SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery.

Since the publication of the Society for Immunotherapy of Cancer\u27s (SITC) original cancer immunotherapy biomarkers resource document, there have been remarkable breakthroughs in cancer immunotherapy, in particular the development and approval of immune checkpoint inhibitors, engineered cellular therapies, and tumor vaccines to unleash antitumor immune activity. The most notable feature of these breakthroughs is the achievement of durable clinical responses in some patients, enabling long-term survival. These durable responses have been noted in tumor types that were not previously considered immunotherapy-sensitive, suggesting that all patients with cancer may have the potential to benefit from immunotherapy. However, a persistent challenge in the field is the fact that only a minority of patients respond to immunotherapy, especially those therapies that rely on endogenous immune activation such as checkpoint inhibitors and vaccination due to the complex and heterogeneous immune escape mechanisms which can develop in each patient. Therefore, the development of robust biomarkers for each immunotherapy strategy, enabling rational patient selection and the design of precise combination therapies, is key for the continued success and improvement of immunotherapy. In this document, we summarize and update established biomarkers, guidelines, and regulatory considerations for clinical immune biomarker development, discuss well-known and novel technologies for biomarker discovery and validation, and provide tools and resources that can be used by the biomarker research community to facilitate the continued development of immuno-oncology and aid in the goal of durable responses in all patients

2023 Summer Experience Program Abstracts

https://openworks.mdanderson.org/sumexp23/1130/thumbnail.jp

INTEGRATIVE CANCER IMMUNOGENOMIC ANALYSIS OF SERIAL MELANOMA BIOPSIES REVEALS CORRELATES OF RESPONSE AND RESISTANCE TO SEQUENTIAL CTLA-4 AND PD-1 BLOCKADE TREATMENT

Melanoma is the most malignant form of skin cancer. The five-year survival rate for metastatic melanoma is 19.9%. Although targeted therapy of BRAF and MEK inhibitors were developed for melanoma, resistance to therapy is inevitable. Immune checkpoint blockade, which reverses the suppression of the immune system, on the other hand, has shown a durable response in 20-30% of patients with metastatic melanoma. However, more predictive and robust biomarkers of response to this therapy are still needed, and resistance mechanisms remain incompletely understood. To address this, we examined a cohort of metastatic melanoma patients treated with sequential checkpoint blockade against cytotoxic T lymphocyte antigen–4 (CTLA-4) followed by programmed death receptor–1 (PD-1) by immunogenomic profile analyses from serial tumor biopsies. From immune profiling (12 marker immunohistochemistry and NanoString Gene Expression Profiling), we found that adaptive immune signatures in tumor biopsies obtained from early on-treatment time points are predictive of response to immune checkpoint blockade. We also demonstrated differential mechanistic signatures of tumor microenvironment induced by CTLA-4 and PD-1 blockade. Importantly, VEGFA was identified as a potential target of combination therapy withPD-1blockade. From genomic profiling (whole exome sequencing and T cell receptor sequencing), we demonstrated that a higher TCR clonality in pre-treatment biopsy was predictive of response to PD-1 but notCTLA-4blockade. We also observed increased TCR clonality after CTLA-4 blockade treatment in patients responding to the following PD-1 blockade treatment. Analysis of copy number alterations (CNAs) identified a higher burden of copy number loss in nonresponders to CTLA-4 and PD-1 blockade and found that it was associated with decreased expression of genes in immune-related pathways. The effect of mutational load and burden of copy number loss on response was nonredundant, suggesting the potential utility of these as a combinatorial biomarker to optimize patient care with checkpoint blockade therapy. In summary, our integrative cancer immunogenomic analysis shows that genomic and immune profiling of longitudinal tumor biopsies can identify novel biomarkers and resistance mechanisms of immune checkpoint blockade

