Exploring the Oral Manifestations of Tuberculosis: A Comprehensive Analysis of Prevalence and Clinicopathological Characteristics of Oral Lesions.

BACKGROUND: The study aimed to report all cases of oral tuberculosis (TB), a rare manifestation of the fatal infectious disease primarily affecting the pulmonary system. The report also evaluated the clinicopathological characteristics of oral TB lesions. METHODS: A total of 25 patients who presented with oral lesions between August 2013 and August 2023 were diagnosed with TB through surgical biopsy despite having no prior history of the disease. Their clinical symptoms, auxiliary examinations, treatments, and outcomes were recorded and analyzed for further study. RESULTS: In a study of 25 patients with oral TB, all patients were found to have the disease, with 16 males and 9 females affected. The gender distribution was skewed toward males, with a 1.77 male-to-female ratio. Twelve cases of the affected sites were reported in the mandible, six cases in the buccal mucosa, four in the lips, two in the gingiva, and one in the tongue. The age range of affected patients was 0-70 years old, and all lesions were indicative of primary TB. The appearance of the affected mucosa varied, with ulceration and swelling being the most common manifestations. CONCLUSION: Patients who present with oral ulcerations and swellings should be evaluated for the possibility of TB. To confirm and differentiate this condition from other diseases, obtaining a biopsy specimen for histological analysis and performing acid-fast stains and cultures is recommended. These tests will enable a precise diagnosis and guide appropriate treatment.

Automated tumor immunophenotyping predicts clinical benefit from anti-PD-L1 immunotherapy.

Cancer immunotherapy has transformed the clinical approach to patients with malignancies, as profound benefits can be seen in a subset of patients. To identify this subset, biomarker analyses increasingly focus on phenotypic and functional evaluation of the tumor microenvironment to determine if density, spatial distribution, and cellular composition of immune cell infiltrates can provide prognostic and/or predictive information. Attempts have been made to develop standardized methods to evaluate immune infiltrates in the routine assessment of certain tumor types; however, broad adoption of this approach in clinical decision-making is still missing. We developed approaches to categorize solid tumors into 'desert', 'excluded', and 'inflamed' types according to the spatial distribution of CD8+ immune effector cells to determine the prognostic and/or predictive implications of such labels. To overcome the limitations of this subjective approach, we incrementally developed four automated analysis pipelines of increasing granularity and complexity for density and pattern assessment of immune effector cells. We show that categorization based on 'manual' observation is predictive for clinical benefit from anti-programmed death ligand 1 therapy in two large cohorts of patients with non-small cell lung cancer or triple-negative breast cancer. For the automated analysis we demonstrate that a combined approach outperforms individual pipelines and successfully relates spatial features to pathologist-based readouts and the patient's response to therapy. Our findings suggest that tumor immunophenotype generated by automated analysis pipelines should be evaluated further as potential predictive biomarkers for cancer immunotherapy. © 2024 The Pathological Society of Great Britain and Ireland.

Liposomal bupivacaine intercostal block placed under direct vision reduces morphine use in thoracic surgery.

Background: Thoracic epidural analgesia (TEA) and liposomal bupivacaine (LB) are two methods used for postoperative pain control after thoracic surgery. Some studies have compared LB to standard bupivacaine. However, data comparing the outcomes of LB to TEA after minimally invasive lung resection is limited. Therefore, the objective of our study was to compare postoperative pain, opioid usage, and outcomes between patients who received TEA vs. LB. Methods: We conducted a retrospective chart review of patients who underwent minimally invasive lung resections over an 8-month period. Intraoperatively, patients received either LB under direct vision or a TEA. Pain scores were obtained in the post-anesthesia care unit (PACU) and at 12, 24, and 48 hours postoperatively. Morphine milligram equivalents (MMEs) were calculated at 24 and 48 hours postoperatively. Postoperative outcomes were then compared between groups. Results: In total, 391 patients underwent minimally invasive lung resection: 236 (60%) wedge resections, 51 (13%) segmentectomies, and 104 (27%) lobectomies. Of these, 326 (83%) received LB intraoperatively. Fewer patients in the LB group experienced postoperative complications (18% vs. 34%, P=0.004). LB patients also had lower median pain scores at 24 (P=0.03) and 48 hours (P=0.001) postoperatively. There was no difference in MMEs at 24 hours (P=0.49). However, at 48 hours, patients who received LB required less narcotics (P=0.02). Median hospital length of stay (LOS) was significantly shorter in patients who received LB (2 vs. 4 days, P<0.001). On multivariable analysis, increasing age, postoperative complications, and use of TEA were independently associated with a longer hospital LOS. Conclusions: Compared to TEA, LB intercostal block placed under direct vision reduced morphine use 48 hours after thoracic surgery. It was also associated with fewer postoperative complications and shorter median hospital LOS. LB is a good alternative to TEA for pain management after minimally invasive lung resection.

