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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., (©2023 American Association for Cancer Research.)

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.

Manipulation of the gut microbiome using live biotherapeutic products shows promise for clinical applications but remains challenging to achieve. Here, we induced dysbiosis in 56 healthy volunteers using antibiotics to test a synbiotic comprising the infant gut microbe, Bifidobacterium longum subspecies infantis (B. infantis), and human milk oligosaccharides (HMOs). B. infantis engrafted in 76% of subjects in an HMO-dependent manner, reaching a relative abundance of up to 81%. Changes in microbiome composition and gut metabolites reflect altered recovery of engrafted subjects compared with controls. Engraftment associates with increases in lactate-consuming Veillonella, faster acetate recovery, and changes in indolelactate and p-cresol sulfate, metabolites that impact host inflammatory status. Furthermore, Veillonella co-cultured in vitro and in vivo with B. infantis and HMO converts lactate produced by B. infantis to propionate, an important mediator of host physiology. These results suggest that the synbiotic reproducibly and predictably modulates recovery of a dysbiotic microbiome., Competing Interests: Declaration of interests Prolacta Bioscience employees are also shareholders in the company. A.M.A., A.Y.K., and R.R.J. are members of Prolacta Bioscience’s clinical advisory board. C.B.P. served as a consultant to Prolacta Bioscience. U.S. Patent no. 8,927,027, International Application pub. no. WO 2021/061991 and WO 2022/155201, and their corresponding family member patents and applications are related to this work., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The successful development and implementation of precision immuno-oncology therapies requires a deeper understanding of the immune architecture at a patient level. T-cell receptor (TCR) repertoire sequencing is a relatively new technology that enables monitoring of T-cells, a subset of immune cells that play a central role in modulating immune response. These immunologic relationships are complex and are governed by various distributional aspects of an individual patient's tumor profile. We propose Bayesian QUANTIle regression for hierarchical COvariates (QUANTICO) that allows simultaneous modeling of hierarchical relationships between multilevel covariates, conducts explicit variable selection, estimates quantile and patient-specific coefficient effects, to induce individualized inference. We show QUANTICO outperforms existing approaches in multiple simulation scenarios. We demonstrate the utility of QUANTICO to investigate the effect of TCR variables on immune response in a cohort of lung cancer patients. At population level, our analyses reveal the mechanistic role of T-cell proportion on the immune cell abundance, with tumor mutation burden as an important factor modulating this relationship. At a patient level, we find several outlier patients based on their quantile-specific coefficient functions, who have higher mutational rates and different smoking history., (© 2022 The Authors. Biometrics published by Wiley Periodicals LLC on behalf of International Biometric Society.)

Patients receiving ibrutinib for CLL rarely achieve undetectable measurable residual disease (U-MRD), necessitating indefinite therapy, with cumulative risks of treatment discontinuation due to progression or adverse events. This study added venetoclax to ibrutinib for up to 2 years, in patients who had received ibrutinib for ≥12 months (mo) and had ≥1 high risk feature (TP53 mutation and/or deletion, ATM deletion, complex karyotype or persistently elevated β 2 -microglobulin). The primary endpoint was U-MRD with 10 -4 sensitivity (U-MRD4) in bone marrow (BM) at 12mo. Forty-five patients were treated. On intention-to-treat analysis, 23/42 (55%) patients improved their response to CR (2 pts were in MRD + CR at venetoclax initiation). U-MRD4 at 12mo was 57%. 32/45 (71%) had U-MRD at the completion of venetoclax: 22/32 stopped ibrutinib; 10 continued ibrutinib. At a median of 41 months from venetoclax initiation, 5/45 patients have progressed; none have died from CLL or Richter Transformation. In 32 patients with BM U-MRD4, peripheral blood (PB) MRD4 was analyzed every 6 months; 10/32 have had PB MRD re-emergence at a median of 13 months post-venetoclax. In summary, the addition of venetoclax in patients treated with ≥12mo of ibrutinib achieved high rate of BM U-MRD4 and may achieve durable treatment-free remission., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: Risk-reducing salpingectomy with delayed oophorectomy has gained interest for individuals at high risk for tubo-ovarian cancer as there is compelling evidence that especially high-grade serous carcinoma originates in the fallopian tubes. Two studies have demonstrated a positive effect of salpingectomy on menopause-related quality of life and sexual health compared with standard risk-reducing salpingo-oophorectomy., Primary Objective: To investigate whether salpingectomy with delayed oophorectomy is non-inferior to the current standard salpingo-oophorectomy for the prevention of tubo-ovarian cancer among individuals at high inherited risk., Study Hypothesis: We hypothesize that postponement of oophorectomy after salpingectomy, to the age of 40-45 ( BRCA1 ) or 45-50 ( BRCA2 ) years, compared with the current standard salpingo-oophorectomy at age 35-40 ( BRCA1 ) or 40-45 ( BRCA2 ) years, is non-inferior in regard to tubo-ovarian cancer risk., Trial Design: In this international prospective preference trial, participants will choose between the novel salpingectomy with delayed oophorectomy and the current standard salpingo-oophorectomy. Salpingectomy can be performed after the completion of childbearing and between the age of 25 and 40 ( BRCA1 ), 25 and 45 ( BRCA2 ), or 25 and 50 ( BRIP1, RAD51C, and RAD51D pathogenic variant carriers) years. Subsequent oophorectomy is recommended at a maximum delay of 5 years beyond the upper limit of the current guideline age for salpingo-oophorectomy. The current National Comprehensive Cancer Network (NCCN) guideline age, which is also the recommended age for salpingo-oophorectomy within the study, is 35-40 years for BRCA1 , 40-45 years for BRCA2, and 45-50 years for BRIP1 , RAD51C , and RAD51D pathogenic variant carriers., Major Inclusion/exclusion Criteria: Premenopausal individuals with a documented class IV or V germline pathogenic variant in the BRCA1, BRCA2, BRIP1, RAD51C, or RAD51D gene who have completed childbearing are eligible for participation. Participants may have a personal history of a non-ovarian malignancy., Primary Endpoint: The primary outcome is the cumulative tubo-ovarian cancer incidence at the target age: 46 years for BRCA1 and 51 years for BRCA2 pathogenic variant carriers., Sample Size: The sample size to ensure sufficient power to test non-inferiority of salpingectomy with delayed oophorectomy compared with salpingo-oophorectomy requires 1500 BRCA1 and 1500 BRCA2 pathogenic variant carriers., Estimated Dates for Completing Accrual and Presenting Results: Participant recruitment is expected to be completed at the end of 2026 (total recruitment period of 5 years). The primary outcome is expected to be available in 2036 (minimal follow-up period of 10 years)., Trial Registration Number: NCT04294927., Competing Interests: Competing interests: None declared., (© IGCS and ESGO 2023. Re-use permitted under CC BY. Published by BMJ.)

