1e:I[23489,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"ShareMenu"] 1f:I[55078,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"AuthorMetricsPanel"] 20:I[39060,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"AuthorPapers"] 3f:I[49310,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js"],"IconMark"] 21:T97a,Background Since the beginning of the coronavirus disease 2019 (COVID-19) epidemic, misinformation has been spreading uninhibited over traditional and social media at a rapid pace. We sought to analyze the magnitude of misinformation that is being spread on Twitter (Twitter, Inc., San Francisco, CA) regarding the coronavirus epidemic. Materials and methods We conducted a search on Twitter using 14 different trending hashtags and keywords related to the COVID-19 epidemic. We then summarized and assessed individual tweets for misinformation in comparison to verified and peer-reviewed resources. Descriptive statistics were used to compare terms and hashtags, and to identify individual tweets and account characteristics. Results The study included 673 tweets. Most tweets were posted by informal individuals/groups (66%), and 129 (19.2%) belonged to verified Twitter accounts. The majority of included tweets contained serious content (91.2%); 548 tweets (81.4%) included genuine information pertaining to the COVID-19 epidemic. Around 70% of the tweets tackled medical/public health information, while the others were pertaining to sociopolitical and financial factors. In total, 153 tweets (24.8%) included misinformation, and 107 (17.4%) included unverifiable information regarding the COVID-19 epidemic. The rate of misinformation was higher among informal individual/group accounts (33.8%, p: <0.001). Tweets from unverified Twitter accounts contained more misinformation (31.0% vs 12.6% for verified accounts, p: <0.001). Tweets from healthcare/public health accounts had the lowest rate of unverifiable information (12.3%, p: 0.04). The number of likes and retweets per tweet was not associated with a difference in either false or unverifiable content. The keyword "COVID-19" had the lowest rate of misinformation and unverifiable information, while the keywords "#2019_ncov" and "Corona" were associated with the highest amount of misinformation and unverifiable content respectively. Conclusions Medical misinformation and unverifiable content pertaining to the global COVID-19 epidemic are being propagated at an alarming rate on social media. We provide an early quantification of the magnitude of misinformation spread and highlight the importance of early interventions in order to curb this phenomenon that endangers public safety at a time when awareness and appropriate preventive actions are paramount.22:T45f,Tumor microbiota can produce active metabolites that affect cancer and immune cell signaling, metabolism, and proliferation. Here, we explore tumor and gut microbiome features that affect chemoradiation response in patients with cervical cancer using a combined approach of deep microbiome sequencing, targeted bacterial culture, and in vitro assays. We identify that an obligate L-lactate-producing lactic acid bacterium found in tumors, Lactobacillus iners, is associated with decreased survival in patients, induces chemotherapy and radiation resistance in cervical cancer cells, and leads to metabolic rewiring, or alterations in multiple metabolic pathways, in tumors. Genomically similar L-lactate-producing lactic acid bacteria commensal to other body sites are also significantly associated with survival in colorectal, lung, head and neck, and skin cancers. Our findings demonstrate that lactic acid bacteria in the tumor microenvironment can alter tumor metabolism and lactate signaling pathways, causing therapeutic resistance. Lactic acid bacteria could be promising therapeutic targets across cancer types.23:T5b5,Diversity of the gut microbiome is associated with higher response rates for cancer patients receiving immunotherapy but has not been investigated in patients receiving radiation therapy. Additionally, current studies investigating the gut microbiome and outcomes in cancer patients may not have adjusted for established risk factors. Here, we sought to determine if diversity and composition of the gut microbiome was independently associated with survival in cervical cancer patients receiving chemoradiation. Our study demonstrates that the diversity of gut microbiota is associated with a favorable response to chemoradiation. Additionally, compositional variation among patients correlated with short term and long-term survival. Short term survivor fecal samples were significantly enriched in Porphyromonas, Porphyromonadaceae, and Dialister, whereas long term survivor samples were significantly enriched in Escherichia Shigella, Enterobacteriaceae, and Enterobacteriales. Moreover, analysis of immune cells from cervical tumor brush samples by flow cytometry revealed that patients with a high microbiome diversity had increased tumor infiltration of CD4+ lymphocytes as well as activated subsets of CD4 cells expressing ki67+ and CD69+ over the course of radiation therapy. Modulation of the gut microbiota before chemoradiation might provide an alternative way to enhance treatment efficacy and improve treatment outcomes in cervical cancer patients.24:T7f1,BACKGROUND: A diverse and abundant gut microbiome can improve cancer patients' treatment response; however, the effect of pelvic chemoradiotherapy (CRT) on gut diversity and composition is unclear. The purpose of this prospective study was to identify changes in the diversity and composition of the gut microbiome during and after pelvic CRT. MATERIALS AND METHODS: Rectal swabs from 58 women with cervical, vaginal, or vulvar cancer from two institutions were prospectively analyzed before CRT (baseline), during CRT (weeks 1, 3, and 5), and at first follow-up (week 12) using 16Sv4 rRNA gene sequencing of the V4 hypervariable region of the bacterial 16S rRNA marker gene. 42 of these patients received antibiotics during the study period. Observed operational taxonomic units (OTUs; representative of richness) and Shannon, Simpson, Inverse Simpson, and Fisher diversity indices were used to characterize alpha (within-sample) diversity. Changes over time were assessed using a paired t-test, repeated measures ANOVA, and linear mixed modeling. Compositional changes in specific bacteria over time were evaluated using linear discriminant analysis effect size. RESULTS: Gut microbiome richness and diversity levels continually decreased throughout CRT (mean Shannon diversity index, 2.52 vs. 2.91; all P <0.01), but were at or near baseline levels in 60% of patients by week 12. Patients with higher gut diversity at baseline had the steepest decline in gut microbiome diversity. Gut microbiome composition was significantly altered during CRT, with increases in Proteobacteria and decreases in Clostridiales, but adapted after CRT, with increases in Bacteroides species. CONCLUSION: After CRT, the diversity of the gut microbiomes in this population tended to return to baseline levels by the 12 week follow-up period, but structure and composition remained significantly altered. These changes should be considered when designing studies to analyze the gut microbiome in patients who receive pelvic CRT for gynecologic cancers.25:T766,Although high-risk human papillomavirus infection is a well-established