Patterns and trends in melanoma mortality in the United States, 1999-2020.

INTRODUCTION: Melanoma, a deadly form of skin cancer, has witnessed a notable increase in incidence over the past decades. Despite advancements in treatment, it remains a significant cause of cancer mortality. Understanding demographic trends and variations in melanoma mortality is crucial for addressing disparities and implementing effective interventions. METHODS: Using the Centers for Disease Control Wide Ranging Online Data for Epidemiologic Research (CDC WONDER) database, we analyzed melanoma mortality data in the United States from 1999 to 2020. Data were stratified by demographic and regional variables, and age-adjusted mortality rates were calculated. Descriptive analysis was performed and Joinpoint regression analysis was employed to identify temporal trends. RESULTS: Between 1999 and 2020, there were 184,416 melanoma-related deaths in the United States Overall, the age-adjusted mortality rate declined from 2.7 to 2.0 per 100,000 people at a rate of -1.3% annually, with significant variations across demographic groups and regions. Men, non-Hispanic White individuals, and those aged > 65 experienced higher mortality rates. Non-Hispanic White individuals noted the steepest decrease in AAMR after 2013 at a rate of -6.1% annually. Disparities were seen by geographic density, with rural populations exhibiting higher mortality compared to their urban and suburban counterparts. CONCLUSION: The study highlights a significant reduction in melanoma mortality in the U.S. since 2013, potentially attributed to advancements in diagnostic techniques such as dermoscopy and the introduction of immune checkpoint inhibitors. Disparities persist, particularly among rural populations. Targeted interventions focusing on increased screening and education are warranted to further mitigate melanoma mortality and address demographic disparities.

Advances in Targeted Therapy for the Treatment of Cervical Cancer.

Cervical cancer is an international public health crisis, affecting several hundred thousand women annually. While not universally protective due to other risk factors, many such cases are preventable with vaccination against high-risk serotypes of the human papilloma virus (HPV 6, 11, 16, 18, 31, 33, 45, 53, 58). Advanced-stage and recurrent cervical cancers are typically lethal and have been the focus in recent years of the integration of immune checkpoint inhibitors (CPIs) to improve survival. We have consolidated information regarding the role of the immune system in both disease progression and disease clearance with the aid of targeted therapies and immunotherapeutic agents. Additionally, we have characterized the treatment modalities currently indicated as the standard of care-such as bevacizumab and the immune CPIs-and those recently approved or in development, including Tivdak, Vigil, and chimeric antigen receptor (CAR) T-cells.

TP53 mutation prevalence in normal airway epithelium as a biomarker for lung cancer risk.

BACKGROUND: There is a need for biomarkers that improve accuracy compared with current demographic risk indices to detect individuals at the highest lung cancer risk. Improved risk determination will enable more effective lung cancer screening and better stratification of lung nodules into high or low-risk category. We previously reported discovery of a biomarker for lung cancer risk characterized by increased prevalence of TP53 somatic mutations in airway epithelial cells (AEC). Here we present results from a validation study in an independent retrospective case-control cohort. METHODS: Targeted next generation sequencing was used to identify mutations within three TP53 exons spanning 193 base pairs in AEC genomic DNA. RESULTS: TP53 mutation prevalence was associated with cancer status (P < 0.001). The lung cancer detection receiver operator characteristic (ROC) area under the curve (AUC) for the TP53 biomarker was 0.845 (95% confidence limits 0.749-0.942). In contrast, TP53 mutation prevalence was not significantly associated with age or smoking pack-years. The combination of TP53 mutation prevalence with PLCOM2012 risk score had an ROC AUC of 0.916 (0.846-0.986) and this was significantly higher than that for either factor alone (P < 0.03). CONCLUSIONS: These results support the validity of the TP53 mutation prevalence biomarker and justify taking additional steps to assess this biomarker in AEC specimens from a prospective cohort and in matched nasal brushing specimens as a potential non-invasive surrogate specimen.

