Brief Research Report: Anti-SARS-CoV-2 Immunity in Long Lasting Responders to Cancer Immunotherapy Through mRNA-Based COVID-19 Vaccination.

Cancer patients (CPs) have been identified as particularly vulnerable to SARS-CoV-2 infection, and therefore are a priority group for receiving COVID-19 vaccination. From the patients with advanced solid tumors, about 20% respond very efficiently to immunotherapy with anti-PD1/PD-L1 antibodies and achieve long lasting cancer responses. It is unclear whether an efficient cancer-specific immune response may also correlate with an efficient response upon COVID-19 vaccination. Here, we explored the antiviral immune response to the mRNA-based COVID-19 vaccine BNT162b2 in a group of 11 long-lasting cancer immunotherapy responders. We analysed the development of SARS-CoV-2-specific IgG serum antibodies, virus neutralizing capacities and T cell responses. Control groups included patients treated with adjuvant cancer immunotherapy (IMT, cohort B), CPs not treated with immunotherapy (no-IMT, cohort C) and healthy controls (cohort A). The median ELISA IgG titers significantly increased after the prime-boost COVID vaccine regimen in all cohorts (Cohort A: pre-vaccine = 900 (100-2700), 3 weeks (w) post-boost = 24300 (2700-72900); Cohort B: pre-vaccine = 300 (100-2700), 3 w post-boost = 8100 (300-72900); Cohort C: pre-vaccine = 500 (100-2700), 3 w post-boost = 24300 (300-72900)). However, at the 3 w post-prime time-point, only the healthy control group showed a statistically significant increase in antibody levels (Cohort A = 8100 (900-8100); Cohort B = 900 (300-8100); Cohort C = 900 (300-8100)) (P < 0.05). Strikingly, while all healthy controls generated high-level antibody responses after the complete prime-boost regimen (Cohort A = 15/15 (100%), not all CPs behaved alike [Cohort B= 12/14 (84'6%); Cohort C= 5/6 (83%)]. Their responses, including those of the long-lasting immunotherapy responders, were more variable (Cohort A: 3 w post-boost (median nAb titers = 95.32 (84.09-96.93), median Spike-specific IFN-γ response = 64 (24-150); Cohort B: 3 w post-boost (median nAb titers = 85.62 (8.22-97.19), median Spike-specific IFN-γ response (28 (1-372); Cohort C: 3 w post-boost (median nAb titers = 95.87 (11.8-97.3), median Spike-specific IFN-γ response = 67 (20-84)). Two long-lasting cancer responders did not respond properly to the prime-boost vaccination and did not generate S-specific IgGs, neutralizing antibodies or virus-specific T cells, although their cancer immune control persisted for years. Thus, although mRNA-based vaccines can induce both antibody and T cell responses in CPs, the immune response to COVID vaccination is independent of the capacity to develop an efficient anti-cancer immune response to anti PD-1/PD-L1 antibodies.

Brief Research Report: Anti-SARS-CoV-2 Immunity in Long Lasting Responders to Cancer Immunotherapy Through mRNA-Based COVID-19 Vaccination

Cancer patients (CPs) have been identified as particularly vulnerable to SARS-CoV-2 infection, and therefore are a priority group for receiving COVID-19 vaccination. From the patients with advanced solid tumors, about 20% respond very efficiently to immunotherapy with anti-PD1/PD-L1 antibodies and achieve long lasting cancer responses. It is unclear whether an efficient cancer-specific immune response may also correlate with an efficient response upon COVID-19 vaccination. Here, we explored the antiviral immune response to the mRNA-based COVID-19 vaccine BNT162b2 in a group of 11 long-lasting cancer immunotherapy responders. We analysed the development of SARS-CoV-2-specific IgG serum antibodies, virus neutralizing capacities and T cell responses. Control groups included patients treated with adjuvant cancer immunotherapy (IMT, cohort B), CPs not treated with immunotherapy (no-IMT, cohort C) and healthy controls (cohort A). The median ELISA IgG titers significantly increased after the prime-boost COVID vaccine regimen in all cohorts (Cohort A: pre-vaccine = 900 (100-2700), 3 weeks (w) post-boost = 24300 (2700-72900);Cohort B: pre-vaccine = 300 (100-2700), 3 w post-boost = 8100 (300-72900);Cohort C: pre-vaccine = 500 (100-2700), 3 w post-boost = 24300 (300-72900)). However, at the 3 w post-prime time-point, only the healthy control group showed a statistically significant increase in antibody levels (Cohort A = 8100 (900-8100);Cohort B = 900 (300-8100);Cohort C = 900 (300-8100)) (P < 0.05). Strikingly, while all healthy controls generated high-level antibody responses after the complete prime-boost regimen (Cohort A = 15/15 (100%), not all CPs behaved alike [Cohort B= 12/14 (84'6%);Cohort C= 5/6 (83%)]. Their responses, including those of the long-lasting immunotherapy responders, were more variable (Cohort A: 3 w post-boost (median nAb titers = 95.32 (84.09-96.93), median Spike-specific IFN-γ response = 64 (24-150);Cohort B: 3 w post-boost (median nAb titers = 85.62 (8.22-97.19), median Spike-specific IFN-γ response (28 (1-372);Cohort C: 3 w post-boost (median nAb titers = 95.87 (11.8-97.3), median Spike-specific IFN-γ response = 67 (20-84)). Two long-lasting cancer responders did not respond properly to the prime-boost vaccination and did not generate S-specific IgGs, neutralizing antibodies or virus-specific T cells, although their cancer immune control persisted for years. Thus, although mRNA-based vaccines can induce both antibody and T cell responses in CPs, the immune response to COVID vaccination is independent of the capacity to develop an efficient anti-cancer immune response to anti PD-1/PD-L1 antibodies.

