A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery

BackgroundPredicting treatment response or survival of cancer patients remains challenging in immuno-oncology. Efforts to overcome these challenges focus, among others, on the discovery of new biomarkers. Despite advances in cellular and molecular approaches, only a limited number of candidate biomarkers eventually enter clinical practice.MethodsA computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and to investigate its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we revealed fundamental principles that explain the diverging success rates of biomarker discovery programs.ResultsOur model shows that a tipping point—a sharp state transition between immune control and immune evasion—induces a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments.ConclusionThese findings have two important implications for clinical oncology. First, the apparent conundrum that ICI induces substantial benefits in some patients yet completely fails in others could be, to a large extent, explained by the presence of a tipping point. Second, predictive biomarkers for immunotherapy should ideally combine both immunological and tumor-related markers, as a patient’s distance from the tipping point can typically not be reliably determined from solely one of these. The notion of a tipping point in cancer-immune dynamics helps to devise more accurate strategies to select appropriate treatments for patients with cancer.

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A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery

10.1101/2020.10.29.20222455 ◽

2020 ◽

Author(s):

Jeroen H.A. Creemers ◽

Willem J. Lesterhuis ◽

Niven Mehra ◽

Winald R. Gerritsen ◽

Carl G. Figdor ◽

...

Keyword(s):

Biomarker Discovery ◽

Patient Survival ◽

Predictive Biomarkers ◽

Tipping Point ◽

Success Rates ◽

Molecular Approaches ◽

Patient Status ◽

New Biomarkers ◽

The Right ◽

Selection Of

ABSTRACTBackgroundIn the field of immuno-oncology, predicting treatment response or survival of cancer patients remains a challenge. Efforts to overcome these challenges focus mainly on the discovery of new biomarkers. Owing to the complexity of cancers and their tumor microenvironment, only a limited number of candidate biomarkers eventually enters clinical practice, despite advances in cellular and molecular approaches.MethodsA computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and show its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we extracted fundamental principles underlying the success rates of biomarker discovery programs.ResultsOur main finding is the prediction of a tipping point – a sharp state transition between immune control and immune evasion – that follows a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments.ConclusionThese findings have two important implications for clinical oncology. First, the apparent conundrum that ICI induces substantial benefits in some patients yet completely fails in others could be, to a large extent, explained by the presence of a tipping point. Second, predictive biomarkers for immunotherapy should ideally combine both immunological and tumor-related markers, as the distance of a patient’ status from the tipping point cannot, in general, be reliably determined from solely one of these. The notion of a tipping point in cancer-immune dynamics could help to optimize strategies in biomarker discovery to ensure accurate selection of the right patient for the right treatment.

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The combined detection of Amphiregulin, Cyclin A1 and DDX20/Gemin3 expression predicts aggressive forms of oral squamous cell carcinoma

British Journal of Cancer ◽

10.1038/s41416-021-01491-x ◽

2021 ◽

Author(s):

Ekaterina Bourova-Flin ◽

Samira Derakhshan ◽

Afsaneh Goudarzi ◽

Tao Wang ◽

Anne-Laure Vitte ◽

...

Keyword(s):

Gene Expression ◽

Squamous Cell ◽

Large Scale ◽

Biomarker Discovery ◽

Gene Expression Signature ◽

Predictive Biomarkers ◽

Specific Gene ◽

Specific Expression ◽

Intrinsic Nature ◽

Independent Cohort

Abstract Background Large-scale genetic and epigenetic deregulations enable cancer cells to ectopically activate tissue-specific expression programmes. A specifically designed strategy was applied to oral squamous cell carcinomas (OSCC) in order to detect ectopic gene activations and develop a prognostic stratification test. Methods A dedicated original prognosis biomarker discovery approach was implemented using genome-wide transcriptomic data of OSCC, including training and validation cohorts. Abnormal expressions of silent genes were systematically detected, correlated with survival probabilities and evaluated as predictive biomarkers. The resulting stratification test was confirmed in an independent cohort using immunohistochemistry. Results A specific gene expression signature, including a combination of three genes, AREG, CCNA1 and DDX20, was found associated with high-risk OSCC in univariate and multivariate analyses. It was translated into an immunohistochemistry-based test, which successfully stratified patients of our own independent cohort. Discussion The exploration of the whole gene expression profile characterising aggressive OSCC tumours highlights their enhanced proliferative and poorly differentiated intrinsic nature. Experimental targeting of CCNA1 in OSCC cells is associated with a shift of transcriptomic signature towards the less aggressive form of OSCC, suggesting that CCNA1 could be a good target for therapeutic approaches.