Systemic benefit of radiation therapy via abscopal effect.

Evidence of a systemic response related to localized radiation therapy (RT) in cancer management is rare. However, enhancing the immune response via immunotherapy followed by localized RT has shown evidence of tumor shrinkage to non-irradiated metastatic disease thereby inducing an "abscopal effect." Combined induction of the cGAS-STING pathway and activation of IFN-gamma signaling cascade related to RT within an activated immune environment promotes neoantigen presentation and expansion of cytotoxic effector cells enabling enhancement of systemic immune response. A proposed mechanism, case examples, and clinical trial evidence of "abscopal effect" benefit are reviewed. Results support strategic therapeutic testing to enhance "abscopal effect."

Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples.

BACKGROUND: Clinical laboratories routinely use formalin-fixed paraffin-embedded (FFPE) tissue or cell block cytology samples in oncology panel sequencing to identify mutations that can predict patient response to targeted therapy. To understand the technical error due to FFPE processing, a robustly characterized diploid cell line was used to create FFPE samples with four different pre-tissue processing formalin fixation times. A total of 96 FFPE sections were then distributed to different laboratories for targeted sequencing analysis by four oncopanels, and variants resulting from technical error were identified. RESULTS: Tissue sections that fail more frequently show low cellularity, lower than recommended library preparation DNA input, or target sequencing depth. Importantly, sections from block surfaces are more likely to show FFPE-specific errors, akin to "edge effects" seen in histology, while the inner samples display no quality degradation related to fixation time. CONCLUSIONS: To assure reliable results, we recommend avoiding the block surface portion and restricting mutation detection to genomic regions of high confidence.

Subclonal landscape of cancer drives resistance to immune therapy.

Tumor mutation burden (TMB) is often used as a biomarker for immunogenicity and prerequisite for immune checkpoint inhibitor (ICI) therapy. However, it is becoming increasingly evident that not all tumors with high TMB respond to ICIs as expected. It has been shown that the ability of T-cells to infiltrate the tumor microenvironment and elicit a specific immune response is dependent not only on the TMB, but also on intra-tumor heterogeneity and the fraction of low-frequency subclonal mutations that make up the tumor. High intra-tumor heterogeneity leads to inefficient recognition of tumor neoantigens by T-cells due to their diluted frequency and spatial heterogeneity. Clinical studies have shown that tumors with a high degree of intra-tumor heterogeneity respond poorly to ICI therapy, and previous cytotoxic treatment may increase the intra-tumor heterogeneity and render second-line ICI therapy less effective. This paper reviews the role of ICI therapy when following chemotherapy or radiation to determine if they may be better suited as first-line therapy in patients with high TMB, low intra-tumor heterogeneity, and high PD-1, PD-L1, or CTLA-4 expression.

Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions.

BACKGROUND: Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing. RESULTS: All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden. CONCLUSION: This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency.

BACKGROUND: Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. RESULTS: In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. CONCLUSION: These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

The abscopal effect of radiation therapy.

Radiation therapy (RT) in some cases results in a systemic anticancer response known as the abscopal effect. Multiple hypotheses support the role of immune activation initiated by RT-induced DNA damage. Optimal radiation dose is necessary to promote the cGAS-STING pathway in response to radiation and initiate an IFN-1 signaling cascade that promotes the maturation and migration of dendritic cells to facilitate antigen presentation and stimulation of cytotoxic T cells. T cells then exert a targeted response throughout the body at areas not subjected to RT. These effects are further augmented through the use of immunotherapeutic drugs resulting in increased T-cell activity. Tumor-infiltrating lymphocyte presence and TREX1, KPNA2 and p53 signal expression are being explored as prognostic biomarkers.

Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA.