risk factor for cervical cancer, other co-factors within the local microenvironment may play an important role in the development of cervical cancer. The current study aimed to characterize the cervicovaginal microbiota in women with premalignant dysplasia or invasive cervical cancer compared with that of healthy women. The study comprised 120 Ethiopian women (60 cervical cancer patients who had not received any treatment, 25 patients with premalignant dysplasia, and 35 healthy women). Cervicovaginal specimens were collected using either an Isohelix DNA buccal swab or an Evalyn brush, and ribosomal RNA sequencing was used to characterize the cervicovaginal microbiota. Shannon and Simpson diversity indices were used to evaluate alpha diversity. Beta diversity was examined using principal coordinate analysis of weighted UniFrac distances. Alpha diversity was significantly higher in patients with cervical cancer than in patients with dysplasia and in healthy women (p < 0.01). Beta diversity was also significantly different in cervical cancer patients compared with the other groups (weighted UniFrac Bray-Curtis, p < 0.01). Microbiota composition differed between the dysplasia and cervical cancer groups. Lactobacillus iners was particularly enriched in patients with cancer, and a high relative abundance of Lactobacillus species was identified in the dysplasia and healthy groups, whereas Porphyromonas, Prevotella, Bacteroides, and Anaerococcus species predominated in the cervical cancer group. In summary, we identified differences in cervicovaginal microbiota diversity, composition, and relative abundance between women with cervical cancer, women with dysplasia, and healthy women. Additional studies need to be carried out in Ethiopia and other regions to control for variation in sample collection.26:T9fa,Background Squamous cell carcinoma of the anus (SCCA) is a rare gastrointestinal cancer. Factors associated with progression of HPV infection to anal dysplasia and cancer are unclear and screening guidelines and approaches for anal dysplasia are less clear than for cervical dysplasia. One potential contributing factor is the anorectal microbiome. In this study, we aimed to identify differences in anal microbiome composition in the settings of HPV infection, anal dysplasia, and anal cancer in this rare disease. Methods Patients were enrolled in two prospective studies. Patients with anal dysplasia were part of a cross-sectional cohort that enrolled women with high-grade lower genital tract dysplasia. Anorectal tumor swabs were prospectively collected from patients with biopsy-confirmed locally advanced SCCA prior to receiving standard-of-care chemoradiotherapy (CRT). Patients with high-grade lower genital tract dysplasia without anal dysplasia were considered high-risk (HR Normal). 16S V4 rRNA Microbiome sequencing was performed for anal swabs. Alpha and Beta Diversity and composition were compared for HR Normal, anal dysplasia, and anal cancer. Results 60 patients with high-grade lower genital tract dysplasia were initially enrolled. Seven patients had concurrent anal dysplasia and 44 patients were considered HR Normal. Anorectal swabs from 21 patients with localized SCCA were included, sequenced, and analyzed in the study. Analysis of weighted and unweighted UniFrac distances demonstrated significant differences in microbial community composition between anal cancer and HR normal (p=0.018). LEfSe identified that all three groups exhibited differential enrichment of specific taxa. Peptoniphilus (p=0.028), Fusobacteria (p=0.0295), Porphyromonas (p=0.034), and Prevotella (p=0.029) were enriched in anal cancer specimens when compared to HR normal. Conclusion Although alpha diversity was similar between HR Normal, dysplasia and cancer patients, composition differed significantly between the three groups. Increased anorectal Peptoniphilus, Fusobacteria, Porphyromonas, and Prevotella abundance were associated with anal cancer. These organisms have been reported in various gastrointestinal cancers with roles in facilitating the proinflammatory microenvironment and neoplasia progression. Future work should investigate a potential role of microbiome analysis in screening for anal dysplasia and investigation into potential mechanisms of how these microbial imbalances influence the immune system and anal carcinogenesis.27:Tbbd,Abstract IMPORTANCE Patients with localized squamous cell carcinoma of the anus (SCCA) who experience treatment toxicity or recurrences have few therapeutic options. Investigation into the microbiome’s influence on treatment toxicity or its potential use as a predictive biomarker in this rare disease could improve these patients’ outcomes. OBJECTIVE To longitudinally characterize the SCCA tumor microbiome and assess its association with treatment-related toxicities. DESIGN Prospective cohort study. SETTING Single tertiary cancer center. PARTICIPANTS Twenty-two patients with biopsy-confirmed non-metastatic SCCA receiving standard-of-care chemoradiotherapy as part of an Institutional Review Board-approved study from April 2017 to July 2019. MAIN OUTCOMES AND MEASURES Diversity and taxonomic characterization of the SCCA microbiome throughout chemoradiotherapy using swab-based anorectal microbial specimen collection and 16S rRNA gene sequencing. RESULTS Twenty-two SCCA patients were included in this study with a median (range) age of 58.5 (39-77), and 18 (82%) were women. Alpha diversity remained relatively stable throughout chemoradiotherapy, except for decreases in the Chao1 ( P =0.03) and Observed Features ( P =0.03) indices at week 5 relative to baseline. Tumor microbial compositions measured using weighted UniFrac changed significantly by the end of treatment ( P= 0.03). Linear discriminant analysis effect sizes revealed differential enrichment of bacteria at specific time points, including the enrichment of Clostridia at both baseline and follow-up and the enrichment of Corynebacterium at week 5. Patients experiencing high toxicity at week 5 had higher relative abundances of Clostridia, Actinobacteria, and Clostridiales at baseline ( P =0.03 for all). CONCLUSIONS AND RELEVANCE Our study presents the first longitudinal characterization of the SCCA microbiome throughout chemoradiation. The tumor microbiome undergoes significant changes during and after chemoradiotherapy, and patient-reported toxicity levels are associated with patients’ microbial profiles. Further studies into these microbial characterizations and associations are needed to elucidate the tumor microbiome’s role in predicting treatment-related outcomes for SCCA patients. Key Points QUESTION How does the squamous cell anal tumor microbiome change during chemoradiotherapy, and how do these changes influence treatment-related toxicity? FINDINGS Prospective longitudinal characterization of anal cancer patients revealed significant modulation of the local tumor microbiome in response to chemoradiotherapy including shifts in overall composition and differential enrichment of key taxa. Additionally, enrichment of specific taxa at baseline was associated with increased levels of treatment-related toxicities by the end of treatment. MEANING The anal tumor microbiome is significantly altered by chemoradiotherapy and could potentially serve as a biomarker for treatment-related toxicities.28:T6eb,PURPOSE: Survival analyses of novel agents with long-term responders often exhibit