Immune heterogeneity in small-cell lung cancer and vulnerability to immune checkpoint blockade.

Atezolizumab (anti-PD-L1), combined with carboplatin and etoposide (CE), is now a standard of care for extensive-stage small-cell lung cancer (ES-SCLC). A clearer understanding of therapeutically relevant SCLC subsets could identify rational combination strategies and improve outcomes. We conduct transcriptomic analyses and non-negative matrix factorization on 271 pre-treatment patient tumor samples from IMpower133 and identify four subsets with general concordance to previously reported SCLC subtypes (SCLC-A, -N, -P, and -I). Deeper investigation into the immune heterogeneity uncovers two subsets with differing neuroendocrine (NE) versus non-neuroendocrine (non-NE) phenotypes, demonstrating immune cell infiltration hallmarks. The NE tumors with low tumor-associated macrophage (TAM) but high T-effector signals demonstrate longer overall survival with PD-L1 blockade and CE versus CE alone than non-NE tumors with high TAM and high T-effector signal. Our study offers a clinically relevant approach to discriminate SCLC patients likely benefitting most from immunotherapies and highlights the complex mechanisms underlying immunotherapy responses.

Anti-TIGIT antibody improves PD-L1 blockade through myeloid and Treg cells.

Tiragolumab, an anti-TIGIT antibody with an active IgG1κ Fc, demonstrated improved outcomes in the phase 2 CITYSCAPE trial (ClinicalTrials.gov: NCT03563716 ) when combined with atezolizumab (anti-PD-L1) versus atezolizumab alone1. However, there remains little consensus on the mechanism(s) of response with this combination2. Here we find that a high baseline of intratumoural macrophages and regulatory T cells is associated with better outcomes in patients treated with atezolizumab plus tiragolumab but not with atezolizumab alone. Serum sample analysis revealed that macrophage activation is associated with a clinical benefit in patients who received the combination treatment. In mouse tumour models, tiragolumab surrogate antibodies inflamed tumour-associated macrophages, monocytes and dendritic cells through Fcγ receptors (FcγR), in turn driving anti-tumour CD8+ T cells from an exhausted effector-like state to a more memory-like state. These results reveal a mechanism of action through which TIGIT checkpoint inhibitors can remodel immunosuppressive tumour microenvironments, and suggest that FcγR engagement is an important consideration in anti-TIGIT antibody development.

SKYSCRAPER-02: Tiragolumab in Combination With Atezolizumab Plus Chemotherapy in Untreated Extensive-Stage Small-Cell Lung Cancer.

