Changes in population-level survival for advanced solid malignancies with new treatment options in the second decade of the 21st century

Classification and Staging of Melanoma in the Head and Neck

The rates of melanoma continue to rise, with recent estimates have shown that 18% to 22% of new melanoma cases occur within the head and neck in the United States each year. The mainstay of treatment of nonmetastatic primary melanomas of the head and neck includes the surgical resection and management of regional disease as indicated. Thorough knowledge of the classification and staging of melanoma is paramount to evaluate prognosis, determine the appropriate surgical intervention, and assess eligibility for adjuvant therapy and clinic trials. The traditional clinicopathologic classification of melanoma is based on morphologic aspects of the growth phase and distinguishes 4 of the most common subtypes as defined by the World Health Organization: superficial spreading, nodular, acral lentiginous, and lentigo maligna melanoma. The data used to derive the AJCC TNM Categories are based on superficial spreading melanoma and nodular subtypes. Melanoma is diagnosed histopathologically following initial biopsy that will assist with classifying the tumor to guide treatment. Classification is based on tumor thickness and ulceration (T stage, Breslow Staging), Regional Lymph Node Involvement (N Stage), and presence of metastasis (M Stage). Tumor thickness (Breslow thickness) and ulceration are 2 independent prognostic factors that have been shown to be the strongest predictors of survival and outcome. Clark level of invasion and mitotic rate are no longer incorporated into the current AJCC staging system, but still have shown to be important prognostic factors for cutaneous melanoma. For patients with metastatic (Stage IV) disease Lactate Dehydrogenase remains an independent predictor of survival. The Maxillofacial surgeon must remain up to date on the most current management strategies in this patient population. Classification systems and staging provide the foundation for clinical decision making and prognostication for the Maxillofacial surgeon when caring for these patients.

Genetic network and gene set enrichment analyses identify MND1 as potential diagnostic and therapeutic target gene for lung adenocarcinoma

This study aimed to characterize the key survival-specific genes for lung adenocarcinoma (LUAD) using machine-based learning approaches. Gene expression profiles were download from gene expression omnibus to analyze differentially expressed genes (DEGs) in LUAD tissues versus healthy lung tissue and to construct protein–protein interaction (PPI) networks. Using high-dimensional datasets of cancer specimens from clinical patients in the cancer genome atlas, gene set enrichment analysis was employed to assess the independent effect of meiotic nuclear divisions 1 (MND1) expression on survival status, and univariate and multivariate Cox regression analyses were applied to determine the associations of clinic-pathologic characteristics and MND1 expression with overall survival (OS). A set of 495 DEGs (145 upregulated and 350 downregulated) was detected, including 63 hub genes with ≥ 10 nodes in the PPI network. Among them, MND1 was participated in several important pathways by connecting with other genes via 17 nodes in lung cancer, and more frequently expressed in LUAD patients with advancing stage (OR = 1.68 for stage III vs. stage I). Univariate and multivariate Cox analyses demonstrated that the expression level of MND1 was significantly and negatively correlated with OS. Therefore, MND1 is a promising diagnostic and therapeutic target for LUAD.

Artificial Intelligence for Prognostic Scores in Oncology: a Benchmarking Study

Introduction: Prognostic scores are important tools in oncology to facilitate clinical decision-making based on patient characteristics. To date, classic survival analysis using Cox proportional hazards regression has been employed in the development of these prognostic scores. With the advance of analytical models, this study aimed to determine if more complex machine-learning algorithms could outperform classical survival analysis methods.

Methods: In this benchmarking study, two datasets were used to develop and compare different prognostic models for overall survival in pan-cancer populations: a nationwide EHR-derived de-identified database for training and in-sample testing and the OAK (phase III clinical trial) dataset for out-of-sample testing. A real-world database comprised 136K first-line treated cancer patients across multiple cancer types and was split into a 90% training and 10% testing dataset, respectively. The OAK dataset comprised 1,187 patients diagnosed with non-small cell lung cancer. To assess the effect of the covariate number on prognostic performance, we formed three feature sets with 27, 44 and 88 covariates. In terms of methods, we benchmarked ROPRO, a prognostic score based on the Cox model, against eight complex machine-learning models: regularized Cox, Random Survival Forests (RSF), Gradient Boosting (GB), DeepSurv (DS), Autoencoder (AE) and Super Learner (SL). The C-index was used as the performance metric to compare different models.

