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PURPOSE Currently, there are no robust biomarkers that predict immunotherapy outcomes in metastatic melanoma. We sought to build multivariable predictive models for response and survival to anti-programmed cell death protein 1 (anti–PD-1) monotherapy or in combination with anticytotoxic T-cell lymphocyte-4 (ipilimumab [IPI]; anti–PD-1 ± IPI) by including routine clinical data available at the point of treatment initiation. METHODS One thousand six hundred forty-four patients with metastatic melanoma treated with anti–PD-1 ± IPI at 16 centers from Australia, the United States, and Europe were included. Demographics, disease characteristics, and baseline blood parameters were analyzed. The end points of this study were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). The final predictive models for ORR, PFS, and OS were determined through penalized regression methodology (least absolute shrinkage and selection operator method) to select the most significant predictors for all three outcomes (discovery cohort, N = 633). Each model was validated internally and externally in two independent cohorts (validation-1 [N = 419] and validation-2 [N = 592]) and nomograms were created. RESULTS The final model for predicting ORR (area under the curve [AUC] = 0.71) in immunotherapy-treated patients included the following clinical parameters: Eastern Cooperative Oncology Group Performance Status, presence/absence of liver and lung metastases, serum lactate dehydrogenase, blood neutrophil-lymphocyte ratio, therapy (monotherapy/combination), and line of treatment. The final predictive models for PFS (AUC = 0.68) and OS (AUC = 0.77) included the same variables as those in the ORR model (except for presence/absence of lung metastases), and included presence/absence of brain metastases and blood hemoglobin. Nomogram calculators were developed from the clinical models to predict outcomes for patients with metastatic melanoma treated with anti–PD-1 ± IPI. CONCLUSION Newly developed combinations of routinely collected baseline clinical factors predict the response and survival outcomes of patients with metastatic melanoma treated with immunotherapy and may serve as valuable tools for clinical decision making.

Patients with cancer have higher COVID-19 morbidity and mortality. Here we present the prospective CAPTURE study, integrating longitudinal immune profiling with clinical annotation. Of 357 patients with cancer, 118 were SARS-CoV-2 positive, 94 were symptomatic and 2 died of COVID-19. In this cohort, 83% patients had S1-reactive antibodies and 82% had neutralizing antibodies against wild type SARS-CoV-2, whereas neutralizing antibody titers against the Alpha, Beta and Delta variants were substantially reduced. S1-reactive antibody levels decreased in 13% of patients, whereas neutralizing antibody titers remained stable for up to 329 days. Patients also had detectable SARS-CoV-2-specific T cells and CD4+ responses correlating with S1-reactive antibody levels, although patients with hematological malignancies had impaired immune responses that were disease and treatment specific, but presented compensatory cellular responses, further supported by clinical recovery in all but one patient. Overall, these findings advance the understanding of the nature and duration of the immune response to SARS-CoV-2 in patients with cancer.

SUMMARYThere is a pressing need to characterise the nature, extent and duration of immune response to SARS-CoV-2 in cancer patients and inform risk-reduction strategies and preserve cancer outcomes. CAPTURE is a prospective, longitudinal cohort study of cancer patients and healthcare workers (HCWs) integrating longitudinal immune profiling and clinical annotation. We evaluated 529 blood samples and 1051 oronasopharyngeal swabs from 144 cancer patients and 73 HCWs and correlated with >200 clinical variables. In patients with solid cancers and HCWs, S1-reactive and neutralising antibodies to SARS-CoV-2 were detectable five months post-infection. SARS-CoV-2-specific T-cell responses were detected, and CD4+T-cell responses correlated with S1 antibody levels. Patients with haematological malignancies had impaired but partially compensated immune responses. Overall, cancer stage, disease status, and therapies did not correlate with immune responses. These findings have implications for understanding individual risks and potential effectiveness of SARS-CoV-2 vaccination in the cancer population.

Simple Summary In recent years, a number of new, effective treatments have become available for advanced renal (kidney) cancer. However, with more drugs available it has become more difficult to decide which drugs to choose for particular patients, and in which order to use them. In addition, the new treatments do not work for all patients and they can have troublesome side effects. At the moment, these choices are made according to factors including the type of renal carcinoma from a biopsy, the extent of cancer for that patient, their previous health and their current fitness. The options are summarized in guidelines although these do not make recommendations for individual patients. It is hoped that ongoing research will uncover new tests that allow these decisions to be made more accurately in a “personalized” manner. This article describes how the process is undertaken at present and how it may change in the future. Abstract Therapeutic options for treating advanced renal cell cancer (RCC) are rapidly evolving. Vascular endothelial growth factor (VEGF)-directed therapy, predominantly VEGF receptor (VEGFr) tyrosine kinase inhibitors (TKIs) had been the most effective first line treatment since 2005 irrespective of International Metastatic RCC Database Consortium (IMDC) risk stratification. However, immune checkpoint inhibitors (ICI) have recently changed the treatment paradigm for advanced RCC particularly as the first-line systemic treatment modality. The combination of Ipilimumab and Nivolumab provides better disease control and long-term outcomes compared with the anti-VEGFr TKI Sunitinib for IMDC intermediate- to poor-risk patients and we now have the option of using ICI with TKI upfront for all IMDC risk groups. This poses a challenge for physicians, both to select the most suitable first line regimen and the most suitable subsequent therapy given the lack of data about sequencing in this setting. This treatment landscape is expected to become more complex with the emerging treatment options. Moreover, these therapeutic options cannot be generalized as significant variability exists between individual’s disease biologies and their physiologies for handling treatment adverse effects. Notable efforts are being made to identify promising predictive biomarkers ranging from neo-antigen load to gene expression profiling. These biomarkers need prospective validation to justify their utility in clinical practice and in treatment decision making. This review article discusses various clinicopathological characteristics that should be carefully evaluated to help select appropriate treatment and discusses the current status of biomarker-based selection.

The discovery of “soft” ionization techniques in mass spectrometry (MS) such as electrospray ionization (ESI) [1] and matrix-assisted laser desorption/ionization (MALDI) [2] for measuring molecular masses of intact proteins was a significant breakthrough in the analysis of proteins. The work was recognized by a Nobel Prize in Chemistry in 2002 and led to the widespread use of ESI–MS to characterize intact protein molecular masses in protein therapeutics discovery and development in biotechnology. Previously, it was only possible to measure peptide molecular masses. In contrast to the analysis of intact purified proteins in simple buffers by ESI–MS, the ability to analyze biotherapeutic proteins in plasma or other tissues is significantly more challenging due to interference from the background plasma/tissue proteome and has only recently been reported for plasma [3]. The intact molecular mass measurement of biotherapeutic proteins in plasma by ESI–MS required isolation of the proteins from plasma using affinity capture followed by elution of the isolated intact biotherapeutics of interest and liquid chromatography (LC)–ESI–MS characterization to determine their intact molecular masses. The ability to obtain intact molecular masses and thereby characterize structural changes in biotherapeutics in plasma for in vivo studies provides key insights for large molecule drug development. Additional information can be obtained by enzymatic digestion followed by peptide analysis using LC-tandem MS (MS/MS) methodology. Understanding biotransformation and molecular changes of biotherapeutics in vivo is particularly valuable for the development of antibody–drug conjugates (ADCs) where efficacy and safety may be affected. It also provides essential structural characterization information for the ADCs in vivo, necessary for designing appropriate quantitative assays for measuring pharmacokinetics (PK) and toxicokinetics (TK).