PURPOSE: The phase III SKYSCRAPER-02 study determined whether the benefits of atezolizumab plus carboplatin and etoposide (CE) could be enhanced by the addition of tiragolumab in untreated extensive-stage small-cell lung cancer (ES-SCLC). We report final progression-free survival (PFS) and overall survival (OS) analyses. METHODS: Patients received tiragolumab 600 mg/placebo, plus atezolizumab 1,200 mg and CE (four cycles), then maintenance tiragolumab/placebo plus atezolizumab. Primary end points were investigator-assessed PFS and OS in patients without history/presence of brain metastases (primary analysis set [PAS]). Additional end points included PFS and OS in all patients regardless of brain metastases status (full analysis set [FAS]), response, and safety. RESULTS: Four hundred ninety patients were randomly assigned (FAS): 243 to tiragolumab arm and 247 to control arm. At the cutoff date (February 6, 2022; median duration of follow-up, 14.3 months [PAS] and 13.9 months [FAS]), final analysis of PFS in the PAS (n = 397) did not reach statistical significance (stratified hazard ratio [HR], 1.11; P = .3504; median, 5.4 months tiragolumab v 5.6 months control). At the cutoff date (September 6, 2022; median duration of follow-up, 21.2 months [FAS]), median OS in the PAS at final OS analysis was 13.1 months in both arms (stratified HR, 1.14; P = .2859). Median PFS and OS in the FAS were consistent with the PAS. The proportion of patients with immune-mediated adverse events (AEs) in the tiragolumab and control arms was 54.4% and 49.2%, respectively (grade 3/4: 7.9% and 7.7%). AEs leading to treatment withdrawal occurred in 8.4% and 9.3% of tiragolumab- and control-treated patients, respectively. CONCLUSION: Tiragolumab did not provide additional benefit over atezolizumab and CE in untreated ES-SCLC. The combination was well tolerated with no new safety signals.

Combined use of CYFRA 21-1 and CA 125 predicts survival of patients with metastatic NSCLC and stable disease in IMpower150.

BACKGROUND: Patients with non-small cell lung cancer (NSCLC) and stable disease (SD) have an unmet clinical need to help guide early treatment adjustments. OBJECTIVE: To evaluate the potential of tumor biomarkers to inform on survival outcomes in NSCLC SD patients. METHODS: This post hoc analysis included 480 patients from the IMpower150 study with metastatic NSCLC, treated with chemotherapy, atezolizumab and bevacizumab combinations, who had SD at first CT scan (post-treatment initiation). Patients were stratified into high- and low-risk groups (overall survival [OS] and progression-free survival [PFS] outcomes) based on serum tumor biomarker levels. RESULTS: The CYFRA 21-1 and CA 125 biomarker combination predicted OS and PFS in patients with SD. Risk of death was ~4-fold higher for the biomarker-stratified high-risk versus low-risk SD patients (hazard ratio [HR] 3.80; 95% confidence interval [CI] 3.02-4.78; p < 0.0001). OS in patients with the low- and high-risk SD was comparable to that in patients with the CT-defined partial response (PR; HR 1.10; 95% CI 0.898-1.34) and progressive disease (PD) (HR 1.05; 95% CI 0.621-1.77), respectively. The findings were similar with PFS, and consistent across treatment arms. CONCLUSIONS: Biomarker testing shows potential for providing prognostic information to help direct treatment in NSCLC patients with SD. Prospective clinical studies are warranted.ClinicalTrials.gov: NCT02366143.

Anti-TIGIT Antibody Tiragolumab Alone or With Atezolizumab in Patients With Advanced Solid Tumors: A Phase 1a/1b Nonrandomized Controlled Trial.