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Find the Flame: Predictive Biomarkers for Immunotherapy in Melanoma

Cancers ◽

10.3390/cancers13081819 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1819

Author(s):

Mattia Garutti ◽

Serena Bonin ◽

Silvia Buriolla ◽

Elisa Bertoli ◽

Maria Antonietta Pizzichetta ◽

...

Keyword(s):

Meta Analysis ◽

Tumor Biology ◽

Predictive Biomarkers ◽

Decision Making Process ◽

Checkpoint Inhibition ◽

Clinical Context ◽

Term Outcome ◽

Long Term Outcome ◽

Therapeutic Landscape

Immunotherapy has revolutionized the therapeutic landscape of melanoma. In particular, checkpoint inhibition has shown to increase long-term outcome, and, in some cases, it can be virtually curative. However, the absence of clinically validated predictive biomarkers is one of the major causes of unpredictable efficacy of immunotherapy. Indeed, the availability of predictive biomarkers could allow a better stratification of patients, suggesting which type of drugs should be used in a certain clinical context and guiding clinicians in escalating or de-escalating therapy. However, the difficulty in obtaining clinically useful predictive biomarkers reflects the deep complexity of tumor biology. Biomarkers can be classified as tumor-intrinsic biomarkers, microenvironment biomarkers, and systemic biomarkers. Herein we review the available literature to classify and describe predictive biomarkers for checkpoint inhibition in melanoma with the aim of helping clinicians in the decision-making process. We also performed a meta-analysis on the predictive value of PDL-1.

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Liquid Biopsy in Hepatocellular Carcinoma: Where Are We Now?

Cancers ◽

10.3390/cancers13092274 ◽

2021 ◽

Vol 13 (9) ◽

pp. 2274

Author(s):

Filippo Pelizzaro ◽

Romilda Cardin ◽

Barbara Penzo ◽

Elisa Pinto ◽

Alessandro Vitale ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liquid Biopsy ◽

Predictive Biomarkers ◽

Invasive Procedure ◽

Therapeutic Monitoring ◽

Comprehensive Overview ◽

Improve Patient Survival ◽

Prognostic Tests ◽

Prognostic Stratification ◽

New Biomarkers

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer related death worldwide. Diagnostic, prognostic, and predictive biomarkers are urgently needed in order to improve patient survival. Indeed, the most widely used biomarkers, such as alpha-fetoprotein (AFP), have limited accuracy as both diagnostic and prognostic tests. Liver biopsy provides an insight on the biology of the tumor, but it is an invasive procedure, not routinely used, and not representative of the whole neoplasia due to the demonstrated intra-tumoral heterogeneity. In recent years, liquid biopsy, defined as the molecular analysis of cancer by-products, released by the tumor in the bloodstream, emerged as an appealing source of new biomarkers. Several studies focused on evaluating extracellular vesicles, circulating tumor cells, cell-free DNA and non-coding RNA as novel reliable biomarkers. In this review, we aimed to provide a comprehensive overview on the most relevant available evidence on novel circulating biomarkers for early diagnosis, prognostic stratification, and therapeutic monitoring. Liquid biopsy seems to be a very promising instrument and, in the near future, some of these new non-invasive tools will probably change the clinical management of HCC patients.

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Next-Generation Biomarkers for Cholangiocarcinoma

Cancers ◽

10.3390/cancers13133222 ◽

2021 ◽

Vol 13 (13) ◽

pp. 3222

Author(s):

Pedro M. Rodrigues ◽

Arndt Vogel ◽

Marco Arrese ◽

Domingo C. Balderramo ◽

Juan W. Valle ◽

...