Results: For in-sample testing on the real-world database the resulting C-index [95% CI] values for RSF 0.720 [0.716, 0.725], GB 0.722 [0.718, 0.727], DS 0.721 [0.717, 0.726] and lastly, SL 0.723 [0.718, 0.728] showed significantly better performance as compared to ROPRO 0.701 [0.696, 0.706]. Similar results were derived across all feature sets. However, for the out-of-sample validation on OAK, the stronger performance of the more complex models was not apparent anymore. Consistently, the increase in the number of prognostic covariates did not lead to an increase in model performance.

Discussion: The stronger performance of the more complex models did not generalize when applied to an out-of-sample dataset. We hypothesize that future research may benefit by adding multimodal data to exploit advantages of more complex models.

Patterns of glioblastoma treatment and survival over a 16-years period: pooled data from the German Cancer Registries

Introduction

Glioblastoma multiforme (GBM) is a primary malignant brain tumour characterized by a very low long-term survival. The aim of this study was to analyse the distribution of treatment modalities and their effect on survival for GBM cases diagnosed in Germany between 1999 and 2014.

Methods

Cases were pooled from the German Cancer Registries with International Classification of Diseases for Oncology, third edition (ICD-O-3) codes for GBM or giant-cell GBM. Three periods, first (January 1999–December 2005), second (January 2006–December 2010) and a third period (January 2011–December 2014) were defined. Kaplan–Meier plots with long-rank test compared median overall survival (OS) between groups. Survival differences were assessed with Cox proportional-hazards models adjusted for available confounders.

Results

In total, 40,138 adult GBM cases were analysed, with a mean age at diagnosis 64.0 ± 12.4 years. GBM was more common in men (57.3%). The median OS was 10.0 (95% CI 9.0–10.0) months. There was an increase in 2-year survival, from 16.6% in the first to 19.3% in the third period. When stratified by age group, period and treatment modalities, there was an improved median OS after 2005 due to treatment advancements. Younger age, female sex, surgical resection, use of radiotherapy and chemotherapy, were independent factors associated with better survival.

Conclusion

The inclusion of temozolomide chemotherapy has considerably improved median OS in the older age groups but had a lesser effect in the younger age group of cases. The analysis showed survival improvements for each treatment option over time.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-021-03596-5.

Population-level changes in outcomes and Medicare cost following the introduction of new cancer therapies

OBJECTIVE

To examine the population-level impacts of the introduction of novel cancer therapies with high cost in the United States, using immunotherapies in advanced nonsmall cell lung cancer (NSCLC) as an example.

DATA SOURCES

Surveillance, Epidemiology, and End Results data in 2012-2015 linked to Medicare fee-for-service claims until 2016.

STUDY DESIGN

We examined population-level trends in treatment patterns, survival, and Medicare spending in patients diagnosed with advanced NSCLC, the leading cause of cancer death in the United States, between 2012 and 2015. We estimated the percentage of patients who received any antineoplastic therapy within two years of diagnosis, including novel immunotherapies. We compared the trends in overall survival and mean two-year Medicare spending per each patient before and after the introduction of immunotherapies in 2015.

DATA COLLECTION/EXTRACTION METHODS

Not Applicable.

PRINCIPAL FINDINGS

The percentage of patients treated with any antineoplastic therapy remained the same at 46.7% in 2012 and 2015, whereas the use of immunotherapies increased from 0% to 15.2%. The two-year survival rate and median survival increased by 3.3 percentage points (95% CI: 2.0, 4.5) and 0.4 months (CI: 0.0, 0.9), respectively, during the same period. The mean two-year total Medicare spending and outpatient spending per patient increased by $5735 (CI: 3479, 8040) and $7661 (CI: 5902, 9311), respectively, which were largely attributable to the increases in immunotherapy spending by $5806 (CI: 5165, 6459).

CONCLUSIONS

The introduction of lung cancer immunotherapies was accompanied by improvements in survival and increases in spending between 2012 and 2015 in the Medicare population. As novel immunotherapies and other target therapies continue to change the clinical management of various cancers, further efforts are needed to ensure their effective and efficient use, and to understand their population-level impacts in the United States.