Purpose: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans., Methods and Materials: A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists., Results: The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D 1% ,D 95% , and D 99% across the targets were within -2.53% ± 1.34%, -0.42% ± 1.27%, and -0.12% ± 1.97%, respectively, and the OAR mean and maximum doses were within -0.33 ± 1.40 Gy and -0.96 ± 2.08 Gy, respectively. For the plan quality assessment study, radiation oncologists flagged 47 OARs for possible plan improvement. There was high interphysician variability; 83% of physician-flagged OARs were flagged by only one of 3 physicians. The comparative dose prediction model flagged 63 OARs, including 30 of 47 physician-flagged OARs., Conclusions: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: Imaging of tumors is a standard step in diagnosing cancer and making subsequent treatment decisions. The field of radiomics aims to develop imaging based biomarkers using methods rooted in artificial intelligence applied to medical imaging. However, a challenging aspect of developing predictive models for clinical use is that many quantitative features derived from image data exhibit instability or lack of reproducibility across different imaging systems or image-processing pipelines. Methods: To address this challenge, we propose a Bayesian sparse modeling approach for image classification based on radiomic features, where the inclusion of more reliable features is favored via a probit prior formulation. Results: We verify through simulation studies that this approach can improve feature selection and prediction given correct prior information. Finally, we illustrate the method with an application to the classification of head and neck cancer patients by human papillomavirus status, using as our prior information a reliability metric quantifying feature stability across different imaging systems., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Shoemaker, Ger, Court, Aerts, Vannucci and Peterson.)

There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with similar microbiome profiles. We propose a novel unsupervised clustering approach in the Bayesian framework that innovates over existing model-based clustering approaches, such as the Dirichlet multinomial mixture model, in three key respects: we incorporate feature selection, learn the appropriate number of clusters from the data, and integrate information on the tree structure relating the observed features. We compare the performance of our proposed method to existing methods on simulated data designed to mimic real microbiome data. We then illustrate results obtained for our motivating data set, a clinical study aimed at characterizing the tumor microbiome of pancreatic cancer patients.

Our understanding of the molecular mechanisms underlying postsurgical recurrence of non-small cell lung cancer (NSCLC) is rudimentary. Molecular and T cell repertoire intratumor heterogeneity (ITH) have been reported to be associated with postsurgical relapse; however, how ITH at the cellular level impacts survival is largely unknown. Here we report the analysis of 2880 multispectral images representing 14.2% to 27% of tumor areas from 33 patients with stage I NSCLC, including 17 cases (relapsed within 3 years after surgery) and 16 controls (without recurrence ≥5 years after surgery) using multiplex immunofluorescence. Spatial analysis was conducted to quantify the minimum distance between different cell types and immune cell infiltration around malignant cells. Immune ITH was defined as the variance of immune cells from 3 intratumor regions. We found that tumors from patients having relapsed display different immune biology compared with nonrecurrent tumors, with a higher percentage of tumor cells and macrophages expressing PD-L1 (P =.031 and P =.024, respectively), along with an increase in regulatory T cells (Treg) (P =.018), antigen-experienced T cells (P =.025), and effector-memory T cells (P =.041). Spatial analysis revealed that a higher level of infiltration of PD-L1 + macrophages (CD68 + PD-L1 + ) or antigen-experienced cytotoxic T cells (CD3 + CD8 + PD-1 + ) in the tumor was associated with poor overall survival (P =.021 and P =.006, respectively). A higher degree of Treg ITH was associated with inferior recurrence-free survival regardless of tumor mutational burden (P =.022), neoantigen burden (P =.021), genomic ITH (P =.012) and T cell repertoire ITH (P =.001). Using multiregion multiplex immunofluorescence, we characterized ITH at the immune cell level along with whole exome and T cell repertoire sequencing from the same tumor regions. This approach highlights the role of immunoregulatory and coinhibitory signals as well as their spatial distribution and ITH that define the hallmarks of tumor relapse of stage I NSCLC., (Copyright © 2022 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights reserved.)