differential hazard rates over time. Such proportional hazards violations (PHV) may reduce the power of the log-rank test and lead to misinterpretation of trial results. We aimed to characterize the incidence and study attributes associated with PHVs in phase III oncology trials and assess the utility of restricted mean survival time and maximum combination test as additional analyses. EXPERIMENTAL DESIGN: Clinicaltrials.gov and PubMed were searched to identify two-arm, randomized, phase III superiority-design cancer trials with time-to-event primary endpoints and published results through 2020. Patient-level data were reconstructed from published Kaplan-Meier curves. PHVs were assessed using Schoenfeld residuals. RESULTS: Three hundred fifty-seven Kaplan-Meier comparisons across 341 trials were analyzed, encompassing 292,831 enrolled patients. PHVs were identified in 85/357 [23.8%; 95% confidence interval (CI), 19.7%, 28.5%] comparisons. In multivariable analysis, non-overall survival endpoints [OR, 2.16 (95% CI, 1.21, 3.87); P = 0.009] were associated with higher odds of PHVs, and immunotherapy comparisons [OR 1.94 (95% CI, 0.98, 3.86); P = 0.058] were weakly suggestive of higher odds of PHVs. Few trials with PHVs (25/85, 29.4%) prespecified a statistical plan to account for PHVs. Fourteen trials with PHVs exhibited discordant statistical signals with restricted mean survival time or maximum combination test, of which 10 (71%) reported negative results. CONCLUSIONS: PHVs are common across therapy types, and attempts to account for PHVs in statistical design are lacking despite the potential for results exhibiting nonproportional hazards to be misinterpreted.29:T607,We evaluated the association of disease outcome with T cell immune-related characteristics and T cell receptor (TCR) repertoire in malignant ascites from patients with high-grade epithelial ovarian cancer. Ascitic fluid samples were collected from 47 high-grade epithelial ovarian cancer patients and analyzed using flow cytometry and TCR sequencing to characterize the complementarity determining region 3 TCR β-chain. TCR functions were analyzed using the McPAS-TCR and VDJ databases. TCR clustering was implemented using Grouping of Lymphocyte Interactions by Paratope Hotspots software. Patients with poor prognosis had ascites characterized by an increased ratio of CD8+ T cells to regulatory T cells, which correlated with an increased productive frequency of the top 100 clones and decreased productive entropy. TCRs enriched in patients with an excellent or good prognosis were more likely to recognize cancer antigens and contained more TCR reads predicted to recognize epithelial ovarian cancer antigens. In addition, a TCR motif that is predicted to bind the TP53 neoantigen was identified, and this motif was enriched in patients with an excellent or good prognosis. Ascitic fluid in high-grade epithelial ovarian cancer patients with an excellent or good prognosis is enriched with TCRs that may recognize ovarian cancer-specific neoantigens, including mutated TP53 and TEAD1. These results suggest that an effective antigen-specific immune response in ascites is vital for a good outcome in high-grade epithelial ovarian cancer.2a:T5f8,BACKGROUND: Gut microbiome community composition differs between cervical cancer (CC) patients and healthy controls, and increased gut diversity is associated with improved outcomes after treatment. We proposed that functions of specific microbial species adjoining the mucus layer may directly impact the biology of CC. METHOD: Metagenomes of rectal swabs in 41 CC patients were examined by whole-genome shotgun sequencing to link taxonomic structures, molecular functions, and metabolic pathway to patient's clinical characteristics. RESULTS: Significant association of molecular functions encoded by the metagenomes was found with initial tumor size and stage. Profiling of the molecular function abundances and their distributions identified 2 microbial communities co-existing in each metagenome but having distinct metabolism and taxonomic structures. Community A (Clostridia and Proteobacteria predominant) was characterized by high activity of pathways involved in stress response, mucus glycan degradation and utilization of degradation byproducts. This community was prevalent in patients with larger, advanced stage tumors. Conversely, community B (Bacteroidia predominant) was characterized by fast growth, active oxidative phosphorylation, and production of vitamins. This community was prevalent in patients with smaller, early-stage tumors. CONCLUSIONS: In this study, enrichment of mucus degrading microbial communities in rectal metagenomes of CC patients was associated with larger, more advanced stage tumors.2b:T475,Pancreatic cancer and its treatment often dramatically impact patients' quality of life (QoL). Given this, as well as increased focus on QoL measures in clinical oncology, there has been a rise in the number of instruments that measure patient-reported outcomes (PROs). In this review, we describe the landscape of different PRO instruments pertaining to pancreatic cancer, with specific emphasis on PRO findings related to pancreatic cancer patients receiving radiotherapy (RT). Twenty-five of the most commonly utilized PROs are compared in detail. Notably, most of the PRO tools discussed are not specific to pancreatic cancer but are generic and have been used in various malignancies. Published findings concerning PROs in pancreatic cancer involving RT are also extracted and summarized. Among the measures used, the European Organization for Research and Treatment Cancer QLQ-C30 was the most commonly utilized. We recommend a careful selection of PRO measures in clinical pancreatic cancer research and care and encourage the use of a combination of symptom-specific and global QoL tools to more fully capture patients' perspectives.2c:T568,Differential censoring, which refers to censoring imbalance between treatment arms, may bias the interpretation of survival outcomes in clinical trials. In 146 phase III oncology trials with statistically significant time-to-event surrogate primary endpoints, we evaluated the association between differential censoring in the surrogate primary endpoints, control arm adequacy, and the subsequent statistical significance of overall survival results. Twenty-four (16%) trials exhibited differential censoring that favored the control arm, whereas 15 (10%) exhibited differential censoring that favored the experimental arm. Positive overall survival was more common in control arm differential censoring trials (63%) than in trials without differential censoring (37%) or with experimental arm differential censoring (47%; odds ratio = 2.64, 95% confidence interval = 1.10 to 7.20; P = .04). Control arm differential censoring trials more frequently used suboptimal control arms at 46% compared with 20% without differential censoring and 13% with experimental arm differential censoring (odds ratio = 3.60, 95% confidence interval = 1.29 to 10.0; P = .007). The presence of control arm differential censoring in trials with surrogate primary endpoints, especially in those with overall survival conversion, may indicate an inadequate control arm and should be examined and explained.2d:T6c9,PURPOSE: Among patients with anal cancer, chemoradiotherapy is often associated with toxicities that diminish quality of life. We describe the GI-related