PURPOSE: To provide evidence-based recommendations to health care providers on integrative approaches to managing anxiety and depression symptoms in adults living with cancer. METHODS: The Society for Integrative Oncology and ASCO convened an expert panel of integrative oncology, medical oncology, radiation oncology, surgical oncology, palliative oncology, social sciences, mind-body medicine, nursing, methodology, and patient advocacy representatives. The literature search included systematic reviews, meta-analyses, and randomized controlled trials published from 1990 through 2023. Outcomes of interest included anxiety or depression symptoms as measured by validated psychometric tools, and adverse events. Expert panel members used this evidence and informal consensus with the Guidelines into Decision Support methodology to develop evidence-based guideline recommendations. RESULTS: The literature search identified 110 relevant studies (30 systematic reviews and 80 randomized controlled trials) to inform the evidence base for this guideline. RECOMMENDATIONS: Recommendations were made for mindfulness-based interventions (MBIs), yoga, relaxation, music therapy, reflexology, and aromatherapy (using inhalation) for treating symptoms of anxiety during active treatment; and MBIs, yoga, acupuncture, tai chi and/or qigong, and reflexology for treating anxiety symptoms after cancer treatment. For depression symptoms, MBIs, yoga, music therapy, relaxation, and reflexology were recommended during treatment, and MBIs, yoga, and tai chi and/or qigong were recommended post-treatment. DISCUSSION: Issues of patient-health care provider communication, health disparities, comorbid medical conditions, cost implications, guideline implementation, provider training and credentialing, and quality assurance of natural health products are discussed. While several approaches such as MBIs and yoga appear effective, limitations of the evidence base including assessment of risk of bias, nonstandardization of therapies, lack of diversity in study samples, and lack of active control conditions as well as future research directions are discussed. Additional information is available at www.asco.org/survivorship-guidelines .

Patients with locally advanced head and neck squamous cell carcinoma (HNSCC) have a poor prognosis, with a significant risk of progression or death despite multimodal treatment with surgery, chemotherapy, and radiotherapy. Immune checkpoint inhibitors targeting the programmed death receptor-1 (PD1) have dramatically changed the treatment landscape for recurrent/metastatic disease, improving overall survival in both the first- and second-line palliative settings. This success has driven the investigation of treatment strategies incorporating immunotherapy earlier into the multimodal curative-intent or salvage treatment of both locally advanced and recurrent/metastatic HNSCC. This review encompassed the following three subjects, with a focus on recently reported and ongoing clinical trials: (1) the use of neoadjuvant immunotherapy prior to surgery for locally advanced HNSCC, (2) the use of immunochemoradiotherapy for locally advanced head and neck cancers, and (3) novel uses of immunotherapy in the salvage of recurrent/metastatic HNSCC via a combined modality, including reirradiation paradigms. The results of these studies are eagerly awaited to improve patient outcomes in this challenging disease.

Background: Approximately 85% of the U.S. military active duty population is male and less than 50 years of age, with elevated levels of known risk factors for oropharyngeal squamous cell carcinoma (OPSCC), including smoking, excessive use of alcohol, and greater numbers of sexual partners, and elevated prevalence of human papilloma virus (HPV). Given the recent rise in incidence of OPSCC related to the HPV, the Department of Defense Serum Repository provides an unparalleled resource for longitudinal studies of OPSCC in the military for the identification of early detection biomarkers.Methods: We identified 175 patients diagnosed with OPSCC with 175 matched healthy controls and retrieved a total of 978 serum samples drawn at the time of diagnosis, 2 and 4 years prior to diagnosis, and 2 years after diagnosis. Following immunoaffinity depletion, serum samples were analyzed by targeted proteomics assays for multiplexed quantification of a panel of 146 candidate protein biomarkers from the curated literature.Results: Using a Random Forest machine learning approach, we derived a 13-protein signature that distinguishes cases versus controls based on longitudinal changes in serum protein concentration. The abundances of each of the 13 proteins remain constant over time in control subjects. The AUC for the derived Random Forest classifier was 0.90.Conclusions: This 13-protein classifier is highly promising for detection of OPSCC prior to overt symptoms.Impact: Use of longitudinal samples has significant potential to identify biomarkers for detection and risk stratification.