Deep learning methods for image segmentation and contouring are gaining prominence as an automated approach for delineating anatomical structures in medical images during radiation treatment planning. These contours are used to guide radiotherapy treatment planning, so it is important that contouring errors are flagged before they are used for planning. This creates a need for effective quality assurance methods to enable the clinical use of automated contours in radiotherapy. We propose a novel method for contour quality assurance that requires only shape features, making it independent of the platform used to obtain the images. Our method uses a random forest classifier to identify low-quality contours. On a dataset of 312 kidney contours, our method achieved a cross-validated area under the curve of 0.937 in identifying unacceptable contours. We applied our method to an unlabeled validation dataset of 36 kidney contours. We flagged 6 contours which were then reviewed by a cervix contour specialist, who found that 4 of the 6 contours contained errors. We used Shapley values to characterize the specific shape features that contributed to each contour being flagged, providing a starting point for characterizing the source of the contouring error. These promising results suggest our method is feasible for quality assurance of automated radiotherapy contours.

Not all patients with cancer and severe neutropenia develop fever, and the fecal microbiome may play a role. In a single-center study of patients undergoing hematopoietic cell transplant ( n  = 119), the fecal microbiome was characterized at onset of severe neutropenia. A total of 63 patients (53%) developed a subsequent fever, and their fecal microbiome displayed increased relative abundances of Akkermansia muciniphila , a species of mucin-degrading bacteria ( P  = 0.006, corrected for multiple comparisons). Two therapies that induce neutropenia, irradiation and melphalan, similarly expanded A. muciniphila and additionally thinned the colonic mucus layer in mice. Caloric restriction of unirradiated mice also expanded A. muciniphila and thinned the colonic mucus layer. Antibiotic treatment to eradicate A. muciniphila before caloric restriction preserved colonic mucus, whereas A. muciniphila reintroduction restored mucus thinning. Caloric restriction of unirradiated mice raised colonic luminal pH and reduced acetate, propionate, and butyrate. Culturing A. muciniphila in vitro with propionate reduced utilization of mucin as well as of fucose. Treating irradiated mice with an antibiotic targeting A. muciniphila or propionate preserved the mucus layer, suppressed translocation of flagellin, reduced inflammatory cytokines in the colon, and improved thermoregulation. These results suggest that diet, metabolites, and colonic mucus link the microbiome to neutropenic fever and may guide future microbiome-based preventive strategies.

Purpose: Discrepancies between planned and delivered dose to GI structures during radiation therapy (RT) of liver cancer may hamper the prediction of treatment outcomes. The purpose of this study is to develop a streamlined workflow for dose accumulation in a treatment planning system (TPS) during liver image-guided RT and to assess its accuracy when using different deformable image registration (DIR) algorithms., Materials and Methods: Fifty-six patients with primary and metastatic liver cancer treated with external beam radiotherapy guided by daily CT-on-rails (CTOR) were retrospectively analyzed. The liver, stomach and duodenum contours were auto-segmented on all planning CTs and daily CTORs using deep-learning methods. Dose accumulation was performed for each patient using scripting functionalities of the TPS and considering three available DIR algorithms based on: (i) image intensities only; (ii) intensities + contours; (iii) a biomechanical model (contours only). Planned and accumulated doses were converted to equivalent dose in 2Gy (EQD2) and normal tissue complication probabilities (NTCP) were calculated for the stomach and duodenum. Dosimetric indexes for the normal liver, GTV, stomach and duodenum and the NTCP values were exported from the TPS for analysis of the discrepancies between planned and the different accumulated doses., Results: Deep learning segmentation of the stomach and duodenum enabled considerable acceleration of the dose accumulation process for the 56 patients. Differences between accumulated and planned doses were analyzed considering the 3 DIR methods. For the normal liver, stomach and duodenum, the distribution of the 56 differences in maximum doses (D2%) presented a significantly higher variance when a contour-driven DIR method was used instead of the intensity only-based method. Comparing the two contour-driven DIR methods, differences in accumulated minimum doses (D98%) in the GTV were >2Gy for 15 (27%) of the patients. Considering accumulated dose instead of planned dose in standard NTCP models of the duodenum demonstrated a high sensitivity of the duodenum toxicity risk to these dose discrepancies, whereas smaller variations were observed for the stomach., Conclusion: This study demonstrated a successful implementation of an automatic workflow for dose accumulation during liver cancer RT in a commercial TPS. The use of contour-driven DIR methods led to larger discrepancies between planned and accumulated doses in comparison to using an intensity only based DIR method, suggesting a better capability of these approaches in estimating complex deformations of the GI organs., Competing Interests: Dr. PB reports support for sponsored research from RaySearch Laboratories and Varian. Dr. KB reports Licensing agreement with RaySearch Laboratories for Deformable Image Registration technologies, participation on RaySearch Clinical Advisory Board. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 McCulloch, Cazoulat, Svensson, Gryshkevych, Rigaud, Anderson, Kirimli, De, Mathew, Zaid, Elganainy, Peterson, Balter, Koay and Brock.)