patient-reported outcomes (PROs) of patients with anal cancer receiving chemoradiotherapy to improve patient-physician communication. METHODS: We prospectively followed patients with nonmetastatic squamous cell carcinoma of the anal canal who received definitive chemoradiotherapy. Patients completed the bowel subdomain of the Expanded Prostate Cancer Index Composite (EPIC) questionnaire before treatment and at 4 subsequent timepoints. We used the paired Wilcoxon test to compare EPIC scores at different times. RESULTS: The study included 21 patients; median age was 57 years. Most patients (52%) had T2 and either N0 or N1 disease (81%). Most patients (91%) received chemotherapy with cisplatin-fluorouracil and either intensity-modulated radiotherapy or volumetric modulated arc therapy. Compared with the patients’ median overall summary score at baseline (66), their median score at 1 week (82) was higher ( P = .009), whereas their median score at 5 weeks (54) was lower ( P = .025). The patients’ median overall summary score at baseline and at 3 months did not differ ( P = .919). Three months after radiotherapy, most patients reported minimal adverse effects compared with baseline. CONCLUSION: The GI-related PROs of patients with anal cancer tend to fluctuate during radiotherapy but return to baseline by 3 months, at which time most patients report few or no residual adverse effects. We provide a clear timeline of GI acute toxicity using sequential PRO measurements that will improve patient-physician communication regarding expectations for cancer treatment.2e:Tb9d,1561 Background: Hazard ratio (HR)-based analyses used in oncology trials rely on the assumption of proportional hazards, i.e. a HR that is constant over time. Proportional hazards violations (PHVs) may lead to misinterpretation of trial results. Restricted mean survival time (RMST) is valid with non-proportional hazards and has received recent attention specifically for immunotherapy (IO) trials but has not been routinely adopted in oncology trial design as a whole. We aimed to comprehensively characterize the incidence and factors associated with PHVs among phase 3 oncology trials and assess RMST as an alternate measure of treatment effect in survival analysis. Methods: We used Clinicaltrials.gov to identify all superiority-design, 2-arm phase 3 cancer trials with time-dependent endpoints with published results through February 2020. We manually reconstructed patient-level data from published Kaplan-Meier (KM) curves, assessed PHVs with the Schoenfeld residual test (p <.05) and analyzed the RMST. To assess reconstruction accuracy, reported and reconstructed HRs were compared. Univariable logistic regression was used to assess the likelihood of PHVs by trial characteristic, with statistically significant factors (p <.05) included in a multivariable analysis. Concordance of RMST-based and HR-based analysis was established when both tests agreed as to the statistical significance of the comparison. Results: Of 342 KM comparisons eligible for reconstruction, 318 comparisons across 315 trials, enrolling 347,538 patients from 1989-2017, were accurately reconstructed and analyzed. PHVs were identified in 76/318 (23.9%) trials. There was no difference in likelihood of PHVs among IO vs non-IO trials (LR 2.31, 95% CI 0.30 17.85, P =.37), nor by disease site, year of trial initiation, or sample size. Few trials with PHVs (16/76) pre-specified a plan to account for non-proportional hazards in statistical design. Trials with an overall survival (OS) primary endpoint (PEP) were less likely to have PHVs than trials with a non-OS PEP (LR: 0.50, 95% CI 0.28 - 0.90, P =.02). Trials whose PEP was non-significant were more likely to have PHVs (LR 1.73, 95% CI 1.01 - 2.97, P =.047). No factor remained significantly associated with PHV in multivariable analysis. Overall, 291/318 (91.5%) KM comparisons were concordant. Among trials with PHVs, 5/76 were significant by RMST but not HR, and 5/76 were significant by HR but not RMST. Of these, 1 led to FDA drug approval, and 2 others are cited in NCCN guidelines. Conclusions: PHVs are common across all phase 3 cancer clinical trials. Attempts to account for PHVs in trial design are lacking despite the potential for trial misinterpretation in the event of non-proportional hazards. RMST-based analysis is broadly concordant with HR-based analysis and may aid in interpretation of trials with PHVs. Hence, we recommend that prospective trials include a priori a statistical plan to account for PHVs.2f:T6f0,ABSTRACT Background Statistical significance currently defines superiority in phase III oncology trials. However, this practice is increasingly questioned. Here, we estimated the fragility of phase III oncology trials. Methods Using Kaplan-Meier curves for the primary endpoints of 230 two-arm superiority phase III oncology trials, we reconstructed data for individual patients. We estimated the survival-inferred fragility index (SIFI) by iteratively flipping the best responder from the experimental arm to the control arm (SIFI B ) until the interpretation was changed according to the significance threshold of each trial. Severe fragility was defined by SIFI ≤ 1%. Results This study included 230 trials enrolling 184,752 patients. The median number of patients required to change trial interpretation was 8 (interquartile range, 4 to 19) or 1.4% (interquartile range, 0.7% to 3%) per SIFI B . Estimations of SIFI by multiple methods were largely consistent. For trials with an overall survival primary endpoint, the median SIFI B was 1% (IQR, 0.5% to 1.9%). Severe fragility was found in 87 trials (38%). As a continuous statistic, the original P value—but not its binary significance interpretation—was associated with fragility and severe fragility. Trials with subsequent FDA approval had lower odds of severe fragility. Lastly, the underlying survival model had differential effects on SIFI estimation. Conclusions Even among phase III oncology trials, which directly inform patient care, changes in the outcomes of few patients are often sufficient to change statistical significance and trial interpretation. These findings imply that current definitions of statistical significance used in phase III oncology are inadequate to identify replicable findings.30:T9b9,ABSTRACT Most oncology trials define superiority of an experimental therapy compared to a control therapy according to frequentist significance thresholds, which are widely misinterpreted. Posterior probability distributions computed by Bayesian inference may be more intuitive measures of uncertainty, particularly for measures of clinical benefit such as the minimum clinically important difference (MCID). Here, we manually reconstructed 194,129 individual patient-level outcomes across 230 phase III, superiority-design, oncology trials. Posteriors were calculated by Markov Chain Monte Carlo sampling using standard priors. All trials interpreted as positive had probabilities > 90% for marginal benefits (HR < 1). However, 38% of positive trials had ≤ 90% probabilities of achieving the MCID (HR < 0.8), even under an enthusiastic prior. A subgroup analysis of 82 trials that led to regulatory approval showed 30% had ≤ 90% probability for meeting the MCID under an enthusiastic prior. Conversely, 24% of negative trials had > 90% probability of achieving marginal benefits, even under a skeptical prior, including 12 trials