Keyword(s):

Biomarker Discovery ◽

Tumor Development ◽

Predictive Biomarkers ◽

Disease Diagnosis ◽

Future Research ◽

Tumor Biopsy ◽

Non Invasive ◽

Tumor Stages ◽

At Risk Populations ◽

Early Tumor

The increasing mortality rates of cholangiocarcinoma (CCA) registered during the last decades are, at least in part, a result of the lack of accurate non-invasive biomarkers for early disease diagnosis, making the identification of patients who might benefit from potentially curative approaches (i.e., surgery) extremely challenging. The obscure CCA pathogenesis and associated etiological factors, as well as the lack of symptoms in patients with early tumor stages, highly compromises CCA identification and to predict tumor development in at-risk populations. Currently, CCA diagnosis is accomplished by the combination of clinical/biochemical features, radiological imaging and non-specific serum tumor biomarkers, although a tumor biopsy is still needed to confirm disease diagnosis. Furthermore, prognostic and predictive biomarkers are still lacking and urgently needed. During the recent years, high-throughput omics-based approaches have identified novel circulating biomarkers (diagnostic and prognostic) that might be included in large, international validation studies in the near future. In this review, we summarize and discuss the most recent advances in the field of biomarker discovery in CCA, providing new insights and future research directions.

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Development and validation of blood-based predictive biomarkers for response to PD-(L)-1 checkpoint inhibitors: evidence of a universal systemic core of 3D immunogenetic profiling across multiple oncological indications.

10.1101/2021.12.21.21268094 ◽

2021 ◽

Author(s):

Ewan Hunter ◽

Mehrnoush Dizfouli ◽

Christina Koutsothanasi ◽

Adam Wilson ◽

Francisco Coroado Santos ◽

...

Keyword(s):

Immune Checkpoint ◽

Immune Checkpoint Inhibitors ◽

Checkpoint Inhibitors ◽

Predictive Biomarkers ◽

Checkpoint Inhibition ◽

Brain Lymphoma ◽

3D Genome ◽

Clinical Assay ◽

Genomic Approach ◽

Development And Validation

Unprecedented advantages in cancer treatment with immune checkpoint inhibitors (ICI) remain limited to a subset of patients. Systemic analyses of the regulatory 3D genome architecture linked to individual epigenetics and immunogenetic controls associated with tumour immune evasion mechanisms and immune checkpoint pathways reveals a highly prevalent patient molecular profiles predictive of response to PD-(L)1 immune checkpoint inhibitors. A clinical blood test based on the set of 8 3D genomic biomarkers has been developed and validated on several independent cancer patient cohorts to predict response to PD-(L)1 immune checkpoint inhibition. The predictive 8 biomarker set is derived from prospective observational clinical trials, representing 229 treatments with Pembrolizumab, Atezolizumab, Durvalumab, in diverse indications: melanoma, non-small cell lung, urethral, hepatocellular, bladder, prostate cancer, head and neck, vulvar, colon, breast, bone, brain, lymphoma, larynx cancer, and cervix cancers. The 3D genomic 8 biomarker panel for response to immune checkpoint therapy achieved high accuracy up to 85%, sensitivity of 93% and specificity of 82%. This study demonstrates that a 3D genomic approach could be used to develop a predictive clinical assay for response to PD-(L)1 checkpoint inhibition in cancer patients.

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240 Discovery of biomarkers of resistance to immune checkpoint blockade in non-small-cell lung cancer (NSCLC) using high-plex digital spatial profiling

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2021-sitc2021.240 ◽

2021 ◽

Vol 9 (Suppl 3) ◽

pp. A258-A258

Author(s):

Myrto Moutafi ◽

Sandra Martinez-Morilla ◽

Prajan Divakar ◽

Ioannis Vathiotis ◽

Niki Gavrielatou ◽

...