A narrative review of lung cancer cytology in the times of coronavirus: what physicians should know.

In the modern era of personalized and precision medicine, lung cancer management needs to be carried out in a multidisciplinary manner. Among other disciplines, also cytopathology is key in diagnosis and treatment management of these patients. Indeed, cytopathology specimens are often the only source of available tissue material for morphological diagnosis and molecular purposes in order to guarantee an adequate treatment decision making, since surgical resection specimens are not available when lung cancer is diagnosed at advanced disease stages. Today, as an effect of the current severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) pandemic, cytopathology is reorganizing and reshaping many of its procedures and workflows, in order to ensure the safety of cytopathologists and laboratory personnel. In particular, careful attention should be paid on biosafety procedures when pulmonary cytological specimens are handled. In addition, also molecular cytopathology, that provides relevant information on the molecular status and on the potential sensitivity to target treatments, is undergoing major changes. In this setting, fully automated technologies, requiring minimal hands-on work, may be a valid option. The aim of this narrative review is to keep updated all the different professional figures involved in lung cancer management and treatment on how SARS-CoV-2 is modifying lung cancer cytopathology.

Predictive molecular pathology in the time of coronavirus disease (COVID-19) in Europe.

AIMS: Lung cancer predictive biomarker testing is essential to select advanced-stage patients for targeted treatments and should be carried out without delays even during health emergencies, such as the coronavirus (COVID-19) outbreak. METHODS: Fifteen molecular laboratories from seven different European countries compared 4 weeks of national lockdown to a corresponding period in 2019, in terms of tissue and/or plasma-based molecular test workload, analytical platforms adopted, number of cases undergoing programmed death-ligand1 (PD-L1) expression assessment and DNA-based molecular tests turnaround time. RESULTS: In most laboratories (80.0%), tissue-based molecular test workload was reduced. In 40.0% of laboratories (6/15), the decrease was >25%, and in one, reduction was as high as 80.0%. In this instance, a concomitant increase in liquid biopsy was reported (60.0%). Remarkably, in 33.3% of the laboratories, real-time PCR (RT-PCR)-based methodologies increased, whereas highly multiplexing assays approaches decreased. Most laboratories (88.9%) did not report significant variations in PD-L1 volume testing. CONCLUSIONS: The workload of molecular testing for patients with advanced-stage lung cancer during the lockdown showed little variations. Local strategies to overcome health emergency-related issues included the preference for RT-PCR tissue-based testing methodologies and, occasionally, for liquid biopsy.

Recommendations for detection, prioritization, and treatment of thoracic oncology patients during the COVID-19 pandemic: the THOCOoP cooperative group.

The world currently faces a pandemic due to SARS-CoV-2. Relevant information has emerged regarding the higher risk of poor outcomes in lung cancer patients. As such, lung cancer patients must be prioritized in terms of prevention, detection and treatment. On May 7th, 45 experts in thoracic cancers from 11 different countries were invited to participate. A core panel of experts regarding thoracic oncology care amidst the pandemic gathered virtually, and a total of 60 initial recommendations were drafted based on available evidence, 2 questions were deleted due to conflicting evidence. By May 16th, 44 experts had agreed to participate, and voted on each of the 58 recommendation using a Delphi panel on a live voting event. Consensus was reached regarding the recommendations (>66 % strongly agree/agree) for 56 questions. Strong consensus (>80 % strongly agree/agree) was reached for 44 questions. Patients with lung cancer represent a particularly vulnerable population during this time. Special care must be taken to maintain treatment while avoiding exposure.

Mortality and Advanced Support Requirement for Patients With Cancer With COVID-19: A Mathematical Dynamic Model for Latin America.

PURPOSE: In the midst of a global pandemic, evidence suggests that similar to other severe respiratory viral infections, patients with cancer are at higher risk of becoming infected by COVID-19 and have a poorer prognosis. METHODS: We have modeled the mortality and the intensive care unit (ICU) requirement for the care of patients with cancer infected with COVID-19 in Latin America. A dynamic multistate Markov model was constructed. Transition probabilities were estimated on the basis of published reports for cumulative probability of complications. Basic reproductive number (R0) values were modeled with R using the EpiEstim package. Estimations of days of ICU requirement and absolute mortality were calculated by imputing number of cumulative cases in the Markov model. RESULTS: Estimated median time of ICU requirement was 12.7 days, median time to mortality was 16.3 days after infection, and median time to severe event was 8.1 days. Peak ICU occupancy for patients with cancer was calculated at 16 days after infection. Deterministic sensitivity analysis revealed an interval for mortality between 18.5% and 30.4%. With the actual incidence tendency, Latin America would be expected to lose approximately 111,725 patients with cancer to SARS-CoV-2 (range, 87,116-143,154 patients) by the 60th day since the start of the outbreak. Losses calculated vary between < 1% to 17.6% of all patients with cancer in the region. CONCLUSION: Cancer-related cases and deaths attributable to SARS-CoV-2 will put a great strain on health care systems in Latin America. Early implementation of interventions on the basis of data given by disease modeling could mitigate both infections and deaths among patients with cancer.