with a primary endpoint of overall survival. Lastly, a phase III oncology-specific prior from a previous work, which uses published summary statistics rather than reconstructed data to compute posteriors, validated the individual patient-level data findings. Taken together, these results suggest that Bayesian models add considerable unique interpretative value to phase III oncology trials and provide a robust solution for overcoming the discrepancies between refuting the null hypothesis and obtaining a MCID. SIGNIFICANCE STATEMENT The statistical analyses of oncology trials are usually performed by calculating P values, although these are poorly understood. Using P value cutoffs, such as P < 0.05, may lead to some treatments being accepted which have little benefit, and other therapies being rejected which have considerable benefit. A more intuitive and direct probability— that an experimental treatment is better than a standard treatment—can be calculated by Bayesian statistics. Here we used software to obtain the outcomes of 194,129 patients enrolled across 230 trials and then calculated probabilities of benefit. Interpretations based on P values disagreed with the probabilities of benefit in one-third of trials. This study suggests that probabilities of benefit would considerably enhance the interpretation of oncology trials.31:T485,SUMMARY This study identifies a novel pathotype of cervical cancer-associated Lactobacillus iners ( L. iners ) that results in chemoradiation resistance in vitro and is associated with poor patient survival. Cervical cancer affects over half a million women a year around the world. Treatment for women with locally advanced cancer is delivered with definitive chemoradiation (CRT) but is curative for only 60% of patients. There are few validated molecular markers to identify patients who will respond poorly to treatment. Tumor microbiome features are associated with treatment resistance in patients with colon and pancreatic cancers, and thus we investigated their role in the response of cervical cancer to therapy. We identified a strong association between poor clinical response to CRT and tumors dominated by L. iners . Cancer-associated L. iners promoted in vitro resistance of cervical cancer cells and modified the local tumor immunologic microenvironment, while non-cancer-associated L. iners did not. Assembly of genomes from cancer-derived L. iners also demonstrated pathogenic, metabolic, and immune functions not found in healthy patients.32:T4c2,Abstract Background Next generation sequencing has progressed rapidly, characterizing microbial communities beyond culture-based or biochemical techniques. 16S ribosomal RNA gene sequencing (16S) produces reliable taxonomic classifications and relative abundances, while shotgun metagenome sequencing (WMS) allows higher taxonomic and functional resolution at greater cost. The purpose of this study was to determine if 16S and WMS provide congruent information for our patient population from paired fecal microbiome samples. Results Comparative indices were highly congruent between 16S and WMS. The most abundant genera for 16S and WMS data did not overlap. Overlap was observed at the Phylum level, as expected. However, relative abundances correlated poorly between the two methodologies (all p>0.05). Hierarchical clustering of both sequencing analyses identified overlapping enterotypes. Both approaches were in agreement with regard to demographic variables.Conclusion Diversity, evenness and richness are comparable when using 16S and WMS techniques, however relative abundances of individual genera are not. Clinical associations with diversity and evenness metrics were similarly identified with WMS or 16S.33:T920,PURPOSE: Cervical cancer remains a significant public health concern globally and particularly in sub-Saharan Africa, where high rates of HIV infection exacerbate cervical cancer incidence. Understanding the cervical microbiome and its role in cancer progression is essential, especially in regions where both cervical cancer incidence and HIV prevalence are high. This study aimed to characterize the cervical microbiome in women living with HIV (WLWH) and HIV-negative women with squamous cell carcinoma of the cervix in Botswana, compare the microbiome between before and after chemoradiation therapy (CRT) in WLWH, and assess the prognostic value of specific microbial taxa for overall survival (OS) in WLWH. PATIENTS AND METHODS: Cervical samples were collected from women with cervical cancer presenting to one hospital in 2018-2019. Patients' clinical data, including HIV status, were recorded. Microbial composition was analyzed using 16S rRNA gene sequencing. Microbiome diversity and composition were evaluated using alpha and beta diversity metrics. Differential microbial abundance was analyzed using linear discriminant analysis effect size. The association between microbial taxa and OS was explored using Cox proportional hazards regression. RESULTS: = .02), suggesting a more imbalanced microbiome in WLWH. WLWH had higher levels of Parvimonas and members of the Corynebacteriaceae and Micrococcaceae families, suggesting a shift toward a more pathogenic microbiome. In WLWH, CRT did not significantly alter overall microbial diversity. However, Lactobacillus and Sutterella were enriched before treatment, reflecting a less pathogenic microbiome, whereas Ruminococcus and Phascolarctobacterium and the families Caulobacterales and Flavobacteriia were enriched after treatment, reflecting microbial adaptations to the altered immune and treatment environment. Notably, higher levels of Flavobacteriia after CRT were independently associated with worse OS in WLWH. CONCLUSION: Microbiome profiles differ between WLWH and HIV-negative women with cervical cancer in Botswana. The microbiome might have prognostic significance. Future research is needed to better understand the significance of the microbiota in cervical cancer progression and treatment outcomes and the potential role of microbiome-targeted interventions.34:T4c2,Abstract Background Next generation sequencing has progressed rapidly, characterizing microbial communities beyond culture-based or biochemical techniques. 16S ribosomal RNA gene sequencing (16S) produces reliable taxonomic classifications and relative abundances, while shotgun metagenome sequencing (WMS) allows higher taxonomic and functional resolution at greater cost. The purpose of this study was to determine if 16S and WMS provide congruent information for our patient population from paired fecal microbiome samples. Results Comparative indices were highly congruent between 16S and WMS. The most abundant genera for 16S and WMS data did not overlap. Overlap was observed at the Phylum level, as expected. However, relative abundances correlated poorly between the two methodologies (all p>0.05). Hierarchical clustering of both sequencing analyses identified overlapping enterotypes. Both approaches were in agreement with regard to demographic variables.Conclusion Diversity, evenness and richness are comparable when using 16S and WMS techniques, however relative abundances of individual genera are not. Clinical associations with diversity and evenness metrics were similarly identified with WMS or 16S.35:T82d,6036 Background: Diversity of the gut microbiome is associated with response rates for patients receiving immunotherapy. Studies investigating the gut microbiome