Importance: Previously published work reported independent benefit of maintenance of oral intake (eat) and swallowing exercise adherence (exercise) during radiotherapy (RT) on diet and functional outcomes. The current study seeks to validate the authors' previously published findings in a large contemporary cohort of patients with oropharynx cancer (OPC) and address limitations of the prior retrospective study using prospective, validated outcome measures., Objective: To examine the longitudinal association of oral intake and swallowing exercise using validated, clinician-graded and patient-reported outcomes., Design, Setting, and Participants: Secondary analysis of a prospective OPC registry including patients who underwent primary RT/chemoradiotherapy (CRT) or primary transoral robotic surgery plus RT/CRT for OPC at a single-institution comprehensive cancer center., Exposures: Adherence to speech pathology swallowing intervention during RT coded as (1) eat: oral intake at end of RT (nothing by mouth [NPO]; partial oral intake [PO], with feeding tube [FT] supplement; full PO); and (2) exercise: swallowing exercise adherence (nonadherent vs partial/full adherence)., Main Outcomes and Measures: Feeding tube and diet (Performance Status Scale for Head and Neck Cancer) patient-reported swallowing-related quality of life (MD Anderson Dysphagia Inventory; MDADI) and clinician-graded dysphagia severity grade (videofluoroscopic Dynamic Imaging Grade of Swallowing Toxicity; DIGEST) were collected at baseline, 3 to 6 months, and 18 to 24 months post-RT., Results: A total of 595 patients (mean [SD] age, 65 [10] years; 532 [89%] male) who underwent primary RT (111 of 595 [19%]), CRT (434 of 595 [73%]), or primary transoral robotic surgery plus RT/CRT (50 of 595 [8%]) were included in this cohort study. At the end of RT, 55 (9%) patients were NPO, 115 (19%) were partial PO, 425 (71%) were full PO, and 340 (57%) reported exercise adherence. After multivariate adjustment, subacute return to solid diet and FT were independently associated with oral intake (odds ratio [OR], 2.0; 95% CI, 1.0-4.1; OR, 0.1; 95% CI, 0.0-0.2, respectively) and exercise (OR, 2.9; 95% CI, 1.9-4.5; OR, 0.3; 95% CI, 0.1-0.5, respectively). Subacute MDADI (β = 6.5; 95% CI, 1.8-11.2), FT duration (days; β = -123.4; 95% CI, -148.5 to -98.4), and less severe dysphagia per DIGEST (OR, 0.6; 95% CI, 0.3-1.0) were independently associated with oral intake, while exercise was independently associated with less severe laryngeal penetration/aspiration per DIGEST-safety (OR, 0.7; 95% CI, 0.4-1.0). DIGEST grade associations with oral intake were not preserved long-term; however, exercise was associated with a higher likelihood of solid diet intake and better swallow safety per DIGEST., Conclusions and Relevance: The findings of this cohort study extend the authors' previously published findings that oral intake and swallowing exercise during RT are associated with favorable functional outcomes, now demonstrated with broader domains of function using validated measures. Patterns of benefit differed in this study. Specifically, better subacute recovery of swallow-related quality of life and less severe dysphagia were found among patients who maintained oral intake independent of exercise adherence, and shorter FT utilization and better long-term diet and swallowing safety were found among those who exercised independent of oral intake.

