TARO: tree-aggregated factor regression for microbiome data integration.

MOTIVATION: Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns. RESULTS: We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the taxonomic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances. AVAILABILITY AND IMPLEMENTATION: The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package.

Optimizing Inpatient Care for Lung Cancer Patients with Immune Checkpoint Inhibitor- Related Pneumonitis Using a Clinical Care Pathway Algorithm.

Purpose: Immune checkpoint inhibitor-related pneumonitis (ICI-P) is a condition associated with high mortality, necessitating prompt recognition and treatment initiation. This study aimed to assess the impact of implementing a clinical care pathway algorithm on reducing the time to treatment for ICI-P. Methods: Patients with lung cancer and suspected ICI-P were enrolled, and a multi-modal intervention promoting algorithm use was implemented in two phases. Pre- and post-intervention analyses were conducted to evaluate the primary outcome of time from ICI-P diagnosis to treatment initiation. Results: Of the 82 patients admitted with suspected ICI-P, 73.17% were confirmed to have ICI-P, predominantly associated with non-small cell lung cancer (91.67%) and stage IV disease (95%). Pembrolizumab was the most commonly used immune checkpoint inhibitor (55%). The mean times to treatment were 2.37 days in the pre-intervention phase and, 3.07 days (p=0.46), and 1.27 days (p=0.40) in the post-intervention phases 1 and 2, respectively. Utilization of the immunotoxicity order set significantly increased from 0% to 27.27% (p = 0.04) after phase 2. While there were no significant changes in ICU admissions or inpatient mortality, outpatient pulmonology follow-ups increased statistically significantly, demonstrating enhanced continuity of care. The overall mortality for patients with ICI-P was 22%, underscoring the urgency of optimizing management strategies. Notably, all patients discharged on high-dose corticosteroids received appropriate gastrointestinal prophylaxis and prophylaxis against Pneumocystis jirovecii pneumonia infections at the end of phase 2. Conclusion: Implementing a clinical care pathway algorithm for ICI-P management standardizes care practices and enhances patient outcomes, underscoring the importance of structured approaches.

Technical note: Radiological clinical equivalence for phantom materials in carbon ion therapy.

PURPOSE: As carbon ion radiotherapy increases in use, there are limited phantom materials for heterogeneous or anthropomorphic phantom measurements. This work characterized the radiological clinical equivalence of several phantom materials in a therapeutic carbon ion beam. METHODS: Eight materials were tested for radiological material-equivalence in a carbon ion beam. The materials were computed tomography (CT)-scanned to obtain Hounsfield unit (HU) values, then irradiated in a monoenergetic carbon ion beam to determine relative linear stopping power (RLSP). The corresponding HU and RLSP for each phantom material were compared to clinical carbon ion calibration curves. For absorbed dose comparison, ion chamber measurements were made in the center of a carbon ion spread-out Bragg peak (SOBP) in water and in the phantom material, evaluating whether the material perturbed the absorbed dose measurement beyond what was predicted by the HU-RLSP relationship. RESULTS: Polyethylene, solid water (Gammex and Sun Nuclear), Blue Water (Standard Imaging), and Techtron HPV had measured RLSP values that agreed within ±4.2% of RLSP values predicted by the clinical calibration curve. Measured RLSP for acrylic was 7.2% different from predicted. The agreement for balsa wood and cork varied between samples. Ion chamber measurements in the phantom materials were within 0.1% of ion chamber measurements in water for most materials (solid water, Blue Water, polyethylene, and acrylic), and within 1.9% for the rest of the materials (balsa wood, cork, and Techtron HPV). CONCLUSIONS: Several phantom materials (Blue Water, polyethylene, solid water [Gammex and Sun Nuclear], and Techtron HPV) are suitable for heterogeneous phantom measurements for carbon ion therapy. Low density materials should be carefully characterized due to inconsistencies between samples.

OSLD nanoDot characterization for carbon radiotherapy dosimetry.