and outcomes in cancer patients often do not adjust for confounding patient and tumor characteristics. We sought to identify independent gut microbial risk factors in cervical cancer (CC) patients receiving chemoradiation (CRT) and to evaluate their impact on survival. Methods: We analyzed baseline 16S rDNA fecal microbiomes of CC patients receiving standard CRT. Patient and tumor characteristics were analyzed by univariate and multivariate Cox regression models for Recurrence-free survival (RFS) and Overall survival (OS) based on univariate p-value>0.2. Characteristics included age, body mass index (BMI), race, stage, grade, histology, nodal status, and max tumor size. Alpha (within sample) diversity was evaluated using Shannon diversity index (SDI). Kaplan-Meier curves were generated for patients with normal BMI and overweight/obese BMI based on Cox analysis. Results: 55 CC patients were included. Univariate analysis identified older age (Hazard Ratio (HR) of 0.93 (95% CI = 0.87-0.98, p = 0.0096)), SDI (HR of 0.51 (95% CI = 0.23-1.1, p = 0.087)) and BMI (HR of 0.92 (95% CI = 0.84-1, p = 0.096)) as risk factors for RFS. Multivariate survival analyses identified BMI and SDI as independent prognostic factors for RFS with a HR of 0.87 (95% CI = 0.77-0.98, p = 0.02) and 0.36 (95% CI = 0.15-0.84, p = 0.018) respectively. For OS, multivariate survival analyses again identified BMI and SDI as independent prognostic factors with a HR of 0.78 (95% CI = 0.623-0.97, p = 0.025) and 0.19 (95% CI = 0.043-0.83, p = 0.028) respectively. Conclusions: Gut diversity is a significant factor for predicting OS in CC patients undergoing CRT when BMI is accounted for, and may help explain the “obesity paradox” in cancer response. Studies exploring the relationship between gut diversity, CRT, and treatment efficacy are needed to further understand the role of the gut microbiome in treatment outcomes. [Table: see text]36:Tdcf,Abstract Purpose Next generation sequencing has progressed rapidly, characterizing microbial communities beyond culture-based or biochemical techniques. 16S ribosomal RNA gene sequencing (16S) produces reliable taxonomic classifications and relative abundances, while shotgun metagenome sequencing (WMS) allows higher taxonomic and functional resolution at greater cost. The purpose of this study was to determine if 16S and WMS provide congruent information for our patient population from paired fecal microbiome samples. Methods Patients with locally advanced cervical cancers were enrolled on a prospective, observational clinical trial with a rectal swab sample collected prior to chemoradiation. Bacterial DNA was extracted from each sample and divided in two parts for 16S or WMS sequencing. We used measures of diversity richness and evenness as comparators of 16S and WMS sequencing. Relative abundances of the most common taxa were also compared between both datasets. Both techniques were tested against baseline patient demographics to assess associations identified with either or both methods. Results Comparative indices were highly congruent between 16S and WMS. The most abundant genera for 16S and WMS data did not overlap. Overlap was observed at the Phylum level, as expected. However, relative abundances correlated poorly between the two methodologies (all p>0.05). Hierarchical clustering of both sequencing analyses identified overlapping enterotypes. Both approaches were in agreement with regard to demographic variables. Conclusion Diversity, evenness and richness are comparable when using 16S and WMS techniques, however relative abundances of individual genera are not. Clinical associations with diversity and evenness metrics were similarly identified with WMS or 16S. Importance The gut microbiome plays an important role in regulating human health and disease. 16S rRNA gene sequencing (16S) and the whole-metagenome shotgun DNA sequencing (WMS) are two approaches to describe the microbial community. 16S sequencing via any amplicon sequencing-based method offers advantages over WMS in terms of precision (specific gene targeting). Additionally, 16S has historically been less costly due to the simplicity of library preparation and it does not require the same level read coverage as WMS. In this study, we performed both sequencing methods on a single rectal swab sample obtained from each cervical cancer patient prior to treatment. We showed that these two methods provide comparable information for diversity, evenness, and richness at higher taxonomic resolution, but are discrepant at a lower resolution. These methodological findings provide valuable information for the design and interpretation of future investigations of the role of the gut microbiome in cancer. Tweet (optional: 256 words, please submit a Tweet that conveys the essential message of your manuscript.) 16S may be sufficient for most initial studies of the gut microbiome in cancer patients, but WMS may be required for analysis of lower level taxonomy. Research support This research was supported in part by the Radiological Society of North America Resident/Fellow Award (to L.E.C.), the National Institutes of Health (NIH) through MD Anderson’s Cancer Center Support Grant P30CA016672, the Emerson Collective and the National Institutes of Health T32 grant #5T32 CA101642-14 (T.T.S). This study was partially funded by The University of Texas MD Anderson Cancer Center HPV-related Cancers Moonshot (L.E.C and A.K.).37:T421,In phase III oncology trials, superiority is defined by statistical significance using P thresholds. However, this approach has been criticized because P is continuous. Here, we reconstruct patient-level data for 230 phase III oncology trials to model the robustness of statistical significance by estimating the survival-inferred fragility index (SIFI), defined as the smallest number of patients changing arms that alters the statistical significance interpretation. The median SIFI was 8 patients (IQR 4–19), representing 1.4% of enrollments (IQR 0.7%–3%). As a continuous statistic, P—but not the significance interpretation—was correlated with SIFI. Moreover, overall survival endpoints were more fragile than surrogate endpoints. Taken together, while phase III oncology trials are intended to robustly inform patient care, shifting the assignment of a few patients is often sufficient to upend the statistical significance interpretation. This vulnerability underscores the need for more robust strategies to identify superiority in oncology.38:T568,Differential censoring, which refers to censoring imbalance between treatment arms, may bias the interpretation of survival outcomes in clinical trials. In 146 phase III oncology trials with statistically significant time-to-event surrogate primary endpoints, we evaluated the association between differential censoring in the surrogate primary endpoints, control arm adequacy, and the subsequent statistical significance of overall survival results. Twenty-four (16%) trials exhibited differential censoring that favored the control arm, whereas 15 (10%) exhibited differential censoring that favored the experimental arm. Positive overall survival was more common in control arm differential censoring trials (63%) than in trials without differential censoring (37%) or with experimental arm differential censoring (47%; odds ratio = 2.64, 95% confidence interval = 1.10 to 7.20; P = .04). Control arm differential censoring trials more