Richter's Syndrome (RS) is an aggressive transformation of CLL, usually clonally-related diffuse large B-cell lymphoma (DLBCL), characterized by frequent TP53 mutations, intrinsic chemoresistance and poor survival. TP53-independent treatments are needed. We conducted a single center, phase 2, investigator-initiated study of high dose blinatumomab (maximum 112 mcg/d after initial, weekly dose escalation), NCT03121534, given for an 8-week induction and 4-week consolidation cycle. Responses were assessed by Lugano 2014 criteria. Serial multi-parameter flow cytometry from blood was performed to identify patient-specific biomarkers for response. Nine patients were treated. Patients had received a median of 4 and 2 prior therapies for CLL and RS, respectively. Five of 9 had del(17p) and 100% had complex karyotype. Four patients had reduction in nodal disease, including one durable complete response lasting >1 y. Treatment was well tolerated, with no grade >3 cytokine release syndrome and 1 case of grade 3, reversible neurotoxicity. Immunophenotyping demonstrated the majority of patients expressed multiple immune checkpoints, especially PD1, TIM3 and TIGIT. The patient who achieved CR had the lowest levels of immune checkpoint expression. Simultaneous targeting with immune checkpoint blockade, especially PD1 inhibition, which has already demonstrated single-agent efficacy in RS, could achieve synergistic killing and enhance outcomes., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Patients with rare solid tumors treated on early phase trials experience toxicities from their tumors and treatments. However, limited data exist to describe the detailed symptom burden suffered by these patients, particularly those with rare solid tumors treated with immunotherapy. We performed a prospective longitudinal study to capture patient-reported symptom burden. Patients completed the validated MD Anderson Symptom Inventory (MDASI)-Immunotherapy with 20 symptoms including 7 immunotherapy-specific items and 6 interference items at baseline and weekly thereafter for up to 9 weeks. Symptoms and interference were rated on 0-10 scales (0 = none or no interference, 10 = worst imaginable or complete interference). Group-based trajectory modelling determined higher and lower symptom groups. A total of 336 MDASI questionnaires were completed by 53 patients (mean age 55.4y, 53% male) with advanced rare cancers receiving pembrolizumab in a Phase II clinical trial. Symptoms reported as most severe over the course of the treatment over 9 weeks were fatigue [mean (M) = 3.8, SD = 2.3], pain (M = 3.7, SD = 2.9), disturbed sleep (M = 2.7, SD = 2.3), drowsiness (M = 2.6, SD = 2.0) and lack of appetite (M = 2.5, SD = 2.1). Pain in the abdomen (M = 2.2, SD = 2.4), rash (M = 1.1, SD = 1.8) and diarrhea (M = 0.9, SD = 1.5) were less severe. Interference with walking was rated the highest (M = 3.4, SD = 2.8) and relations with others was rated the lowest (M = 2.1, SD = 2.6). Using a composite score based on the five most severe symptoms (fatigue, pain, lack of appetite, feeling drowsy and sleep disturbance), 43% were classified into the high symptom burden group. Using a score based on immunotherapy-specific symptoms (e.g., rash, diarrhea) 33% of patients were included in the high symptom group. Symptom burden stayed relatively stable in the high- and low-symptom burden patient groups from baseline through 9 weeks. Some patients with rare malignancies experienced high symptom burden even at baseline. In patients with rare cancers, symptom trajectories stayed relatively stable over nine weeks of treatment with pembrolizumab.Trial registration: ClinicalTrials.gov identifier: NCT02721732., (© 2022. The Author(s).)

Background: Immune-checkpoint inhibitors (ICIs) changed the therapeutic landscape of patients with lung cancer. However, only a subset of them derived clinical benefit and evidenced the need to identify reliable predictive biomarkers. Liquid biopsy is the non-invasive and repeatable analysis of biological material in body fluids and a promising tool for cancer biomarkers discovery. In particular, there is growing evidence that extracellular vesicles (EVs) play an important role in tumor progression and in tumor-immune interactions. Thus, we evaluated whether extracellular vesicle PD-L1 expression could be used as a biomarker for prediction of durable treatment response and survival in patients with non-small cell lung cancer (NSCLC) undergoing treatment with ICIs., Methods: Dynamic changes in EV PD-L1 were analyzed in plasma samples collected before and at 9 ± 1 weeks during treatment in a retrospective and a prospective independent cohorts of 33 and 39 patients, respectively., Results: As a result, an increase in EV PD-L1 was observed in non-responders in comparison to responders and was an independent biomarker for shorter progression-free survival and overall survival. To the contrary, tissue PD-L1 expression, the commonly used biomarker, was not predictive neither for durable response nor survival., Conclusion: These findings indicate that EV PD-L1 dynamics could be used to stratify patients with advanced NSCLC who would experience durable benefit from ICIs., (© 2022. The Author(s).)

Background: In microbiome data analysis, unsupervised clustering is often used to identify naturally occurring clusters, which can then be assessed for associations with characteristics of interest. In this work, we systematically compared beta diversity and clustering methods commonly used in microbiome analyses. We applied these to four published datasets where highly distinct microbiome profiles could be seen between sample groups, as well a clinical dataset with less clear separation between groups., Results: Although no single method outperformed the others consistently, we did identify the key scenarios where certain methods can underperform. Specifically, the Bray Curtis (BC) metric resulted in poor clustering in a dataset where high-abundance OTUs were relatively rare. In contrast, the unweighted UniFrac (UU) metric clustered poorly on dataset with a high prevalence of low-abundance OTUs. To explore these hypotheses about BC and UU, we systematically modified the properties of the poorly performing datasets and found that this approach resulted in improved BC and UU performance. Based on these observations, we rationally combined BC and UU to generate a novel metric. We tested its performance while varying the relative contributions of each metric and also compared it with another combined metric, the generalized UniFrac distance. The proposed metric showed high performance across all datasets., Conclusions: Our systematic evaluation of clustering performance in these five datasets demonstrates that there is no existing clustering method that universally performs best across all datasets. We propose a combined metric of BC and UU that capitalizes on the complementary strengths of the two metrics. Video abstract., (© 2022. The Author(s).)