Importance: Inhibition of the T-cell immunoreceptor with Ig and ITIM domains (TIGIT)/poliovirus receptor pathway may amplify the antitumor immune response of atezolizumab in programmed death ligand 1-selected tumors. Objective: To evaluate the safety and antitumor activity of the anti-TIGIT antibody tiragolumab and its combination with atezolizumab in patients with advanced solid tumors. Design, Setting, and Participants: The GO30103 open-label, first-in-human phase 1a/1b dose-escalation and dose-expansion nonrandomized controlled trial was conducted at 13 sites in 6 countries (Australia, Canada, France, Korea, Spain, and the US). The start dates were May 23, 2016, for phase 1a and October 11, 2016, for phase 1b. Patients were aged 18 years or older with measurable disease at baseline. The clinical cutoff date was October 1, 2021. Data analysis was performed on January 24, 2022. Interventions: Patients received fixed-dose intravenous tiragolumab on day 1 of each 21-day cycle (2 mg escalating to 1200 mg) in phase 1a, plus fixed-dose intravenous atezolizumab (1200 mg every 3 weeks) in phase 1b. Patients were treated until disease progression, loss of clinical benefit, or development of unacceptable toxicity. Main Outcomes and Measures: The primary end points included the safety, tolerability, and recommended phase 2 dose (RP2D) of tiragolumab or combination tiragolumab plus atezolizumab. The secondary end point included the investigator-assessed objective response rate (ORR). Counts and percentages are used for categorical variables, and medians and ranges are used for continuous variables. Results: Among the phase 1a (n = 24) and 1b (n = 49) dose-escalation cohorts, the median age was 60 (range, 40-77) and 54 (range, 25-81) years, respectively. More than half of patients were women (14 of 24 [58%] and 25 of 49 [51%]), and more than a third (10 [42%] and 18 [37%]) had received 4 or more prior cancer therapies. No dose-limiting toxicities occurred, and the maximum tolerated dose of tiragolumab was not reached (NR). The most frequent treatment-related adverse events (AEs) were fatigue (5 of 24 [21%]) in phase 1a and pruritus (5 of 49 [10%]) in phase 1b; the majority of AEs were grade 1 or 2. Immune-mediated AEs occurred in 4 of 24 (17%) and 29 of 49 (59%) patients during phases 1a and 1b, respectively (primarily grade 1 or 2). The RP2D of tiragolumab was 600 mg intravenously every 3 weeks, which was tested in phase 1b dose expansion. The confirmed ORR was 0% during phase 1a, with evidence of antitumor activity in 6% of patients (n = 3) during phase 1b. The safety profile of combination tiragolumab plus atezolizumab in phase 1b was similar in the dose-escalation and dose-expansion cohorts. The confirmed ORR was 46% (6 of 13) in the non-small cell lung cancer (NSCLC) cohort (median duration of response [DOR], NR) and 28% (5 of 18) in the esophageal cancer (EC) cohort (median DOR, 15.2 [95% CI, 7.0 to NR] months). Conclusions and Relevance: In this nonrandomized controlled trial, tiragolumab was well tolerated with or without atezolizumab; no new safety signals were observed. Preliminary antitumor activity was demonstrated for the combination regimen in patients with cancer immunotherapy-naive metastatic NSCLC or EC. Trial Registration: ClinicalTrials.gov Identifier: NCT02794571.

Predicting COVID-19 severity: Challenges in reproducibility and deployment of machine learning methods.

The increasing use of electronic health records (EHR) based computable phenotypes in clinical research is providing new opportunities for development of data-driven medical applications. Adopted widely in the United States and globally, EHRs facilitate systematic collection of patients' longitudinal information, which serves as one of the important foundations for artificial intelligence applications in medicine. Harmonization of input variables and outcome definitions is critically important for wider clinical applicability of artificial intelligence (AI) methodologies. In this review, we focused on Coronavirus Disease 2019 (COVID-19) severity machine learning prediction models and explored the pipeline for standardizing future disease severity model development using EHR information. We identified 2,967 studies published between 01/01/2020 and 02/15/2022 and selected 135 independent studies that had built machine learning prediction models to predict severity related outcomes of COVID-19 patients based on EHR data for the final review. These 135 studies spanning across 27 counties covered a broad range of severity related prediction outcomes. We observed substantial inconsistency in COVID-19 severity phenotype definitions among models in these studies. Moreover, there was a gap between the outcome of these models and clinician-recognized clinical concepts. Accordingly, we recommend that robust clinical input metrics, with outcome definitions which eliminate ambiguity in interpretation, to reduce algorithmic bias, mitigate model brittleness and improve generalizability of a universal model for COVID-19 severity. This framework can potentially be extended to broader clinical application.

A longitudinal circulating tumor DNA-based model associated with survival in metastatic non-small-cell lung cancer.

