; P = .002).Conclusions and RelevanceThe results of this meta-analysis suggest that Black and Hispanic men are underrepresented in trials compared with their share of PCa incidence. The representation of Black patients has consistently remained low during the past 2 decades.

ImportanceCytokine storm due to COVID-19 can cause high morbidity and mortality and may be more common in patients with cancer treated with immunotherapy (IO) due to immune system activation.ObjectiveTo determine the association of baseline immunosuppression and/or IO-based therapies with COVID-19 severity and cytokine storm in patients with cancer.Design, Setting, and ParticipantsThis registry-based retrospective cohort study included 12 046 patients reported to the COVID-19 and Cancer Consortium (CCC19) registry from March 2020 to May 2022. The CCC19 registry is a centralized international multi-institutional registry of patients with COVID-19 with a current or past diagnosis of cancer. Records analyzed included patients with active or previous cancer who had a laboratory-confirmed infection with SARS-CoV-2 by polymerase chain reaction and/or serologic findings.ExposuresImmunosuppression due to therapy; systemic anticancer therapy (IO or non-IO).Main Outcomes and MeasuresThe primary outcome was a 5-level ordinal scale of COVID-19 severity: no complications; hospitalized without requiring oxygen; hospitalized and required oxygen; intensive care unit admission and/or mechanical ventilation; death. The secondary outcome was the occurrence of cytokine storm.ResultsThe median age of the entire cohort was 65 years (interquartile range [IQR], 54-74) years and 6359 patients were female (52.8%) and 6598 (54.8%) were non-Hispanic White. A total of 599 (5.0%) patients received IO, whereas 4327 (35.9%) received non-IO systemic anticancer therapies, and 7120 (59.1%) did not receive any antineoplastic regimen within 3 months prior to COVID-19 diagnosis. Although no difference in COVID-19 severity and cytokine storm was found in the IO group compared with the untreated group in the total cohort (adjusted odds ratio [aOR], 0.80; 95% CI, 0.56-1.13, and aOR, 0.89; 95% CI, 0.41-1.93, respectively), patients with baseline immunosuppression treated with IO (vs untreated) had worse COVID-19 severity and cytokine storm (aOR, 3.33; 95% CI, 1.38-8.01, and aOR, 4.41; 95% CI, 1.71-11.38, respectively). Patients with immunosuppression receiving non-IO therapies (vs untreated) also had worse COVID-19 severity (aOR, 1.79; 95% CI, 1.36-2.35) and cytokine storm (aOR, 2.32; 95% CI, 1.42-3.79).Conclusions and RelevanceThis cohort study found that in patients with cancer and COVID-19, administration of systemic anticancer therapies, especially IO, in the context of baseline immunosuppression was associated with severe clinical outcomes and the development of cytokine storm.Trial RegistrationClinicalTrials.gov Identifier: NCT04354701