Extracellular vesicles as cancer liquid biopsy biomarker

Extracellular vesicles (EV) are nanosized cup-shape vesicles, harboring a complex molecular repertoire of lipids, nucleic acids and protein. They exhibit the ability to carry molecular information from parental to target cells, along with playing vital roles in tumorigenesis, growth, progression, metastasis and drug resistance. Alongside circulating tumor cells and circulating cell-free DNA, EV are emerging as an important liquid biopsy component due to their ability to not only mirrorinformation from cell of origin, but also the ability to protect their content in the circulation until arrival at the destination. This thesis describes the isolation method of EV from cell lines, plasma and urine via differential centrifugation. Proteomic characterization was carried out with western blot, in which exosomal proteins, namely tetraspanins CD9 and CD81, were found to be enriched in the vesicles. Transmission electron microscope with anti-CD63 immunolabeling conjugated to 5 nm gold nanoparticles was used for the visualization of EV. Based on the defined criteria CD63-positive EV, varying from cup-shaped to round, 10-100 nm, with an intact membrane and central depression were identified. KRAS, BRAF and ALK mutations from EV isolated from patients of colorectal cancer (CRC), melanoma (MM) and neuroblastoma (NB) were analyzed utilizing Droplet Digital PCR. EV-plasma samples collected post-therapy and tissue samples biopsied prior to treatment were compared, thus, allowing for the investigation of the vesicles’ potential to monitor treatment response and disease progression. Mutated cDNA species were identified in ten of thirty-five cases. Concordance rates with corresponding tissues were 54%, 44% and 25% in CRC, MM and NB cohorts, respectively. Furthermore, two discordant cases were highlighted due to their interesting medical background. In regards to both cases, a mutation switch after anti-EGFR or BRAF/MEK inhibitor therapy was detected prior to disease progression validated via cancer staging or repeated tissue genotyping, providing a prognosis for a disease relapse. In conclusion, we proved that extracellular vesicles are able to provide information on tumor heterogeneity and prognosticate progression. The oncology field is being revolutionizing and advancing in the direction of targeted therapy to provide patients with a more precise approach. Liquid biopsy is therefore a good accompaniment to tissue biopsy in assisting the future development of targeted therapy, which requires the possibility of repetitive real-time monitoring to understand the dynamic changes within the disease. EV-derived nucleic acids may provide clinically relevant diagnostic information and mirror the evolution of the disease.Extrazelluläre Vesikel sind nanogroße, kelchförmige Vesikel mit komplexen molekularen Inhalten wie Lipiden, Nukleinsäuren und Proteinen. Sie können ihre molekularen Informationen von Mutterzellen an Zielzellen weitergeben und spielen daher eine wichtige Rolle bei Tumorentstehung, -wachstum und -progression, sowie beim Prozess der Metastasierung und dem Auftreten von Resistenzen gegen geläufige Therapiemethoden. Aufgrund ihres Vermögens nicht nur die Informationen von Mutterzellen widerzuspiegeln, sondern auch ihren molekularen Inhalt während des Transports durch die Blutzirkulation bis zur Ankunft am Zielort zu schützen, entwickeln sich EV neben zirkulierenden Tumorzellen und zell-freie DNA zu einer weiteren wichtigen Komponente der Liquid Biopsy. Diese Dissertation beschreibt Methoden zur Isolierung von EV aus Zelllinien, Plasmen und Urin durch differentielle Zentrifugation. Die Charakterisierung der Proteine erfolgte durch Western Blots und ergab eine Anreicherung exosomaler Proteine (Tetraspanin CD9 und CD81) in den Vesikeln. Zur Visualisierung der Vesikel wurde ein Transmissionselektronenmikroskop mit anti-CD63 immunozytochemischer Markierung, konjugiert zu 5 nm Gold-Nanopartikeln, genutzt. Basierend auf definierten Kriterien wurden CD63 positive EV, mit variierender Form von rund bis kelchförmig, einer Größe von 10-100 nm, einer intakten Membran und zentraler Depression identifiziert. Die aus Proben von Patienten mit kolorektalem Karzinom, Melanom und Neuroblastom isolierten EV wurden mit Hilfe von Droplet Digital PCRs auf Mutationen in KRAS, BRAF und ALK untersucht. Vergleiche von posttherapeutischen EV-Plasmaproben mit prätherapeutischen Biopsieproben ermöglichten die Abschätzung des Potentials von EV als Faktor zur Überwachung des Therapieanschlagens und Krankheitsverlaufs. In 10 von 35 Fällen konnte eine Mutation in der cDNA festgestellt werden. Die Konkordanzrate mit dem dazugehörigen Tumorgewebe in den jeweiligen Kohorten waren 54% für kolorektale Karzinome, 44% für Melanome und 25% Neuroblastome. Außerdem lassen sich zwei Fälle aufgrund ihrer Abweichungen und ihrem interessanten medizinischen Hintergrund hervorheben. In beiden Fällen konnte eine Änderung der Mutation nach einer Therapie mit EGFR- oder BRAF/MEK-Inhibitoren vor der Progressionsbestätigung durch Krebs-Staging oder wiederholtes Feststellen des Gewebe-Genotyps detektiert werden und ermöglichte somit ein Krankheitsrezidiv zu prognostizieren. Zusammenfassend gesagt, sind extrazelluläre Vesikel in der Lage Informationen über die Tumorheterogenität zu liefern, sowie eine Prognose über das Voranschreiten des Tumorwachstums zu ermöglichen. Die onkologische Forschung bewegt sich in Richtung von personalisierten und patientenspezifischen Therapien. Dies erfordert die Möglichkeit einer wiederholbaren Echtzeitüberwachung der Krebserkrankung. Liquid Biopsy stellt daher einen guten und wichtigen Zusatz zur herkömmlichen Gewebebiopsie dar, um die dynamischen Veränderungen zu verstehen. Die in extrazellulären Vesikeln enthaltenen Nukleinsäuren können dabei klinische sowie diagnostische Information anbieten und folglich die Entwicklung der Krankheit widerspiegeln