Objective. This study characterized optically-stimulated luminescent dosimeter (OSLD) nanoDots for use in a therapeutic carbon beam using the Imaging and Radiation Oncology Core (IROC) framework for remote output verification.Approach. The absorbed dose correction factors for OSLD (fading, linearity, beam quality, angularity, and depletion), as defined by AAPM TG 191, were characterized for carbon beams. For the various correction factors, the effect of linear energy transfer (LET) was examined by characterizing in both a low and high LET setting.Main results. Fading was not statistically different between reference photons and carbon, nor between low and high LET beams; thus, the standard IROC-defined exponential function could be used to characterize fading. Dose linearity was characterized with a linear fit; while low and high LET carbon linearity was different, these differences were small and could be rolled into the uncertainty budget if using a single linearity correction. A linear fit between beam quality and dose-averaged LET was determined. The OSLD response at various angles of incidence was not statistically different, thus a correction factor need not be applied. There was a difference in depletion between low and high LET irradiations in a primary carbon beam, but this difference was small over the standard five readings. The largest uncertainty associated with the use of OSLDs in carbon was because of thekQcorrection factor, with an uncertainty of 6.0%. The overall uncertainty budget was 6.3% for standard irradiation conditions.Significance. OSLD nanoDot response was characterized in a therapeutic carbon beam. The uncertainty was larger than for traditional photon applications. These findings enable the use of OSLDs for carbon absorbed dose measurements, but with less accuracy than conventional OSLD audit programs.

Randomized Placebo-Controlled, Biomarker-Stratified Phase Ib Microbiome Modulation in Melanoma: Impact of Antibiotic Preconditioning on Microbiome and Immunity.

Gut-microbiota modulation shows promise in improving immune-checkpoint blockade (ICB) response; however, precision biomarker-driven, placebo-controlled trials are lacking. We performed a multicenter, randomized placebo-controlled, biomarker-stratified phase I trial in patients with ICB-naïve metastatic melanoma using SER-401, an orally delivered Firmicutes-enriched spore formulation. Fecal microbiota signatures were characterized at baseline; patients were stratified by high versus low Ruminococcaceae abundance prior to randomization to the SER-401 arm (oral vancomycin-preconditioning/SER-401 alone/nivolumab + SER-401), versus the placebo arm [placebo antibiotic/placebo microbiome modulation (PMM)/nivolumab + PMM (NCT03817125)]. Analysis of 14 accrued patients demonstrated that treatment with SER-401 + nivolumab was safe, with an objective response rate of 25% in the SER-401 arm and 67% in the placebo arm (though the study was under-powered related to poor accrual during the COVID-19 pandemic). Translational analyses demonstrated that vancomycin preconditioning was associated with the disruption of the gut microbiota and impaired immunity, with incomplete recovery at ICB administration (particularly in patients with high baseline Ruminococcaceae). These results have important implications for future microbiome modulation trials. SIGNIFICANCE: This first-of-its-kind, placebo-controlled, randomized biomarker-driven microbiome modulation trial demonstrated that vancomycin + SER-401 and anti-PD-1 are safe in melanoma patients. Although limited by poor accrual during the pandemic, important insights were gained via translational analyses, suggesting that antibiotic preconditioning and interventional drug dosing regimens should be carefully considered when designing such trials.

Artificial Intelligence-Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care.

PURPOSE: Increased automation has been identified as one approach to improving global cancer care. The Radiation Planning Assistant (RPA) is a web-based tool offering automated radiotherapy (RT) contouring and planning to low-resource clinics. In this study, the RPA workflow and clinical acceptability were assessed by physicians around the world. METHODS: The RPA output for 75 cases was reviewed by at least three physicians; 31 radiation oncologists at 16 institutions in six countries on five continents reviewed RPA contours and plans for clinical acceptability using a 5-point Likert scale. RESULTS: For cervical cancer, RPA plans using bony landmarks were scored as usable as-is in 81% (with minor edits 93%); using soft tissue contours, plans were scored as usable as-is in 79% (with minor edits 96%). For postmastectomy breast cancer, RPA plans were scored as usable as-is in 44% (with minor edits 91%). For whole-brain treatment, RPA plans were scored as usable as-is in 67% (with minor edits 99%). For head/neck cancer, the normal tissue autocontours were acceptable as-is in 89% (with minor edits 97%). The clinical target volumes (CTVs) were acceptable as-is in 40% (with minor edits 93%). The volumetric-modulated arc therapy (VMAT) plans were acceptable as-is in 87% (with minor edits 96%). For cervical cancer, the normal tissue autocontours were acceptable as-is in 92% (with minor edits 99%). The CTVs for cervical cancer were scored as acceptable as-is in 83% (with minor edits 92%). The VMAT plans for cervical cancer were acceptable as-is in 99% (with minor edits 100%). CONCLUSION: The RPA, a web-based tool designed to improve access to high-quality RT in low-resource settings, has high rates of clinical acceptability by practicing clinicians around the world. It has significant potential for successful implementation in low-resource clinics.