Modulation of commensal gut microbiota is increasingly recognized as a promising strategy to reduce mortality in patients with malignant diseases, but monitoring for dysbiosis is generally not routine clinical practice due to equipment, expertise and funding required for sequencing analysis. A low-threshold alternative is microbial diversity profiling by single-cell flow cytometry (FCM), which we compared to 16S rRNA sequencing in human fecal samples and employed to characterize longitudinal changes in the microbiome composition of patients with aggressive B-cell non-Hodgkin lymphoma undergoing chemoimmunotherapy. Diversity measures obtained from both methods were correlated and captured identical trends in microbial community structures, finding no difference in patients' pretreatment alpha or beta diversity compared to healthy controls and a significant and progressive loss of alpha diversity during chemoimmunotherapy. Our results highlight the potential of FCM-based microbiome profiling as a reliable and accessible diagnostic tool that can provide novel insights into cancer therapy-associated dysbiosis dynamics.

Purpose: Radiation treatment planning for head and neck cancer is a complex process with much variability; automated treatment planning is a promising option to improve plan quality and efficiency. This study compared radiation plans generated from a fully automated radiation treatment planning system to plans generated manually that had been clinically approved and delivered., Methods and Materials: The study cohort consisted of 50 patients treated by a specialized head and neck cancer team at a tertiary care center. An automated radiation treatment planning system, the Radiation Planning Assistant, was used to create autoplans for all patients using their original, approved contours. Common dose-volume histogram (DVH) criteria were used to compare the quality of autoplans to the clinical plans. Fourteen radiation oncologists, each from a different institution, then reviewed and compared the autoplans and clinical plans in a blinded fashion., Results: Autoplans and clinical plans were very similar with regard to DVH metrics for coverage and critical structure constraints. Physician reviewers found both the clinical plans and autoplans acceptable for use; overall, 78% of the clinical plans and 88% of the autoplans were found to be usable as is (without any edits). When asked to choose which plan would be preferred for approval, 27% of physician reviewers selected the clinical plan, 47% selected the autoplan, 25% said both were equivalent, and 0% said neither. Hence, overall, 72% of physician reviewers believed the autoplan or either the clinical or autoplan was preferable., Conclusions: Automated radiation treatment planning creates consistent, clinically acceptable treatment plans that meet DVH criteria and are found to be appropriate on physician review., (Copyright © 2021 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.)

The purpose of this study is to compare the ejection fraction (EF) calculation of CT and SPECT at high heart rate. A dynamic cardiac phantom with programmable end-systolic volume (ESV), end-diastolic volume (EDV), and heart rate was used to compare CT, which has high spatial resolution (< 1 mm) and modest temporal resolution of 175 msec, and SPECT, which has high temporal resolution of 16 bins per cardiac cycle but poor spatial resolution (> 1 cm) in EF, ESV, and EDV at the heart rates ≤ 100 bpm for EF = 30 (disease state) and EF = 60 (healthy state). EF calculations for SPECT were accurate in 2% for 40 to 100 bpm for both EF = 30 and EF = 60, and were not heart rate dependent although both ESV and EDV could be underestimated by 18-20%. EF calculations for CT were accurate in 2.2% for 40 and 60 bpm. Inaccuracy in EF calculations, ESV and EDV estimates increased when the heart rate or EF increased. SPECT was accurate for EF calculation for the heart rates ≤ 100 bpm and CT was accurate for the heart rates of ≤ 60 bpm. CT was less accurate for the high heart rates of 80 and 100 bpm, or high EF = 60.

Cytotoxic T lymphocyte (CTL)-based cancer immunotherapies have shown great promise for inducing clinical regressions by targeting tumor-associated antigens (TAA). To expand the TAA landscape of pancreatic ductal adenocarcinoma (PDAC), we performed tandem mass spectrometry analysis of HLA class I-bound peptides from 35 PDAC patient tumors. This identified a shared HLA-A*0101 restricted peptide derived from co-transcriptional activator Vestigial-like 1 (VGLL1) as a putative TAA demonstrating overexpression in multiple tumor types and low or absent expression in essential normal tissues. Here we show that VGLL1-specific CTLs expanded from the blood of a PDAC patient could recognize and kill in an antigen-specific manner a majority of HLA-A*0101 allogeneic tumor cell lines derived not only from PDAC, but also bladder, ovarian, gastric, lung, and basal-like breast cancers. Gene expression profiling reveals VGLL1 as a member of a unique group of cancer-placenta antigens (CPA) that may constitute immunotherapeutic targets for patients with multiple cancer types.

Purpose: Our purpose was to investigate and classify the reasons why institutions fail the Imaging and Radiation Oncology Core (IROC) stereotactic body radiation therapy (SBRT) spine and moving lung phantoms, which are used to credential institutions for clinical trial participation., Methods and Materials: All IROC moving lung and SBRT spine phantom irradiation failures recorded from January 2012 to December 2018 were evaluated in this study. A failure was a case where the institution did not meet the established IROC criteria for agreement between planned and delivered dose. We analyzed the reports for all failing irradiations, including point dose disagreement, dose profiles, and gamma analyses. Classes of failure patterns were created and used to categorize each instance., Results: There were 158 failing cases analyzed: 116 of 1052 total lung irradiations and 42 of 263 total spine irradiations. Seven categories were required to describe the lung phantom failures, whereas 4 were required for the spine. Types of errors present in both phantom groups included systematic dose and localization errors. Fifty percent of lung failures were due to a superior-inferior localization error, that is, error in the direction of major motion. Systematic dose errors, however, contributed to only 22% of lung failures. In contrast, the majority (60%) of spine phantom failures were due to systematic dose errors, with localization errors (in any direction) accounting for only 14% of failures., Conclusions: There were 2 distinct patterns of failure between the IROC moving lung and SBRT spine phantoms. The majority of the lung phantom failures were due to localization errors, whereas the spine phantom failures were largely attributed to systematic dose errors. Both of these errors are clinically relevant and could manifest as errors in patient cases. These findings highlight the value of independent end-to-end dosimetry audits and can help guide the community in improving the quality of radiation therapy by focusing attention on where errors manifest in the community., (Copyright © 2020 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.)