One of the great challenges in therapeutic oncology is determining who might achieve survival benefits from a particular therapy. Studies on longitudinal circulating tumor DNA (ctDNA) dynamics for the prediction of survival have generally been small or nonrandomized. We assessed ctDNA across 5 time points in 466 non-small-cell lung cancer (NSCLC) patients from the randomized phase 3 IMpower150 study comparing chemotherapy-immune checkpoint inhibitor (chemo-ICI) combinations and used machine learning to jointly model multiple ctDNA metrics to predict overall survival (OS). ctDNA assessments through cycle 3 day 1 of treatment enabled risk stratification of patients with stable disease (hazard ratio (HR) = 3.2 (2.0-5.3), P < 0.001; median 7.1 versus 22.3 months for high- versus low-intermediate risk) and with partial response (HR = 3.3 (1.7-6.4), P < 0.001; median 8.8 versus 28.6 months). The model also identified high-risk patients in an external validation cohort from the randomized phase 3 OAK study of ICI versus chemo in NSCLC (OS HR = 3.73 (1.83-7.60), P = 0.00012). Simulations of clinical trial scenarios employing our ctDNA model suggested that early ctDNA testing outperforms early radiographic imaging for predicting trial outcomes. Overall, measuring ctDNA dynamics during treatment can improve patient risk stratification and may allow early differentiation between competing therapies during clinical trials.

Predicting hospitalization of COVID-19 positive patients using clinician-guided machine learning methods.

OBJECTIVES: The coronavirus disease 2019 (COVID-19) is a resource-intensive global pandemic. It is important for healthcare systems to identify high-risk COVID-19-positive patients who need timely health care. This study was conducted to predict the hospitalization of older adults who have tested positive for COVID-19. METHODS: We screened all patients with COVID test records from 11 Mass General Brigham hospitals to identify the study population. A total of 1495 patients with age 65 and above from the outpatient setting were included in the final cohort, among which 459 patients were hospitalized. We conducted a clinician-guided, 3-stage feature selection, and phenotyping process using iterative combinations of literature review, clinician expert opinion, and electronic healthcare record data exploration. A list of 44 features, including temporal features, was generated from this process and used for model training. Four machine learning prediction models were developed, including regularized logistic regression, support vector machine, random forest, and neural network. RESULTS: All 4 models achieved area under the receiver operating characteristic curve (AUC) greater than 0.80. Random forest achieved the best predictive performance (AUC = 0.83). Albumin, an index for nutritional status, was found to have the strongest association with hospitalization among COVID positive older adults. CONCLUSIONS: In this study, we developed 4 machine learning models for predicting general hospitalization among COVID positive older adults. We identified important clinical factors associated with hospitalization and observed temporal patterns in our study cohort. Our modeling pipeline and algorithm could potentially be used to facilitate more accurate and efficient decision support for triaging COVID positive patients.

Tiragolumab plus atezolizumab versus placebo plus atezolizumab as a first-line treatment for PD-L1-selected non-small-cell lung cancer (CITYSCAPE): primary and follow-up analyses of a randomised, double-blind, phase 2 study.