A radiotherapy community data-driven approach to determine which complexity metrics best predict the impact of atypical TPS beam modeling on clinical dose calculation accuracy.

PURPOSE: To quantify the impact of treatment planning system beam model parameters, based on the actual spread in radiotherapy community data, on clinical treatment plans and determine which complexity metrics best describe the impact beam modeling errors have on dose accuracy. METHODS: Ten beam modeling parameters for a Varian accelerator were modified in RayStation to match radiotherapy community data at the 2.5, 25, 50, 75, and 97.5 percentile levels. These modifications were evaluated on 25 patient cases, including prostate, non-small cell lung, H&N, brain, and mesothelioma, generating 1,000 plan perturbations. Differences in the mean planned dose to clinical target volumes (CTV) and organs at risk (OAR) were evaluated with respect to the planned dose using the reference (50th-percentile) parameter values. Correlation between CTV dose differences, and 18 different complexity metrics were evaluated using linear regression; R-squared values were used to determine the best metric. RESULTS: Perturbations to MLC offset and transmission parameters demonstrated the greatest changes in dose: up to 5.7% in CTVs and 16.7% for OARs. More complex clinical plans showed greater dose perturbation with atypical beam model parameters. The mean MLC Gap and Tongue & Groove index (TGi) complexity metrics best described the impact of TPS beam modeling variations on clinical dose delivery across all anatomical sites; similar, though not identical, trends between complexity and dose perturbation were observed among all sites. CONCLUSION: Extreme values for MLC offset and MLC transmission beam modeling parameters were found to most substantially impact the dose distribution of clinical plans and careful attention should be given to these beam modeling parameters. The mean MLC Gap and TGi complexity metrics were best suited to identifying clinical plans most sensitive to beam modeling errors; this could help provide focus for clinical QA in identifying unacceptable plans.

Treatment patterns and outcomes of high-grade immune checkpoint inhibitor-related pneumonitis in an oncology hospitalist service.

PURPOSE: Immune checkpoint inhibitors (ICI) have become standard of care for some types of lung cancer. Along with expanding usage comes the emergence of immune-related adverse events (irAEs), including ICI-related pneumonitis (ICI-P). Treatment guidelines for managing irAEs have been developed; however, how clinicians manage irAEs in the real-world setting is less well known. We aimed to describe the outcomes and care patterns of grade ≥ 3 ICI-P in an onco-hospitalist service. PATIENTS AND METHODS: We included patients with lung cancer treated with ICI who were admitted to an oncology hospitalist service with a suspicion of ICI-P. We described the hospitalization characteristics, treatment patterns, discharge practices, and clinical outcomes of patients with confirmed ICI-P. The primary outcome was time to start treatment for ICI-P. RESULTS: Among 49 patients admitted with a suspicion of ICI-P, 31 patients were confirmed to have ICI-P and subsequently received ICI-P directed treatment. Pulmonology was consulted in 97% of patients. Median time to start treatment for ICI-P was 1 day (IQR 0-3.5 days). All 31 patients received corticosteroids. Inpatient mortality was 32%. Majority of patients discharged with steroids were prescribed prophylaxis for gastritis and opportunistic infections. Thirty-eight percent of patients were seen by pulmonology and 86% were seen by the oncology team post-discharge. CONCLUSION: Our study confirms prior findings of high mortality among patients with high-grade ICI-P. Early diagnosis and treatment are key to improving clinical outcomes. Understanding the care patterns and adherence to treatment guidelines of clinicians caring for this patient population may help identify ways to further standardize management practices and improve patient outcomes.