Keyword(s):

Lung Cancer ◽

Immune Checkpoint ◽

Biomarker Discovery ◽

Univariate Analysis ◽

Predictive Biomarkers ◽

Protein Detection ◽

Statistical Testing ◽

Multiple Testing Correction ◽

Stromal Compartment ◽

Pre Treatment

BackgroundDespite the clinical effectiveness of Immune Checkpoint Inhibitors (ICI) in lung cancer, only around 20% remain disease free at 5 years. Predictive biomarkers for ICIs are neither sensitive nor specific. Here, we used the GeoMx Digital Spatial Profiler (DSP) (NanoString, Inc.) to analyze high-plex protein in a quantitative and spatially resolved manner from single formalin-fixed paraffin embedded tissue sections toward the goal of identification of new biomarkers with better predictive value.MethodsPre-treatment samples from 56 patients with NSCLC treated with ICI were collected, represented in Yale tissue microarray 471 (YTMA471), and analyzed. A panel of 71 photocleavable oligonucleotide-labeled primary antibodies (NanoString Human IO panel) was used for protein detection. Protein expression was measured in 4 molecularly defined tissue compartments, defined by fluorescence co-localization (tumor [panCK+], leukocytes [CD45+/CD68-], macrophages [CD68+] and an aggregate stromal immune cell compartment, defined as the sum of leukocyte and macrophage expression [panCK-/CD45+/CD68+]) generating 284 variables representing potential predictive biomarkers. Promising candidates were orthogonally validated with Quantitative Immunofluorescence (QIF). Pre-treatment samples from 40 patients with NSCLC (YTMA404) that received ICI, and 174 non-ICI treated operable NSCLC patients (YTMA423) were analyzed to provide independent cohort validation. All statistical testing was performed using a two-sided significance level of α=0.05 and multiple testing correction (Benjamini-Hochberg method, FDR < 0.1).ResultsInitial biomarker discovery on 284 protein variables were generated by univariate analysis using continuous log-scaled data. High PD-L1 expression in tumor cells predicted longer survival (PFS; HR 0.67, p=0.017) and validated the training cohort. We found 4 markers associated with PFS, and 3 with OS in the stromal compartment. Of these, expression of CD66b in stromal immune cells predicted significantly shorter OS (HR 1.31, p=0.016) and shorter PFS (HR 1.24, p = 0.04). Tertile analysis using QIF on all three tissue cohorts for CD66b expression, assessed by QIF, showed that CD66b was indicative but not prognostic for survival [discovery cohort, YTMA471 (OS; HR 3.02, p=0.013, PFS; HR 2.38, p=0.023), validation cohort; YTMA404 (OS; HR 2.97, p=0.018, PFS; HR 1.85, p=0.1), non-ICI treated cohort YTMA423 (OS; HR 1.02, p>0.9, PFS; HR 0.72, p=0.4)].ConclusionsUsing the DSP technique, we have discovered that CD66b expressed in the stromal immune [panCK-/CD45+/CD68+] molecular compartment is associated with resistance to ICI therapy in NSCLC. This observation was validated by an orthogonal approach in an independent ICI treated NSCLC cohort. Since CD66b identifies neutrophils, further studies are warranted to characterize the role of neutrophils in ICI resistance.AcknowledgementsDr Moutafi is supported by a scholarship from the Hellenic Society of Medical Oncologists (HESMO)Ethics ApprovalAll tissue samples were collected and used under the approval from the Yale Human Investigation Committee protocol #9505008219 with an assurance filed with and approved by the U.S. Department of Health and Human Services

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Proteomics in Melanoma Biomarker Discovery: Great Potential, Many Obstacles

International Journal of Proteomics ◽

10.1155/2011/181890 ◽

2011 ◽

Vol 2011 ◽

pp. 1-8 ◽

Cited By ~ 15

Author(s):

Michael S. Sabel ◽

Yashu Liu ◽

David M. Lubman

Keyword(s):

Biomarker Discovery ◽

Predictive Biomarkers ◽

Clinical Staging ◽

Fresh Tissue ◽

Primary Tumors ◽

Unique Challenge ◽

Advantages And Disadvantages ◽

Serum Proteomics ◽

Therapeutic Decisions ◽

Large Populations

The present clinical staging of melanoma stratifies patients into heterogeneous groups, resulting in the application of aggressive therapies to large populations, diluting impact and increasing toxicity. To move to a new era of therapeutic decisions based on highly specific tumor profiling, the discovery and validation of new prognostic and predictive biomarkers in melanoma is critical. Genomic profiling, which is showing promise in other solid tumors, requires fresh tissue from a large number of primary tumors, and thus faces a unique challenge in melanoma. For this and other reasons, proteomics appears to be an ideal choice for the discovery of new melanoma biomarkers. Several approaches to proteomics have been utilized in the search for clinically relevant biomarkers, but to date the results have been relatively limited. This article will review the present work using both tissue and serum proteomics in the search for melanoma biomarkers, highlighting both the relative advantages and disadvantages of each approach. In addition, we review several of the major obstacles that need to be overcome in order to advance the field.