BACKGROUND: Targeted inhibition of the PD-L1-PD-1 pathway might be further amplified through combination of PD-1 or PD-L1 inhibitors with novel anti-TIGIT inhibitory immune checkpoint agents, such as tiragolumab. In the CITYSCAPE trial, we aimed to assess the preliminary efficacy and safety of tiragolumab plus atezolizumab (anti-PD-L1) therapy as first-line treatment for non-small-cell lung cancer (NSCLC). METHODS: CITYSCAPE is a phase 2, randomised, double-blind, placebo-controlled trial. Patients with chemotherapy-naive, PD-L1-positive (defined as a tumour proportion score of ≥1% by 22C3 immunohistochemistry pharmDx assay; Dako, Agilent Technologies, Santa Clara, CA, USA) recurrent or metastatic NSCLC with measurable disease, Eastern Cooperative Oncology Group performance status of 0 or 1, and no EGFR or ALK alterations were enrolled from 41 clinics in Europe, Asia, and the USA. Patients were randomly assigned (1:1), via an interactive voice or web-based response system, to receive tiragolumab (600 mg) plus atezolizumab (1200 mg) or placebo plus atezolizumab intravenously once every 3 weeks. Investigators and patients were masked to treatment assignment. The co-primary endpoints were investigator-assessed objective response rate and progression-free survival as per Response Evaluation Criteria in Solid Tumors version 1.1 in the intention-to-treat population, analysed after approximately 80 progression-free survival events had been observed in the primary population. Safety was assessed in all patients who received at least one dose of study drug. This trial is registered with ClinicalTrials.gov, NCT03563716, and is ongoing. FINDINGS: Patients were enrolled between Aug 10, 2018, and March 20, 2019. At data cutoff for the primary analysis (June 30, 2019), 135 of 275 patients assessed for eligibility were randomly assigned to receive tiragolumab plus atezolizumab (67 [50%]) or placebo plus atezolizumab (68 [50%]). In this primary analysis, after a median follow-up of 5·9 months (4·6-7·6, in the intention-to-treat population, 21 patients (31·3% [95% CI 19·5-43·2]) in the tiragolumab plus atezolizumab group versus 11 patients (16·2% [6·7-25·7]) in the placebo plus atezolizumab group had an objective response (p=0·031). Median progression-free survival was 5·4 months (95% CI 4·2-not estimable) in the tiragolumab plus atezolizumab group versus 3·6 months (2·7-4·4) in the placebo plus atezolizumab group (stratified hazard ratio 0·57 [95% CI 0·37-0·90], p=0·015). 14 (21%) patients receiving tiragolumab plus atezolizumab and 12 (18%) patients receiving placebo plus atezolizumab had serious treatment-related adverse events. The most frequently reported grade 3 or worse treatment-related adverse event was lipase increase (in six [9%] patients in the tiragolumab plus atezolizumab group vs two [3%] in the placebo plus atezolizumab group). Two treatment-related deaths (of pyrexia and infection) occurred in the tiragolumab plus atezolizumab group. INTERPRETATION: Tiragolumab plus atezolizumab showed a clinically meaningful improvement in objective response rate and progression-free survival compared with placebo plus atezolizumab in patients with chemotherapy-naive, PD-L1-positive, recurrent or metastatic NSCLC. Tiragolumab plus atezolizumab was well tolerated, with a safety profile generally similar to that of atezolizumab alone. These findings demonstrate that tiragolumab plus atezolizumab is a promising immunotherapy combination for the treatment of previously untreated, locally advanced unresectable or metastatic NSCLC. FUNDING: F Hoffmann-La Roche and Genentech.

Intratumoral plasma cells predict outcomes to PD-L1 blockade in non-small cell lung cancer.

Inhibitors of the programmed cell death-1 (PD-1/PD-L1) signaling axis are approved to treat non-small cell lung cancer (NSCLC) patients, based on their significant overall survival (OS) benefit. Using transcriptomic analysis of 891 NSCLC tumors from patients treated with either the PD-L1 inhibitor atezolizumab or chemotherapy from two large randomized clinical trials, we find a significant B cell association with extended OS with PD-L1 blockade, independent of CD8+ T cell signals. We then derive gene signatures corresponding to the dominant B cell subsets present in NSCLC from single-cell RNA sequencing (RNA-seq) data. Importantly, we find increased plasma cell signatures to be predictive of OS in patients treated with atezolizumab, but not chemotherapy. B and plasma cells are also associated with the presence of tertiary lymphoid structures and organized lymphoid aggregates. Our results suggest an important contribution of B and plasma cells to the efficacy of PD-L1 blockade in NSCLC.

Both T cell priming in lymph node and CXCR3-dependent migration are the key events for predicting the response of atezolizumab.