Tsyn-Seq: a T-cell Synapse-Based Antigen Identification Platform.

Tools for genome-wide rapid identification of peptide-major histocompatibility complex targets of T-cell receptors (TCR) are not yet universally available. We present a new antigen screening method, the T-synapse (Tsyn) reporter system, which includes antigen-presenting cells (APC) with a Fas-inducible NF-κB reporter and T cells with a nuclear factor of activated T cells (NFAT) reporter. To functionally screen for target antigens from a cDNA library, productively interacting T cell-APC aggregates were detected by dual-reporter activity and enriched by flow sorting followed by antigen identification quantified by deep sequencing (Tsyn-seq). When applied to a previously characterized TCR specific for the E7 antigen derived from human papillomavirus type 16 (HPV16), Tsyn-seq successfully enriched the correct cognate antigen from a cDNA library derived from an HPV16-positive cervical cancer cell line. Tsyn-seq provides a method for rapidly identifying antigens recognized by TCRs of interest from a tumor cDNA library. See related Spotlight by Makani and Joglekar, p. 515.

Influence of oral microbiome on longitudinal patterns of oral mucositis severity in patients with squamous cell carcinoma of the head and neck.

BACKGROUND: This study investigated the influence of oral microbial features on the trajectory of oral mucositis (OM) in patients with squamous cell carcinoma of the head and neck. METHODS: OM severity was assessed and buccal swabs were collected at baseline, at the initiation of cancer treatment, weekly during cancer treatment, at the termination of cancer treatment, and after cancer treatment termination. The oral microbiome was characterized via the 16S ribosomal RNA V4 region with the Illumina platform. Latent class mixed-model analysis was used to group individuals with similar trajectories of OM severity. Locally estimated scatterplot smoothing was used to fit an average trend within each group and to assess the association between the longitudinal OM scores and longitudinal microbial abundances. RESULTS: Four latent groups (LGs) with differing patterns of OM severity were identified for 142 subjects. LG1 has an early onset of high OM scores. LGs 2 and 3 begin with relatively low OM scores until the eighth and 11th week, respectively. LG4 has generally flat OM scores. These LGs did not vary by treatment or clinical or demographic variables. Correlation analysis showed that the abundances of Bacteroidota, Proteobacteria, Bacteroidia, Gammaproteobacteria, Enterobacterales, Bacteroidales, Aerococcaceae, Prevotellaceae, Abiotrophia, and Prevotella_7 were positively correlated with OM severity across the four LGs. Negative correlation was observed with OM severity for a few microbial features: Abiotrophia and Aerococcaceae for LGs 2 and 3; Gammaproteobacteria and Proteobacteria for LGs 2, 3, and 4; and Enterobacterales for LGs 2 and 4. CONCLUSIONS: These findings suggest the potential to personalize treatment for OM. PLAIN LANGUAGE SUMMARY: Oral mucositis (OM) is a common and debilitating after effect for patients treated for squamous cell carcinoma of the head and neck. Trends in the abundance of specific microbial features may be associated with patterns of OM severity over time. Our findings suggest the potential to personalize treatment plans for OM via tailored microbiome interventions.

Quantifying the Effect of 4-Dimensional Computed Tomography-Based Deformable Dose Accumulation on Representing Radiation Damage for Patients with Locally Advanced Non-Small Cell Lung Cancer Treated with Standard-Fractionated Intensity-Modulated Radiation Therapy.