frequently used suboptimal control arms at 46% compared with 20% without differential censoring and 13% with experimental arm differential censoring (odds ratio = 3.60, 95% confidence interval = 1.29 to 10.0; P = .007). The presence of control arm differential censoring in trials with surrogate primary endpoints, especially in those with overall survival conversion, may indicate an inadequate control arm and should be examined and explained.39:T6a9,PURPOSE: value thresholds, which are frequently misinterpreted. Posterior probability, however, directly estimates the probability of the hypothesis at hand. Here, we reanalyze a large collection of modern phase III trials and benchmark posterior probability versus the standard trial interpretation based on statistical significance. METHODS: Outcomes from 194,129 patients were manually reconstructed from the primary end points of 230 phase III, superiority-design oncology trials. Posterior probabilities of treatment effect were then calculated across multiple priors and several effect sizes of clinical relevance, including minimum clinically important difference (MCID) defined as hazard ratio (HR) < 0.8 per ASCO criteria or HR < 0.64 per European Society of Medical Oncology (ESMO) criteria. RESULTS: value thresholds had probabilities >90% for achieving at least marginal benefits (HR < 1). However, only 62% of positive trials (74/120) had >90% probabilities of achieving the ASCO MCID (HR < 0.8), even under an enthusiastic prior, including 70% of trials (57/82) leading to regulatory approval. Only 30% of positive trials (36/120) had >90% probability of achieving the ESMO MCID (HR < 0.64). Conversely, 24% of trials (26/110) interpreted as not superior had >90% probability of achieving marginal benefits (HR < 1), even under a skeptical prior. CONCLUSION: Bayesian models, although often in agreement with statistical significance thresholds, add considerable unique interpretative value for a subset of phase III oncology trials. Posterior probability may provide a solution for overcoming the discrepancies between refuting the null hypothesis and detecting clinically relevant effects.3a:T71b,PURPOSE: This study investigates the interplay between T-cell receptor (TCR) immune characteristics and microbiome profiles to explore the relationship between immune diversity and microbial composition in cervical samples from Ethiopia. METHODS: Cervical specimens were collected from patients at Tikur Anbessa Specialized Hospital in Addis Ababa, and rural Butajira, south-central Ethiopia. Patient data, including age, human papillomavirus status, pathology, and TCR immune characteristics, were analyzed with a focus on the interactions between TCR profiles and microbiome compositions in malignant samples. RESULTS: Three distinct TCR profiles were identified: Group 1 (TCR active) exhibited features of active immune engagement, including high diversity, clonal expansion, and repertoire richness. Group 2 (TCR restricted) showed reduced TCR diversity and expansion, suggesting a restricted repertoire. Group 3 (TCR balanced) had moderate diversity and clonal activity. TCR repertoire groups were linked with microbial diversity, with Group 1 (TCR active) showing the highest number of microbes (high operational taxonomic units and microbial diversity). Maximum TCR clonal expansion positivity associated with microbial richness, while Group 3 (TCR balanced) was linked to reduced microbial alpha diversity. Taxonomic analysis revealed specific organisms enriched in TCR repertoire group. CONCLUSION: Variations in TCR profiles are linked to distinct microbial environments in cervical cancer with greater microbial richness in patients with greater maximum productive frequency. These findings underscore the interplay between TCR diversity, microbiome composition, and malignancy, offering insights into the potential implications for microbiome-targeted therapies and prognostic biomarkers in cervical cancer.3b:T1173,Background The terms ‘hot’ or ‘cold,’ pioneered by Galon et al., classify tumors based on their high or low levels of immune infiltration. 1 These descriptions influence the available treatment options for different cancer types,2–4 yet they do not inform patient-specific treatments that could harness each patient’s unique tumor microenvironment. Moreover, the high heterogeneity in tumor-immune contexture within and across various cancer types5–7 highlights the need for a quantitative immune temperature, beyond a qualitative hot or cold, to fully inform therapy options. We hypothesized that we could use pan-cancer pretreatment immune compositions to generate patient-specific immune temperatures that predict treatment response and outcome. Our objective was to generate data-driven immune temperatures validated on separate clinical cohorts.Methods We analyzed untreated tissue samples of 10,469 patients across 31 tumor types. We used ESTIMATE 8 to calculate tumor purity and CIBERSORT9 to estimate anti- and pro-tumor immune phenotypes.10 For each patient, we mapped the proportions of tumor, anti- and pro-tumor cells on a triangular domain, which represents every possible combination of the relative proportions of the three populations. Next, we generated anti- and pro-tumor scores and combined them in an optimal way to generate a normalized immune temperature, 0-100°. We then validated the immune temperature in a separate cohort of 59 non-small cell lung cancer (NSCLC) patients who underwent conventional fractionated radiotherapy (RT) (n = 36, locoregional control (LRC); n = 23, locoregional failure (LRF)).Results The derived quantitative immune temperature broadly aligns with the qualitative hot/cold cancer types. Furthermore, the immune temperature correlates with LRC after RT in NSCLC. Patients who achieved LRC had significantly higher immune temperatures compared to those with LRF (mean 51.66° vs. 34.77°, p = 0.004), with a classification accuracy of 0.71. Additionally, patients with immune temperatures above the median (42.3°) had significantly improved overall survival compared to those with lower values (p = 0.031). We found a similar correlation with survival outcomes in eleven different cancer types downloaded from TIMEDB, 11 although specific treatment information for those cases was unavailable. The immune temperature also correlated with tumor control (p = 0.012, week 3), with a high prediction accuracy (AUC=0.805), in an independent cervical cancer cohort treated with chemoradiotherapy.Conclusions The novel concept of a quantitative immune temperature to predict patient-specific treatment response and outcome demonstrated clinically actionable accuracy and is poised to help guide clinical decision-making towards improved precision medicine that harnesses an individual’s immune contexture.References Galon J, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 2006;313(5795):1960–1964.Galon J, Bruni D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nature Reviews Drug Discovery. 2019;18(3):197–218.Maleki Vareki S. High and low mutational burden tumors versus immunologically hot and cold tumors and response to immune checkpoint inhibitors. J Immunother Cancer. 2018;6(1):157.Wu B, et al. Cold and hot tumors: from molecular mechanisms to targeted therapy. Signal Transduction and Targeted Therapy. 2024;9(1):274.Genomic map of the cellular composition of the tumor microenvironment. Nature Immunology. 