Anti-PD-L1 antibodies benefit many cancer patients, even those with "non-inflamed tumor". Determining which patients will benefit remains an important clinical goal. In a non-inflamed tumor mouse model, we found that PD-L1 was highly expressed on antigen-presenting cells (APCs) especially on CD103+ CD11c+ dendritic cells in tumor-draining lymph nodes (dLNs), suppressing T-cell priming by APCs. In this model, anti-PD-L1 antibodies enhanced T-cell priming and increased CXCR3+ activated T-cells in dLNs, which was followed by the trafficking of T-cells to tumors in response to CXCR3 ligands. As predictive biomarker, each APCs-related gene expression (AP score) alone or T-cells trafficking-related chemokine gene expression (T score) alone were still less than perfect among the 17 mouse models examined. However a combining score of AP score and T score (AP/T score) precisely identified anti-PD-L1-sensitive tumors. In the phase 3 trial of atezolizumab vs docetaxel in advanced NSCLC patients (OAK), the AP/T score could identify atezolizumab-treated NSCLC patients who achieved significant improvement in overall survival. This biomarker concept would be a clinically valuable for prediction of anti-PD-L1 antibody efficacy.

Molecular determinants of response to PD-L1 blockade across tumor types.

Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis lead to durable clinical responses in subsets of cancer patients across multiple indications, including non-small cell lung cancer (NSCLC), urothelial carcinoma (UC) and renal cell carcinoma (RCC). Herein, we complement PD-L1 immunohistochemistry (IHC) and tumor mutation burden (TMB) with RNA-seq in 366 patients to identify unifying and indication-specific molecular profiles that can predict response to checkpoint blockade across these tumor types. Multiple machine learning approaches failed to identify a baseline transcriptional signature highly predictive of response across these indications. Signatures described previously for immune checkpoint inhibitors also failed to validate. At the pathway level, significant heterogeneity is observed between indications, in particular within the PD-L1+ tumors. mUC and NSCLC are molecularly aligned, with cell cycle and DNA damage repair genes associated with response in PD-L1- tumors. At the gene level, the CDK4/6 inhibitor CDKN2A is identified as a significant transcriptional correlate of response, highlighting the association of non-immune pathways to the outcome of checkpoint blockade. This cross-indication analysis reveals molecular heterogeneity between mUC, NSCLC and RCC tumors, suggesting that indication-specific molecular approaches should be prioritized to formulate treatment strategies.

Intratumoral CD103+ CD8+ T cells predict response to PD-L1 blockade.

BACKGROUND: CD8+ tissue-resident memory T (TRM) cells, marked by CD103 (ITGAE) expression, are thought to actively suppress cancer progression, leading to the hypothesis that their presence in tumors may predict response to immunotherapy. METHODS: Here, we test this by combining high-dimensional single-cell modalities with bulk tumor transcriptomics from 1868 patients enrolled in lung and bladder cancer clinical trials of atezolizumab (anti-programmed cell death ligand 1 (PD-L1)). RESULTS: ITGAE was identified as the most significantly upregulated gene in inflamed tumors. Tumor CD103+ CD8+ TRM cells exhibited a complex phenotype defined by the expression of checkpoint regulators, cytotoxic proteins, and increased clonal expansion. CONCLUSIONS: Our analyses indeed demonstrate that the presence of CD103+ CD8+ TRM cells, quantified by tracking intratumoral CD103 expression, can predict treatment outcome, suggesting that patients who respond to PD-1/PD-L1 blockade are those who exhibit an ongoing antitumor T-cell response.

The Influence of Geography, Religion, Religiosity and Institutional Factors on Worldwide End-of-Life Care for the Critically Ill: The WELPICUS Study.

PURPOSE: To evaluate the association between provider religion and religiosity and consensus about end-of-life care and explore if geographical and institutional factors contribute to variability in practice. MATERIALS AND METHODS: Using a modified Delphi method 22 end-of-life issues consisting of 35 definitions and 46 statements were evaluated in 32 countries in North America, South America, Eastern Europe, Western Europe, Asia, Australia and South Africa. A multidisciplinary, expert group from specialties treating patients at the end-of-life within each participating institution assessed the association between 7 key statements and geography, religion, religiosity and institutional factors likely influencing the development of consensus. RESULTS: Of 3049 participants, 1366 (45%) responded. Mean age of respondents was 45 ± 9 years and 55% were females. Following 2 Delphi rounds, consensus was obtained for 77 (95%) of 81 definitions and statements. There was a significant difference in responses across geographical regions. South African and North American respondents were more likely to encourage patients to write advance directives. Fewer Eastern European and Asian respondents agreed with withdrawing life-sustaining treatments without consent of patients or surrogates. While respondent's religion, years in practice or institution did not affect their agreement, religiosity, physician specialty and responsibility for end-of-life decisions did. CONCLUSIONS: Variability in agreement with key consensus statements about end-of-life care is related primarily to differences among providers, with provider-level variations related to differences in religiosity and specialty. Geography also plays a role in influencing some end-of-life practices. This information may help understanding ethical dilemmas and developing culturally sensitive end-of-life care strategies.