PURPOSE: The aim of this study was to investigate the dosimetric and clinical effects of 4-dimensional computed tomography (4DCT)-based longitudinal dose accumulation in patients with locally advanced non-small cell lung cancer treated with standard-fractionated intensity-modulated radiation therapy (IMRT). METHODS AND MATERIALS: Sixty-seven patients were retrospectively selected from a randomized clinical trial. Their original IMRT plan, planning and verification 4DCTs, and ∼4-month posttreatment follow-up CTs were imported into a commercial treatment planning system. Two deformable image registration algorithms were implemented for dose accumulation, and their accuracies were assessed. The planned and accumulated doses computed using average-intensity images or phase images were compared. At the organ level, mean lung dose and normal-tissue complication probability (NTCP) for grade ≥2 radiation pneumonitis were compared. At the region level, mean dose in lung subsections and the volumetric overlap between isodose intervals were compared. At the voxel level, the accuracy in estimating the delivered dose was compared by evaluating the fit of a dose versus radiographic image density change (IDC) model. The dose-IDC model fit was also compared for subcohorts based on the magnitude of NTCP difference (|ΔNTCP|) between planned and accumulated doses. RESULTS: Deformable image registration accuracy was quantified, and the uncertainty was considered for the voxel-level analysis. Compared with planned doses, accumulated doses on average resulted in <1-Gy lung dose increase and <2% NTCP increase (up to 8.2 Gy and 18.8% for a patient, respectively). Volumetric overlap of isodose intervals between the planned and accumulated dose distributions ranged from 0.01 to 0.93. Voxel-level dose-IDC models demonstrated a fit improvement from planned dose to accumulated dose (pseudo-R2 increased 0.0023) and a further improvement for patients with ≥2% |ΔNTCP| versus for patients with <2% |ΔNTCP|. CONCLUSIONS: With a relatively large cohort, robust image registrations, multilevel metric comparisons, and radiographic image-based evidence, we demonstrated that dose accumulation more accurately represents the delivered dose and can be especially beneficial for patients with greater longitudinal response.

Increased Prevalence of Clostridioides difficile Infection Among Pediatric Oncology Patients: Risk Factors for Infection and Complications.

BACKGROUND: Pediatric oncology patients, who are typically immunosuppressed, exposed to medications associated with increased Clostridioides difficile infection (CDI) risk and hospitalized, are expected to be at substantial risk for infection and complications. Although certain C. difficile ribotypes have been associated with more severe infection in adults, such an association has not been described in children. METHODS: To characterize CDI epidemiology, including risk factors and complications among pediatric oncology patients, we retrospectively reviewed charts of patients 1-18 years old treated at a designated cancer center during 2000-2017. We used fluorescence-based polymerase chain reaction to identify ribotypes causing disease at our institution. RESULTS: In 11,366 total patients, we identified 207 CDI cases during the study period. CDI prevalence in our pediatric oncology population was 18 cases per 1000 patients. CDI was highest among patients with acute myeloid leukemia, neuroblastoma, and desmoplastic small round cell tumor (105, 66 and 111 cases per 1000 patients, respectively; P < 0.01). Fever, leukocytosis, elevated creatinine and abdominal radiation and fluoroquinolone exposure concurrent with treatment of CDI were associated with complications. Patients with severe CDI experienced increased mortality. Ribotypes previously associated with severe infection were observed infrequently and were not associated with mortality. CONCLUSIONS: This is the largest study of CDI in pediatric oncology patients to date. The study identifies specific oncologic diagnoses with increased CDI risk and factors predictive of poor outcomes. As CDI treatment guidelines are developed for this population, these data will be useful for risk stratification of patients in need of early, aggressive treatment.

Baseline extracellular vesicle miRNA-30c and autophagic CTCs predict chemoradiotherapy resistance and outcomes in patients with lung cancer.

Concurrent chemoradiotherapy (cCRT) is the mainstay of treatment for patients diagnosed with locally advanced non-small cell lung cancer (NSCLC). One significant challenge in the effectiveness of this therapy is the potential development of resistance mechanisms, where autophagy up-regulation has been proposed as a key contributing factor. However, there is a lack of reliable biomarkers to predict outcomes on these patients. Interestingly, for addressing this gap, extracellular vesicles (EVs) and circulating tumor cells (CTCs) have emerged as potential sources of such biomarkers. In this study, we investigated EV-associated miRNAs and presence of autophagic CTCs in prospectively collected serial samples from 38 patients with stage III NSCLC undergoing cCRT. Our findings revealed that non-responders exhibited low levels of baseline EV miR-375, miR-200c, and miR-30c. In particular, EV miR-30c showed high predictive value with an area under the curve of 87.2%. Low EV miR-30c and the presence of autophagic-activated CTCs emerged as independent predictive biomarkers for shorter relapse-free survival and overall survival. Furthermore, in experimental models simulating the effects of chemo- and radiotherapy, the administration of miR-30c, either through direct transfection or encapsulation into human EVs, led to the inhibition of autophagy in these cells. This is the first report demonstrating that EV miR-30c inhibits tumor autophagy and its quantification, together with autophagic-activated CTCs, could be used as biomarkers for the stratification and monitoring of patients with NSCLC undergoing cCRT, and they may hold promising potential for guiding subsequent consolidation treatment with immunotherapy or other novel therapies based on autophagy inhibitors.