Research Interest and Public Interest in Melanoma: A Bibliometric and Google Trends Analysis

INTRODUCTION

Melanoma is a severe skin cancer that metastasizes quickly. Bibliometric analysis can quantify hotspots of research interest. Google Trends can provide information to address public concerns.

METHODS

The top 15 most frequently cited articles on melanoma each year from 2015 to 2019, according to annual citations, were retrieved from the Web of Science database. Original articles, reviews, and research letters were included in this research. For the Google Trends analysis, the topic "Melanoma" was selected as the keyword. Online search data from 2004 to 2019 were collected. Four countries (New Zealand, Australia, the United States and the United Kingdom) were selected for seasonal analysis. Annual trends in relative search volume and seasonal variation were analyzed, and the top related topics and rising related topics were also selected and analyzed.

RESULTS

The top 15 most frequently cited articles each year were all original articles that focused on immunotherapy (n=8), omics (n=5), and the microbiome (n=2). The average relative search volume remained relatively stable across the years. The seasonal variation analysis revealed that the peak appeared in summer, and the valley appeared in winter. The diseases associated with or manifestations of melanoma, treatment options, risk factors, diagnostic tools, and prognosis were the topics in which the public was most interested. Most of the topics revealed by bibliometric and Google Trends analyses were consistent, with the exception of issues related to the molecular biology of melanoma.

CONCLUSION

This study revealed the trends in research interest and public interest in melanoma, which may pave the way for further research.

Lactic Acid and an Acidic Tumor Microenvironment suppress Anticancer Immunity

Immune evasion and altered metabolism, where glucose utilization is diverted to increased lactic acid production, are two fundamental hallmarks of cancer. Although lactic acid has long been considered a waste product of this alteration, it is now well accepted that increased lactic acid production and the resultant acidification of the tumor microenvironment (TME) promotes multiple critical oncogenic processes including angiogenesis, tissue invasion/metastasis, and drug resistance. We and others have hypothesized that excess lactic acid in the TME is responsible for suppressing anticancer immunity. Recent studies support this hypothesis and provide mechanistic evidence explaining how lactic acid and the acidic TME impede immune cell functions. In this review, we consider lactic acid’s role as a critical immunoregulatory molecule involved in suppressing immune effector cell proliferation and inducing immune cell de-differentiation. This results in the inhibition of antitumor immune responses and the activation of potent, negative regulators of innate and adaptive immune cells. We also consider the role of an acidic TME in suppressing anticancer immunity. Finally, we provide insights to help translate this new knowledge into impactful anticancer immune therapies.

The complex relationship between body mass index and response to immune checkpoint inhibition in metastatic melanoma patients

Despite major improvements in combatting metastatic melanoma since the advent of immunotherapy, the overall survival for patients with advanced disease remains low. Recently, there is a growing number of reports supporting an “obesity paradox,” in which patients who are overweight or mildly obese may exhibit a survival benefit in patients who received immune checkpoint inhibitors. We studied the relationship between body mass index and progression-free survival and overall survival in a cohort of 423 metastatic melanoma patients receiving immunotherapy, enrolled and prospectively followed up in the NYU Interdisciplinary Melanoma Cooperative Group database. We analyzed this association stratified by first vs. second or greater-line of treatment and treatment type adjusting for age, gender, stage, lactate dehydrogenase, Eastern Cooperative Oncology Group performance status, number of metastatic sites, and body mass index classification changes. In our cohort, the patients who were overweight or obese did not have different progression-free survival than patients with normal body mass index. Stratifying this cohort by first vs. non-first line immunotherapy revealed a moderate but insignificant association between being overweight or obese and better progression-free survival in patients who received first line. Conversely, an association with worse progression-free survival was observed in patients who received non-first line immune checkpoint inhibitors. Specifically, overweight and obese patients receiving combination immunotherapy had a statistically significant survival benefit, whereas patients receiving the other treatment types showed heterogeneous trends. We caution the scientific community to consider several important points prior to drawing conclusions that could potentially influence patient care, including preclinical data associating obesity with aggressive tumor biology, the lack of congruence amongst several investigations, and the limited reproduced comprehensiveness of these studies.