Plasma and tumor caveolin-1 (Cav-1) are linked with disease progression in prostate cancer. Here we report that metabolomic profiling of longitudinal plasmas from a prospective cohort of 491 active surveillance (AS) participants indicates prominent elevations in plasma sphingolipids in AS progressors that, together with plasma Cav-1, yield a prognostic signature for disease progression. Mechanistic studies of the underlying tumor supportive onco-metabolism reveal coordinated activities through which Cav-1 enables rewiring of cancer cell lipid metabolism towards a program of 1) exogenous sphingolipid scavenging independent of cholesterol, 2) increased cancer cell catabolism of sphingomyelins to ceramide derivatives and 3) altered ceramide metabolism that results in increased glycosphingolipid synthesis and efflux of Cav-1-sphingolipid particles containing mitochondrial proteins and lipids. We also demonstrate, using a prostate cancer syngeneic RM-9 mouse model and established cell lines, that this Cav-1-sphingolipid program evidences a metabolic vulnerability that is targetable to induce lethal mitophagy as an anti-tumor therapy.

A non-immunogenic tumor microenvironment (TME) is a significant barrier to immune checkpoint blockade (ICB) response. The impact of Polybromo-1 (PBRM1) on TME and response to ICB in renal cell carcinoma (RCC) remains to be resolved. Here we show that PBRM1/Pbrm1 deficiency reduces the binding of brahma-related gene 1 (BRG1) to the IFNγ receptor 2 (Ifngr2) promoter, decreasing STAT1 phosphorylation and the subsequent expression of IFNγ target genes. An analysis of 3 independent patient cohorts and of murine pre-clinical models reveals that PBRM1 loss is associated with a less immunogenic TME and upregulated angiogenesis. Pbrm1 deficient Renca subcutaneous tumors in mice are more resistance to ICB, and a retrospective analysis of the IMmotion150 RCC study also suggests that PBRM1 mutation reduces benefit from ICB. Our study sheds light on the influence of PBRM1 mutations on IFNγ-STAT1 signaling and TME, and can inform additional preclinical and clinical studies in RCC.

This phase 2 study was conducted to prospectively evaluate how clinical and biological factors correlate with outcome in patients with treatment-naive (TN) and relapsed (R) chronic lymphocytic leukemia (CLL) treated with lenalidomide and rituximab. Oral lenalidomide 10 mg was administered daily starting on day 9 of cycle 1. IV rituximab 375 mg/m 2 was administered weekly during cycle 1 and every 4 weeks for cycles 3 to 12. Sequencing of a custom panel of 295 genes was performed in pretreatment bone marrow samples. The study included 61 patients with TN CLL and 59 with R CLL; the overall response rate (ORR) was 73% and 64%, respectively. A baseline β 2 -microglobulin level <4 mg/L was associated with higher ORR in both groups (both, P = .03), and absence of mutations in the NOTCH signaling pathway showed a trend for association with higher ORR in R CLL ( P = .10). Median PFS was 50 months in TN patients and 28 months in R patients. On multivariate analysis, age ≥65 years ( P = .02) was associated with shorter PFS in TN patients, whereas according to univariate analysis, >2 previous therapies ( P = .02) was the only factor associated with shorter PFS in R patients. A trend for association between mutations in the NOTCH pathway and shorter PFS was observed in TN CLL ( P = .15). Further exploration of the NOTCH pathway may help optimize the efficacy of this combination in patients with CLL. This study protocol was approved by the University of Texas MD Anderson Cancer Center institutional review board and registered at clinicaltrials.gov (#NCT01446133)., (© 2019 by The American Society of Hematology.)

Achieving undetectable MRD (U-MRD) status after chemoimmunotherapy predicts longer progression-free and overall survival. The predictive factors and timing of relapse in patients with U-MRD and value of interim MRD analysis are ill-defined. This was a prospective study of 289 patients with CLL treated first-line with FCR. MRD analysis was performed after course 3 (C3) and at end of therapy (EOT) in bone marrow using 4-color flow cytometry (sensitivity 10 -4 ). Eighteen percent of patients had U-MRD after C3 and 48% at EOT. U-MRD status at EOT was associated with longer PFS (median NR vs 38 mo, p < 0.001). MRD level (≤1% vs >1%) after C3 predicted greater likelihood of U-MRD status at EOT (64% vs 9%, p < 0.001). PFS was significantly longer for patients with MRD ≤1% vs >1% after C3 (median 73 mo vs 41 mo, p < 0.001), but similar for <0.01% vs 0.01-1%. Interim MRD status may therefore be used for risk stratification and to individualize therapy. Eighty-five patients with U-MRD status at EOT had yearly blood MRD monitoring; MRD re-emerged in 38/85, a median of 48 mo after EOT and preceded clinical progression by a median of 24 months, which may allow development of early intervention strategies.