TARO: tree-aggregated factor regression for microbiome data integration.

Motivation: Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns. Results: We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the phylogenetic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances. Availability and implementation: The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package .

Deep learning-based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers.

BACKGROUND: In recent years, deep-learning models have been used to predict entire three-dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated. PURPOSE: To develop a deep-learning model to predict high-quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements. METHODS: A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U-Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5-point scale (5, acceptable as-is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions. RESULTS: The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel-wise dose difference was -0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics of D 1 % ${D}_{1{\mathrm{\% }}}$ and D 98 % ${D}_{98{\mathrm{\% }}}$ were -1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were -0.30 ± 1.66 Gy and -0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions. CONCLUSIONS: Deep-learning dose prediction can be used to predict high-quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization.

Remotely Delivered Cancer Genetic Testing in the Making Genetic Testing Accessible (MAGENTA) Trial: A Randomized Clinical Trial.

Importance: Requiring personalized genetic counseling may introduce barriers to cancer risk assessment, but it is unknown whether omitting counseling could increase distress. Objective: To assess whether omitting pretest and/or posttest genetic counseling would increase distress during remote testing. Design, Setting, and Participants: Making Genetic Testing Accessible (MAGENTA) was a 4-arm, randomized noninferiority trial testing the effects of individualized pretest and/or posttest genetic counseling on participant distress 3 and 12 months posttest. Participants were recruited via social and traditional media, and enrollment occurred between April 27, 2017, and September 29, 2020. Participants were women aged 30 years or older, English-speaking, US residents, and had access to the internet and a health care professional. Previous cancer genetic testing or counseling was exclusionary. In the family history cohort, participants had a personal or family history of breast or ovarian cancer. In the familial pathogenic variant (PV) cohort, participants reported 1 biological relative with a PV in an actionable cancer susceptibility gene. Data analysis was performed between December 13, 2020, and May 31, 2023. Intervention: Participants completed baseline questionnaires, watched an educational video, and were randomized to 1 of 4 arms: the control arm with pretest and/or posttest genetic counseling, or 1 of 3 study arms without pretest and posttest counseling. Genetic counseling was provided by phone appointments and testing was done using home-delivered saliva kits. Main Outcomes and Measures: The primary outcome was participant distress measured by the Impact of Event Scale 3 months after receiving the results. Secondary outcomes included completion of testing, anxiety, depression, and decisional regret. Results: A total of 3839 women (median age, 44 years [range 22-91 years]), most of whom were non-Hispanic White and college educated, were randomized, 3125 in the family history and 714 in the familial PV cohorts. In the primary analysis in the family history cohort, all experimental arms were noninferior for distress at 3 months. There were no statistically significant differences in anxiety, depression, or decisional regret at 3 months. The highest completion rates were seen in the 2 arms without pretest counseling. Conclusions and Relevance: In the MAGENTA clinical trial, omitting individualized pretest counseling for all participants and posttest counseling for those without PV during remote genetic testing was not inferior with regard to posttest distress, providing an alternative care model for genetic risk assessment. Trial Registration: ClinicalTrials.gov Identifier: NCT02993068.

Gut Microbiome in Patients With Early-Stage and Late-Stage Melanoma.

