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.

Identifying factors associated with falls in postmenopausal breast cancer survivors: a multi-disciplinary approach.

OBJECTIVE: To identify neuromuscular, balance, and vision factors that contribute to falls in recently treated breast cancer survivors (BCS) and explore links between fall risk factors and cancer treatment. DESIGN: Case-control plus prospective observation. SETTING: Comprehensive cancer center. PARTICIPANTS: BCS (N=59; mean age, 58y) within 2 years of chemotherapy completion and/or on adjuvant endocrine therapy. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Objective measures of postural control, vision, and neuromuscular function included: (1) a sensory organization test (SOT), (2) a visual assessment battery, (3) muscle mass by dual energy x-ray absorptiometry, and (4) neuromuscular function with strength by repetition maximum, power by timed stair climb, and gait speed by 4m walk. Falls were self-reported for the past year (retrospective) and monthly for 6 months (prospective). RESULTS: Fifty eight percent of BCS reported falls in the past year. BCS with a history of falls had lower SOT scores with a vestibular deficit pattern in postural control (P<.01) and took longer to read letters on the contrast sensitivity chart (P<.05). Vestibular score on the SOT mediated the relationship between treatment and falls among BCS who received chemotherapy only, but not adjuvant endocrine therapy. CONCLUSIONS: Results of this project suggest that balance disturbances of vestibular origin and delays in detecting low contrast visual stimuli are associated with falls in BCS. Future studies that track falls and fall risk factors in BCS from diagnosis through treatment are warranted, as are studies that can identify treatment-related vestibular dysfunction and altered visual processing.