Background and Purpose: Remote beam output audits, which independently measure an institution's machine calibration, are a common component of independent radiotherapy peer review. This work reviews the results and trends of these audit results across several organisations and geographical regions., Materials and Methods: Beam output audit results from the Australian Clinical Dosimetry Services, International Atomic Energy Agency, Imaging and Radiation Oncology Core, and Radiation Dosimetry Services were evaluated from 2010 to the present. The rate of audit results outside a +/-5% tolerance was evaluated for photon and electron beams as a function of the year of irradiation and nominal beam energy. Additionally, examples of confirmed calibration errors were examined to provide guidance to clinical physicists and auditing bodies., Results: Of the 210,167 audit results, 1323 (0.63%) were outside of tolerance. There was a clear trend of improved audit performance for more recent dates, and while all photon energies generally showed uniform rates of results out of tolerance, low (6 MeV) and high (≥18 MeV) energy electron beams showed significantly elevated rates. Twenty nine confirmed calibration errors were explored and attributed to a range of issues, such as equipment failures, errors in setup, and errors in performing the clinical reference calibration. Forty-two percent of these confirmed errors were detected during ongoing periodic monitoring, and not at the time of the first audit of the machine., Conclusions: Remote beam output audits have identified, and continue to identify, numerous and often substantial beam calibration errors., Competing Interests: Conflict of Interest: No conflict to declare

In this paper, we propose a Bayesian hierarchical approach to infer network structures across multiple sample groups where both shared and differential edges may exist across the groups. In our approach, we link graphs through a Markov random field prior. This prior on network similarity provides a measure of pairwise relatedness that borrows strength only between related groups. We incorporate the computational efficiency of continuous shrinkage priors, improving scalability for network estimation in cases of larger dimensionality. Our model is applied to patient groups with increasing levels of chronic obstructive pulmonary disease severity, with the goal of better understanding the break down of gene pathways as the disease progresses. Our approach is able to identify critical hub genes for four targeted pathways. Furthermore, it identifies gene connections that are disrupted with increased disease severity and that characterize the disease evolution. We also demonstrate the superior performance of our approach with respect to competing methods, using simulated data.

By analyzing multitissue gene expression and genome-wide genetic variation data in samples from a vervet monkey pedigree, we generated a transcriptome resource and produced the first catalog of expression quantitative trait loci (eQTLs) in a nonhuman primate model. This catalog contains more genome-wide significant eQTLs per sample than comparable human resources and identifies sex- and age-related expression patterns. Findings include a master regulatory locus that likely has a role in immune function and a locus regulating hippocampal long noncoding RNAs (lncRNAs), whose expression correlates with hippocampal volume. This resource will facilitate genetic investigation of quantitative traits, including brain and behavioral phenotypes relevant to neuropsychiatric disorders.

The observation that variants regulating gene expression (expression quantitative trait loci, eQTL) are at a high frequency among SNPs associated with complex traits has made the genome-wide characterization of gene expression an important tool in genetic mapping studies of such traits. As part of a study to identify genetic loci contributing to bipolar disorder and other quantitative traits in members of 26 pedigrees from Costa Rica and Colombia, we measured gene expression in lymphoblastoid cell lines derived from 786 pedigree members. The study design enabled us to comprehensively reconstruct the genetic regulatory network in these families, provide estimates of heritability, identify eQTL, evaluate missing heritability for the eQTL, and quantify the number of different alleles contributing to any given locus. In the eQTL analysis, we utilize a recently proposed hierarchical multiple testing strategy which controls error rates regarding the discovery of functional variants. Our results elucidate the heritability and regulation of gene expression in this unique Latin American study population and identify a set of regulatory SNPs which may be relevant in future investigations of complex disease in this population. Since our subjects belong to extended families, we are able to compare traditional kinship-based estimates with those from more recent methods that depend only on genotype information.

In this paper, we propose a Bayesian approach to inference on multiple Gaussian graphical models. Specifically, we address the problem of inferring multiple undirected networks in situations where some of the networks may be unrelated, while others share common features. We link the estimation of the graph structures via a Markov random field (MRF) prior which encourages common edges. We learn which sample groups have a shared graph structure by placing a spike-and-slab prior on the parameters that measure network relatedness. This approach allows us to share information between sample groups, when appropriate, as well as to obtain a measure of relative network similarity across groups. Our modeling framework incorporates relevant prior knowledge through an edge-specific informative prior and can encourage similarity to an established network. Through simulations, we demonstrate the utility of our method in summarizing relative network similarity and compare its performance against related methods. We find improved accuracy of network estimation, particularly when the sample sizes within each subgroup are moderate. We also illustrate the application of our model to infer protein networks for various cancer subtypes and under different experimental conditions.