Importance: The gut microbiome modulates the immune system and responses to immunotherapy in patients with late-stage melanoma. It is unknown whether fecal microbiota profiles differ between healthy individuals and patients with melanoma or if microbiota profiles differ among patients with different stages of melanoma. Defining gut microbiota profiles in individuals without melanoma and those with early-stage and late-stage melanoma may reveal features associated with disease progression. Objective: To characterize and compare gut microbiota profiles between healthy volunteers and patients with melanoma and between patients with early-stage and late-stage melanoma. Design, Setting, and Participants: This single-site case-control study took place at an academic comprehensive cancer center. Fecal samples were collected from systemic treatment-naive patients with stage I to IV melanoma from June 1, 2015, to January 31, 2019, and from healthy volunteers from June 1, 2021, to January 31, 2022. Patients were followed up for disease recurrence until November 30, 2021. Main Outcomes and Measures: Fecal microbiota was profiled by 16S ribosomal RNA sequencing. Clinical and pathologic characteristics, treatment, and disease recurrence were extracted from electronic medical records. Fecal microbiome diversity, taxonomic profiles and inferred functional profiles were compared between groups. Results: A total of 228 participants were enrolled (126 men [55.3%]; median age, 59 [range, 21-90] years), including 49 volunteers without melanoma, 38 patients with early-stage melanoma (29 with stage I or melanoma in situ and 9 with stage II), and 141 with late-stage melanoma (66 with stage III and 75 with stage IV). Community differences were observed between patients with melanoma and volunteers. Patients with melanoma had a higher relative abundance of Fusobacterium compared with controls on univariate analysis (0.19% vs 0.003%; P < .001), but this association was attenuated when adjusted for covariates (log2 fold change of 5.18 vs controls; P = .09). Microbiomes were distinct between patients with early-stage and late-stage melanoma. Early-stage melanoma had a higher alpha diversity (Inverse Simpson Index 14.6 [IQR, 9.8-23.0] vs 10.8 [IQR, 7.2-16.8]; P = .003), and a higher abundance of the genus Roseburia on univariate analysis (2.4% vs 1.2%; P < .001) though statistical significance was lost with covariate adjustment (log2 fold change of 0.86 vs controls; P = .13). Multiple functional pathways were differentially enriched between groups. No associations were observed between the microbial taxa and disease recurrence in patients with stage III melanoma treated with adjuvant immunotherapy. Conclusions and Relevance: The findings of this case-control study suggest that fecal microbiota profiles were significantly different among patients with melanoma and controls and between patients with early-stage and late-stage melanoma. Prospective investigations of the gut microbiome and changes that occur with disease progression may identify future microbial targets for intervention.

SETD2 Loss and ATR Inhibition Synergize to Promote cGAS Signaling and Immunotherapy Response in Renal Cell Carcinoma.

PURPOSE: Immune checkpoint blockade (ICB) demonstrates durable clinical benefits in a minority of patients with renal cell carcinoma (RCC). We aimed to identify the molecular features that determine the response and develop approaches to enhance it. EXPERIMENTAL DESIGN: We investigated the effects of SET domain-containing protein 2 (SETD2) loss on the DNA damage response pathway, the cytosolic DNA-sensing pathway, the tumor immune microenvironment, and the response to ataxia telangiectasia and rad3-related (ATR) and checkpoint inhibition in RCC. RESULTS: ATR inhibition activated the cyclic GMP-AMP synthase (cGAS)-interferon regulatory factor 3 (IRF3)-dependent cytosolic DNA-sensing pathway, resulting in the concurrent expression of inflammatory cytokines and immune checkpoints. Among the common RCC genotypes, SETD2 loss is associated with preferential ATR activation and sensitizes cells to ATR inhibition. SETD2 knockdown promoted the cytosolic DNA-sensing pathway in response to ATR inhibition. Treatment with the ATR inhibitor VE822 concurrently upregulated immune cell infiltration and immune checkpoint expression in Setd2 knockdown Renca tumors, providing a rationale for ATR inhibition plus ICB combination therapy. Setd2-deficient Renca tumors demonstrated greater vulnerability to ICB monotherapy or combination therapy with VE822 than Setd2-proficient tumors. Moreover, SETD2 mutations were associated with a higher response rate and prolonged overall survival in patients with ICB-treated RCC but not in patients with non-ICB-treated RCC. CONCLUSIONS: SETD2 loss and ATR inhibition synergize to promote cGAS signaling and enhance immune cell infiltration, providing a mechanistic rationale for the combination of ATR and checkpoint inhibition in patients with RCC with SETD2 mutations.