2024;25(10):1789–1790.Flemming A. Tumour heterogeneity determines immune response. Nature Reviews Immunology. 2019;19(11):662–663.Jia Q, et al. Heterogeneity of the tumor immune microenvironment and its clinical relevance. Exp Hematol Oncol. 2022;11(1):24.Yoshihara K, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nature Communications. 2013;4(1):2612.Newman AM, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7.Alfonso JCL, et al. Tumor-immune ecosystem dynamics define an individual radiation immune score to predict pan-cancer radiocurability. Neoplasia. 2021;23(11):1110–1122.Wang X, et al. TIMEDB: tumor immune micro-environment cell composition database with automatic analysis and interactive visualization. Nucleic Acids Res. 2023;51(D1):D1417-d1424.3c:Tb32,11018 Background: The conventional interpretation of superiority oncology trials relies on statistical significance defined by P values, which are frequently misinterpreted and oversimplified. On the other hand, Bayesian analysis, which incorporates prior knowledge, directly estimates the probability of a hypothesis, with greater flexibility to examine clinically relevant effect sizes. Here, we studied the effects of Bayesian models on phase III trial interpretation. We further hypothesized that Bayesian approaches, using differential priors for efficacy and futility, would improve trial efficiency. Methods: Phase III superiority-design, two-arm oncology trials were screened from ClinicalTrials.gov for this meta-epidemiological study. Individual patient-level data were manually reconstructed from the Kaplan-Meir curves of the primary endpoint. First, Bayesian Cox regressions of reconstructed data applied skeptical N (0, 0.355), enthusiastic N (-0.41, 0.4), and neutral priors N (0, 10 6 ), where N (mean, standard deviation) denotes a normal distribution, to estimate posterior probabilities of the primary endpoint effects with Markov chain Monte Carlo sampling. Minimum clinically important differences (MCID) in the experimental arm were defined by HR < 0.8 per ASCO criteria (Ellis et al, J Clin Oncol 2014). Second, a single event or enrollment-driven interim analysis with Bayesian stopping rules was simulated 100 times for each trial using in silico models of randomly varying accrual kinetics with published patient outcomes. Interim efficacy and futility were defined per simulation by probabilities ≥ 85% for achieving effect sizes larger than the MCID/3 using a skeptical or enthusiastic prior, respectively. Early trial closure was recommended if ≥ 75% of simulations met efficacy or futility criteria. Results: After screening,194,129 patient outcomes from 230 trials were reconstructed. Overall survival was the primary endpoint in 90 trials (39%). All trials interpreted as positive had > 90% probabilities of marginal benefits (HR < 1). However, 38% of trials interpreted as positive had ≤ 90% probabilities of achieving the MCID (HR < 0.8), even under an enthusiastic prior. Conversely, 24% of trials interpreted as negative had > 90% probability of achieving marginal benefits, even under a skeptical prior. In the interim analysis simulations, early closure was recommended for 82 trials (36%). Bayesian interim analysis was associated with >99% probability of reducing enrollment sizes. The trial and its simulated interim analysis remained concordant (Bayesian Cohen’s κ, 0.95). Conclusions: Bayesian models add unique interpretative value for clinically relevant effects, and may improve trial efficiency without compromising trial interpretation. Bayesian models should be increasingly incorporated in phase III trials.3d:T4f8,Table S3: List of 341 trials included in the overall analysis. ADT, androgen deprivation therapy; mFOLFOX, folinic acid, fluorouracil, and oxaliplatin; FOLFIRI, folinic acid, fluorouracil, irinotecan; GM-CSF, granulocyte-macrophage colony-stimulating factor; R-DHAP; rituximab, dexamethasone, cytarabine, cisplatin; O-DHAP; ofatumumab, dexamethasone, cytarabine, cisplatin; EDP, etoposide doxorubicin cisplatin; G-CHOP, obintuzumab, cyclophosphamide, doxorubicin hydrochloride, vincristine, prednisone; RCHOP, rituximab, cyclophosphamide, doxorubicin hydrochloride, vincristine, prednisone; VR-CAP, rituximab, cyclophosphamide, doxorubicin, and prednisone with bortezomib; AC, Adriamycin with cyclophosphamide; T, paclitaxel; XT, capecitabine plus paclitaxel; GIST, gastrointestinal stromal tumor; IFN, interferon; 5FU, 5-fluorouracil; FA, folinic acid; recMAGE-A3, recombinant MAGE A-3; NGR-Htnf, NGR–human tumor necrosis factor; TACE, trans-arterial chemoembolization; T-DM1, Trastuzumab emtansine; Mf, mitomycin-C and fluoropyrimidine; iceMFP, intraperitoneal cisplatin (100 mg), intravenous mitomycin-C (15 mg/m(2)) on postoperative day 1, followed by oral doxifluridine for 12 months, and six monthly intravenous cisplatin (60 mg/m(2); 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Akl","id":"openalex:A5041615321","orcid":"https://orcid.org/0000-0002-3444-8618","affiliation":"American University of Beirut","institutionId":"I98635879","institutionUrl":"https://ror.org/04pznsd21","affiliations":["Faculty of Medicine, American University of Beirut, Beirut, LBN","Faculty of Medicine, American Univeristy of Beirut, Beirut, LBN","Public Health, American University of Beirut, Beirut, LBN"]},{"name":"Khalil Baddour","id":"openalex:A5041715279","orcid":"https://orcid.org/0000-0002-4362-0782","affiliation":"American University of Beirut","institutionId":"I98635879","institutionUrl":"https://ror.org/04pznsd21","affiliations":["Faculty of Medicine, American University of Beirut, Beirut, LBN","Faculty of Medicine, American Univeristy of Beirut, Beirut, LBN","Public Health, American University of Beirut, Beirut, LBN"]}],"venue":"Cureus","venueId":"S2738950867","publisher":"Cureus, Inc.","publisherId":"P4310316779","year":2020,"publishedDate":"2020-03-13","type":"article","citationCount":899,"referenceCount":12,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.7759/cureus.7255","oaUrl":"https://assets.cureus.com/uploads/original_article/pdf/28976/1612429917-1612429913-20210204-18268-1u5b65r.pdf","pdfUrl":"https://assets.cureus.com/uploads/original_article/pdf/28976/1612429917-1612429913-20210204-18268-1u5b65r.pdf","topics":[{"id":"T11147","name":"Misinformation and Its Impacts","score":1,"subfieldId":"3312","subfield":"Sociology and Political Science","fieldId":"33","field":"Social Sciences"},{"id":"T13979","name":"Communication and COVID-19 Impact","score":0.00009999999747378752,"subfieldId":"3315","subfield":"Communication","fieldId":"33","field":"Social Sciences"},{"id":"T13607","name":"Academic Publishing and Open Access","score":0,"subfieldId":"1802","subfield":"Information Systems and Management","fieldId":"18","field":"Decision Sciences"}],"sources":["openalex"],"id":"openalex:W3011186656"},{"ids":{"openalex":"W4387783936","doi":"10.1016/j.ccell.2023.09.012","pmid":"37863066"},"title":"Tumor-resident Lactobacillus iners confer chemoradiation resistance through lactate-induced metabolic rewiring","abstract":"$22","authors":[{"name":"Lauren Elizabeth Colbert","id":"openalex:A5065934113","orcid":"https://orcid.org/0000-0001-5806-4339","affiliation":"The University of Texas MD Anderson Cancer Center","institutionId":"I1343551460","institutionUrl":"https://ror.org/04twxam07","affiliations":["Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 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