C-reactive protein reduction post treatment is associated with improved survival in atezolizumab (anti-PD-L1) treated non-small cell lung cancer patients.

PURPOSE: Overall survival (OS) is the most significant endpoint for evaluation of treatment benefit with checkpoint inhibitors (CPI) in cancer. We evaluated serum C-reactive protein (CRP) in non-small cell lung cancer (NSCLC) trials with atezolizumab (anti-PD-L1) as an early OS surrogate. METHODS: Serum from patients enrolled in randomized Phase II (n = 240) and Phase III (n = 701) trials of NSCLC patients (POPLAR, OAK) who progressed on prior-platinum chemotherapy, were analyzed for CRP levels over time. Patients were grouped by changes in CRP levels post-treatment as either increased (≥ 1.5 fold), decreased (≤ 1.5 fold) or unchanged (within +1.5 fold) relative to pre-treatment levels to assess association with progression free survival (PFS) and OS. RESULTS: Decrease in serum CRP levels at 6 weeks relative to pre-treatment were observed in patients with RECIST1.1 based complete or partial responses (CR/PR) to atezolizumab whereas patients with disease progression (PD) demonstrated an increase in CRP levels in the Phase II POPLAR study, and confirmed in the Phase III OAK study. Decrease in serum CRP as early as six weeks post treatment predicted improved PFS and OS, even in patients who were determined as stable disease (SD) in their first scan. This effect was not observed in the chemotherapy arms. CONCLUSION: Modulation of serum CRP correlates with clinical outcome post-atezolizumab treatment. This routine lab test may provide utility in informing OS signals as early as 6 weeks post-initiation of therapy with CPIs in NSCLC.

ctDNA Predicts Overall Survival in Patients With NSCLC Treated With PD-L1 Blockade or With Chemotherapy.

PURPOSE: Identification of predictors for overall survival (OS) allows timely detection of clinical efficacy signals and therefore facilitates treatment decisions. We assessed the association between circulating tumor DNA (ctDNA) metrics and the primary end point of OS in a subset of previously treated patients with locally advanced or metastatic non-small-cell lung cancer, who underwent atezolizumab or docetaxel treatment in the open-label randomized phase III OAK trial. MATERIALS AND METHODS: Plasma from 94 patients at baseline and at subsequent cycles of therapy every 3 weeks was analyzed retrospectively for ctDNA. ctDNA was measured by allele frequency and mutant molecules per milliliter (MMPM). Concordance between various per-sample metrics and clinical outcome were assessed using C index. RESULTS: Of all the ctDNA metrics tested, the association of median MMPM at 6 weeks with OS in patients treated with atezolizumab or docetaxel had a C index > 0.7. The OS hazard ratios relative to high ctDNA above median MMPM within each arm were 0.28 (95% CI, 0.11 to 0.75) for atezolizumab and 0.19 (95% CI, 0.08 to 0.48) for docetaxel. For patients who had ctDNA median MMPM levels of < 4.79, the median survival time was more than 17 months in docetaxel-treated patients and the median survival time was not reached in the atezolizumab-treated patients. CONCLUSION: ctDNA MMPM levels measured at 6 weeks post-treatment are associated with OS in advanced non-small-cell lung cancer. Our results suggest that ctDNA has the potential for a noninvasive early liquid biopsy predictor for OS that warrants further studies to demonstrate its utility in clinical development.