Sustained remissions in CLL after frontline FCR treatment with very-long-term follow-up.

Chemoimmunotherapy with fludarabine, cyclophosphamide, and rituximab (FCR) achieves durable remissions, with flattening of the progression-free survival (PFS) curve in patients with mutated immunoglobulin heavy chain variable gene (IGHV-M). We updated long-term follow-up results from the original 300-patient FCR study initiated at MD Anderson in 1999. The current median follow-up is 19.0 years. With this extended follow-up, the median PFS for patients with IGHV-M was 14.6 years vs 4.2 years for patients with unmutated IGHV (IGHV-UM). Disease progression beyond 10 years was uncommon. In total, 16 of 94 (17%) patients in remission at 10 years subsequently progressed with the additional follow-up compared with the patients in our prior report in 2015. Only 4 of 45 patients (9%) with IGHV-M progressed beyond 10 years. Excluding Richter transformation, 96 of 300 patients (32%) developed 106 other malignancies, with 19 of 300 (6.3%) developing therapy-related myeloid neoplasms (tMNs), which were fatal in 16 of 19 (84%). No pretreatment patient characteristics predicted the risk of tMNs. In summary, FCR remains an option for patients with IGHV-M chronic lymphocytic leukemia (CLL), with a significant fraction achieving functional cure of CLL. A risk-benefit assessment is warranted when counseling patients, balancing potential functional cure with the risk of late relapses and serious secondary malignancies.

Liver Cancer Vascularity Driven by Extracellular Matrix Stiffness: Implications for Imaging Research.

BACKGROUND: Extracellular matrix stiffness represents a barrier to effective local and systemic drug delivery. Increasing stiffness disrupts newly formed vessel architecture and integrity, leading to tumor-like vasculature. The resulting vascular phenotypes are manifested through different cross-sectional imaging features. Contrast-enhanced studies can help elucidate the interplay between liver tumor stiffness and different vascular phenotypes. PURPOSE: This study aims to correlate extracellular matrix stiffness, dynamic contrast-enhanced computed tomography, and dynamic contrast-enhancement ultrasound imaging features of 2 rat hepatocellular carcinoma tumor models. METHODS AND MATERIALS: Buffalo-McA-RH7777 and Sprague Dawley (SD)-N1S1 tumor models were used to evaluate tumor stiffness by 2-dimensional shear wave elastography, along with tumor perfusion by dynamic contrast-enhanced ultrasonography and contrast-enhanced computed tomography. Atomic force microscopy was used to calculate tumor stiffness at a submicron scale. Computer-aided image analyses were performed to evaluate tumor necrosis, as well as the percentage, distribution, and thickness of CD34+ blood vessels. RESULTS: Distinct tissue signatures between models were observed according to the distribution of the stiffness values by 2-dimensional shear wave elastography and atomic force microscopy ( P < 0.05). Higher stiffness values were attributed to SD-N1S1 tumors, also associated with a scant microvascular network ( P ≤ 0.001). Opposite results were observed in the Buffalo-McA-RH7777 model, exhibiting lower stiffness values and richer tumor vasculature with predominantly peripheral distribution ( P = 0.03). Consistent with these findings, tumor enhancement was significantly greater in the Buffalo-McA-RH7777 tumor model than in the SD-N1S1 on both dynamic contrast-enhanced ultrasonography and contrast-enhanced computed tomography ( P < 0.005). A statistically significant positive correlation was observed between tumor perfusion on dynamic contrast-enhanced ultrasonography and contrast-enhanced computed tomography in terms of the total area under the curve and % microvessel tumor coverage ( P < 0.05). CONCLUSIONS: The stiffness signatures translated into different tumor vascular phenotypes. Two-dimensional shear wave elastography and dynamic contrast-enhanced ultrasonography adequately depicted different stromal patterns, which resulted in unique imaging perfusion parameters with significantly greater contrast enhancement observed in softer tumors.