The primary objective of the FDA-led Sequencing and Quality Control Phase 2 (SEQC2) project is to develop standard analysis protocols and quality control metrics for use in DNA testing to enhance scientific research and precision medicine. This study reports a targeted next-generation sequencing (NGS) method that will enable more accurate detection of actionable mutations in circulating tumor DNA (ctDNA) clinical specimens. To accomplish this, a synthetic internal standard spike-in was designed for each actionable mutation target, suitable for use in NGS following hybrid capture enrichment and unique molecular index (UMI) or non-UMI library preparation. When mixed with contrived ctDNA reference samples, internal standards enabled calculation of technical error rate, limit of blank, and limit of detection for each variant at each nucleotide position in each sample. True-positive mutations with variant allele fraction too low for detection by current practice were detected with this method, thereby increasing sensitivity.

Resident Memory T Cells and Their Effect on Cancer.

Resident memory T (TRM) cells are a unique subset of CD8+ T cells that are present within certain tissues and do not recirculate through the blood. Long term memory establishment and maintenance are dependent on tissue population of memory T cells. They are characterized by dual CD69/CD103 positivity, and play a role in both response to viral infection and local cancer immunosurveillance. Human TRM cells demonstrate the increased expression of adhesion molecules to facilitate tissue retention, have reduced proliferation and produce both regulatory and immune responsive cytokines. TRM cell phenotype is often characterized by a distinct expression profile driven by Runx3, Blimp1, and Hobit transcription factors. The accumulation of TRM cells in tumors is associated with increased survival and response to immunotherapies, including anti-PD-1 and anti-CTLA-4. In this review, we explore potential mechanisms of TRM cell transformation and maintenance, as well as potential applications for the use of TRM cells in both the development of supportive therapies and establishing more accurate prognoses.

Technical advance in targeted NGS analysis enables identification of lung cancer risk-associated low frequency TP53, PIK3CA, and BRAF mutations in airway epithelial cells.

BACKGROUND: Standardized Nucleic Acid Quantification for SEQuencing (SNAQ-SEQ) is a novel method that utilizes synthetic DNA internal standards spiked into each sample prior to next generation sequencing (NGS) library preparation. This method was applied to analysis of normal appearing airway epithelial cells (AEC) obtained by bronchoscopy in an effort to define a somatic mutation field effect associated with lung cancer risk. There is a need for biomarkers that reliably detect those at highest lung cancer risk, thereby enabling more effective screening by annual low dose CT. The purpose of this study was to test the hypothesis that lung cancer risk is characterized by increased prevalence of low variant allele frequency (VAF) somatic mutations in lung cancer driver genes in AEC. METHODS: Synthetic DNA internal standards (IS) were prepared for 11 lung cancer driver genes and mixed with each AEC genomic (g) DNA specimen prior to competitive multiplex PCR amplicon NGS library preparation. A custom Perl script was developed to separate IS reads and respective specimen gDNA reads from each target into separate files for parallel variant frequency analysis. This approach identified nucleotide-specific sequencing error and enabled reliable detection of specimen mutations with VAF as low as 5 × 10- 4 (0.05%). This method was applied in a retrospective case-control study of AEC specimens collected by bronchoscopic brush biopsy from the normal airways of 19 subjects, including eleven lung cancer cases and eight non-cancer controls, and the association of lung cancer risk with AEC driver gene mutations was tested. RESULTS: TP53 mutations with 0.05-1.0% VAF were more prevalent (p < 0.05) and also enriched for tobacco smoke and age-associated mutation signatures in normal AEC from lung cancer cases compared to non-cancer controls matched for smoking and age. Further, PIK3CA and BRAF mutations in this VAF range were identified in AEC from cases but not controls. CONCLUSIONS: Application of SNAQ-SEQ to measure mutations in the 0.05-1.0% VAF range enabled identification of an AEC somatic mutation field of injury associated with lung cancer risk. A biomarker comprising TP53, PIK3CA, and BRAF somatic mutations may better stratify individuals for optimal lung cancer screening and prevention outcomes.