Statistical Methods for Gene-Environment Interactions

Despite significant main effects of genetic and environmental risk factors have been found, the interactions between them can play critical roles and demonstrate important implications in medical genetics and epidemiology. Although many important gene-environment (G-E) interactions have been identified, the existing findings are still insufficient and there exists a strong need to develop statistical methods for analyzing G-E interactions. In this dissertation, we propose four statistical methodologies and computational algorithms for detecting G-E interactions and one application to imaging data. Extensive simulation studies are conducted in comparison with multiple advanced alternatives. In the analyses of The Cancer Genome Atlas datasets on multiple cancers, biologically meaningful findings are obtained. First, we develop two robust interaction analysis methods for prognostic outcomes. Compared to continuous and categorical outcomes, prognosis has been less investigated, with additional challenges brought by the unique characteristics of survival times. Most of the existing G-E interaction approaches for prognosis data share the limitation that they cannot accommodate long-tailed or contaminated outcomes. In the first method, we adopt the censored quantile regression and partial correlation for survival outcomes. Under a marginal modeling framework, this proposed approach is robust to long-tailed prognosis and is computationally straightforward to apply. Furthermore, outliers and contaminations among predictors are observed in real data. In the second method, we propose a joint model using the penalized trimmed regression that is robust to leverage points and vertical outliers. The proposed method respects the hierarchical structure of main effects and interactions and has an effective computational algorithm based on coordinate descent optimization and stability selection. Second, we propose a penalized approach to incorporate additional information for identifying important hierarchical interactions. Due to the high dimensionality and low signal levels, it is challenging to analyze interactions so that incorporating additional information is desired. We adopt the minimax concave penalty for regularized estimation and the Laplacian quadratic penalty for additional information. Under a unified formulation, multiple types of additional information and genetic measurements can be effectively utilized and improved identification accuracy can be achieved. Third, we develop a three-step procedure using multidimensional molecular data to identify G-E interactions. Recent studies have shown that collectively analyzing multiple types of molecular changes is not only biologically sensible but also leads to improved estimation and prediction. In this proposed method, we first estimate the relationship between gene expressions and their regulators by a multivariate penalized regression, and then identify regulatory modules via sparse biclustering. Next, we establish integrative covariates by principal components extracted from the identified regulatory modules. Last but not least, we construct a joint model for disease outcomes and employ Lasso-based penalization to select important main effects and hierarchical interactions. The proposed method expands the scope of interaction analysis to multidimensional molecular data. Last, we present an application using both marginal and joint models to analyze histopathological imaging-environment interactions. In cancer diagnosis, histopathological imaging has been routinely conducted and can be processed to generate high-dimensional features. To explore potential interactions, we conduct marginal and joint analyses, which have been extensively examined in the context of G-E interactions. This application extends the practical applicability of interaction analysis to imaging data and provides an alternative venue that combines histopathological imaging and environmental data in cancer modeling. Motivated by the important implications of G-E interactions and to overcome the limitations of the existing methods, the goal of this dissertation is to advance in methodological development for G-E interaction analysis and to provide practically useful tools for identifying important interactions. The proposed methods emerge from practical issues observed in real data and have solid statistical properties. With a balance between theory, computation, and data analysis, this dissertation provide four novel approaches for analyzing interactions to achieve more robust and accurate identification of biologically meaningful interactions