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Survival-Associated Metabolic Genes in Human Papillomavirus-Positive Head and Neck Cancers

Cancers ◽

10.3390/cancers12010253 ◽

2020 ◽

Vol 12 (1) ◽

pp. 253 ◽

Cited By ~ 6

Author(s):

Martin A. Prusinkiewicz ◽

Steven F. Gameiro ◽

Farhad Ghasemi ◽

Mackenzie J. Dodge ◽

Peter Y. F. Zeng ◽

...

Keyword(s):

Human Papillomavirus ◽

Head And Neck ◽

Patient Survival ◽

Tricarboxylic Acid Cycle ◽

Predictive Biomarkers ◽

Head And Neck Cancers ◽

The Cancer Genome Atlas ◽

Metabolic Genes ◽

Hpv Status ◽

The Impact

Human papillomavirus (HPV) causes an increasing number of head and neck squamous cell carcinomas (HNSCCs). Altered metabolism contributes to patient prognosis, but the impact of HPV status on HNSCC metabolism remains relatively uncharacterized. We hypothesize that metabolism-related gene expression differences unique to HPV-positive HNSCC influences patient survival. The Cancer Genome Atlas RNA-seq data from primary HNSCC patient samples were categorized as 73 HPV-positive, 442 HPV-negative, and 43 normal-adjacent control tissues. We analyzed 229 metabolic genes and identified numerous differentially expressed genes between HPV-positive and negative HNSCC patients. HPV-positive carcinomas exhibited lower expression levels of genes involved in glycolysis and higher levels of genes involved in the tricarboxylic acid cycle, oxidative phosphorylation, and β-oxidation than the HPV-negative carcinomas. Importantly, reduced expression of the metabolism-related genes SDHC, COX7A1, COX16, COX17, ELOVL6, GOT2, and SLC16A2 were correlated with improved patient survival only in the HPV-positive group. This work suggests that specific transcriptional alterations in metabolic genes may serve as predictive biomarkers of patient outcome and identifies potential targets for novel therapeutic intervention in HPV-positive head and neck cancers.

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Patient-Derived Xenograft and Organoid Models for Precision Medicine Targeting of the Tumour Microenvironment in Head and Neck Cancer

Cancers ◽

10.3390/cancers12123743 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3743

Author(s):

Tet Woo Lee ◽

Amy Lai ◽

Julia K. Harms ◽

Dean C. Singleton ◽

Benjamin D. Dickson ◽

...

Keyword(s):

Head And Neck ◽

Precision Medicine ◽

Patient Survival ◽

Predictive Biomarkers ◽

Tumour Microenvironment ◽

Response To Therapy ◽

Preclinical Models ◽

Therapeutic Success ◽

Patient Derived Xenograft ◽

Individualise Therapy

Patient survival from head and neck squamous cell carcinoma (HNSCC), the seventh most common cause of cancer, has not markedly improved in recent years despite the approval of targeted therapies and immunotherapy agents. Precision medicine approaches that seek to individualise therapy through the use of predictive biomarkers and stratification strategies offer opportunities to improve therapeutic success in HNSCC. To enable precision medicine of HNSCC, an understanding of the microenvironment that influences tumour growth and response to therapy is required alongside research tools that recapitulate the features of human tumours. In this review, we highlight the importance of the tumour microenvironment in HNSCC, with a focus on tumour hypoxia, and discuss the fidelity of patient-derived xenograft and organoids for modelling human HNSCC and response to therapy. We describe the benefits of patient-derived models over alternative preclinical models and their limitations in clinical relevance and how these impact their utility in precision medicine in HNSCC for the discovery of new therapeutic agents, as well as predictive biomarkers to identify patients’ most likely to respond to therapy.

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