Cutaneous Melanoma Classification: The Importance of High-Throughput Genomic Technologies

Cutaneous melanoma is an aggressive tumor responsible for 90% of mortality related to skin cancer. In the recent years, the discovery of driving mutations in melanoma has led to better treatment approaches. The last decade has seen a genomic revolution in the field of cancer. Such genomic revolution has led to the production of an unprecedented mole of data. High-throughput genomic technologies have facilitated the genomic, transcriptomic and epigenomic profiling of several cancers, including melanoma. Nevertheless, there are a number of newer genomic technologies that have not yet been employed in large studies. In this article we describe the current classification of cutaneous melanoma, we review the current knowledge of the main genetic alterations of cutaneous melanoma and their related impact on targeted therapies, and we describe the most recent highthroughput genomic technologies, highlighting their advantages and disadvantages. We hope that the current review will also help scientists to identify the most suitable technology to address melanoma-related relevant questions. The translation of this knowledge and all actual advancements into the clinical practice will be helpful in better defining the different molecular subsets of melanoma patients and provide new tools to address relevant questions on disease management. Genomic technologies might indeed allow to better predict the biological - and, subsequently, clinical - behavior for each subset of melanoma patients as well as to even identify all molecular changes in tumor cell populations during disease evolution toward a real achievement of a personalized medicine

Algorithms and Methods for Robust Processing and Analysis of Mass Spectrometry Data

Liquid chromatography-mass spectrometry (LC-MS) and mass spectrometry imaging (MSI) are two techniques that are routinely used to study proteins, peptides, and metabolites at a large scale. Thousands of biological compounds can be identified and quantified in a single experiment with LC-MS, but many studies fail to convert this data to a better understanding of disease biology. One of the primary reasons for this is low reproducibility, which in turn is partially due to inaccurate and/or inconsistent data processing. Protein biomarkers and signatures for various types of cancer are frequently discovered with LC-MS, but their behavior in independent cohorts is often inconsistent to that in the discovery cohort. Biomarker candidates must be thoroughly validated in independent cohorts, which makes the ability to share data across different laboratories crucial to the future success of the MS-based research fields. The emergence and growth of public repositories for MSI data is a step in the rightdirection. Still, many of those data sets remain incompatible one another due to inaccurate or incompatible preprocessing strategies. Ensuring compatibility between data generated in different labs is therefore necessary to gain access to the full potential of MS-based research. In two of the studies that I present in this thesis, we used LC-MS to characterize lymph node metastases from individuals with melanoma. Furthermore, my thesis work has resulted in two novel preprocessing methods for MSI data sets. The first one is a peak detection method that achieves considerably higher sensitivity for faintly expressed compounds than existing methods, and the second one is a accurate, robust, and general approach to mass alignment. Both algorithms deliberately rely on centroid spectra, which makes them compatible with most shared data sets. I believe that the improvements demonstrated by these methods can lead to a higher reproducibility in the MS-based research fields, and, ultimately, to a better understanding of disease processes