Immune-enrichment of non-small cell lung cancer baseline biopsies for multiplex profiling define prognostic immune checkpoint combinations for patient stratification

Biomarker development for PD-(L)1 axis inhibition: a consensus view from the SITC Biomarkers Committee

Therapies targeting the programmed cell death protein-1/programmed death-ligand 1 (PD-L1) (abbreviated as PD-(L)1) axis are a significant advancement in the treatment of many tumor types. However, many patients receiving these agents fail to respond or have an initial response followed by cancer progression. For these patients, while subsequent immunotherapies that either target a different axis of immune biology or non-immune combination therapies are reasonable treatment options, the lack of predictive biomarkers to follow-on agents is impeding progress in the field. This review summarizes the current knowledge of mechanisms driving resistance to PD-(L)1 therapies, the state of biomarker development along this axis, and inherent challenges in future biomarker development for these immunotherapies. Innovation in the development and application of novel biomarkers and patient selection strategies for PD-(L)1 agents is required to accelerate the delivery of effective treatments to the patients most likely to respond.

New Achievements from Molecular Biology and Treatment Options for Refractory/Relapsed Ovarian Cancer-A Systematic Review

Ovarian cancer (OC) has a high rate of mortality and is the fifth most common cause of death in females all over the world. The etiology is still unclear. Numerous factors such as smoking, obesity, and unhealthy diet may affect the risk of OC. Having a family history of breast and OC is one of the main risks for developing OC. Mutations of BRCA1/2 are associated with OC risk as well. The histopathological classification of OC reveals the four most common types: serous, clear cell, endometrioid, and mucinous; these are epithelial OC types, and other types are rare. Furthermore, OC can be subdivided into types I and II. Type I tumors are most probably caused by atypical proliferative tumors. Type II tumors include high-grade carcinoma of the serous type, carcinosarcoma, and carcinoma, which are not differentiated and generally originate from tubal intraepithelial carcinoma of the serous type. Typically, type I tumors are present in early stages, usually with good prognosis. Type II tumors are classified as high-grade tumors and are most often diagnosed at advanced FIGO stages with poor prognosis. High-grade serous OC accounts for 90% of serous OC. Tumor heterogeneity aggravates OC treatment. The standard care for primary epithelial ovarian cancer (EOC) is cytoreductive surgery followed by platinum-based chemotherapy. Neoadjuvant chemotherapy can be used in certain cases followed by cytoreductive surgery. The main prognostic factor is complete tumor resection. However, about 70% of patients relapse. Resistance to chemotherapeutic agents remains a major challenge in EOC treatment, in which many different factors are involved. In recent years, the examination of molecular parameters and their prognostic impact has become increasingly relevant in EOC, and furthermore, the use of immunotherapy has expanded the therapeutic range. As the clinical need is greatest for relapsed patients, this systematic review will focus on recent advances in molecular biology with prognostic and predictive markers and treatment options for recurrent/refractory OC. Inclusion criteria for the review: potential prospective or predictive biomarkers in preclinical or clinical use in relapsed and refractory OC, prognostic impact, clinical and preclinical trials, and immunotherapy. Exclusion criteria for the review: primary OC, no full text or abstract available, not the topic mentioned above, and text not available in English. Risk of bias: the included studies were evaluated descriptively for the topics mentioned above, and data were not compared with each other. The objective is to highlight the molecular mechanisms of the most promising targeted agents under clinical investigation to demonstrate their potential relevance in recurrent/refractory OC.

Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer

The tumor immune composition influences prognosis and treatment sensitivity in lung cancer. The presence of effective adaptive immune responses is associated with increased clinical benefit after immune checkpoint blockers. Conversely, immunotherapy resistance can occur as a consequence of local T-cell exhaustion/dysfunction and upregulation of immunosuppressive signals and regulatory cells. Consequently, merely measuring the amount of tumor-infiltrating lymphocytes (TILs) may not accurately reflect the complexity of tumor-immune interactions and T-cell functional states and may not be valuable as a treatment-specific biomarker. In this work, we investigate an immune-related biomarker (PhenoTIL) and its value in associating with treatment-specific outcomes in non-small cell lung cancer (NSCLC). PhenoTIL is a novel computational pathology approach that uses machine learning to capture spatial interplay and infer functional features of immune cell niches associated with tumor rejection and patient outcomes. PhenoTIL's advantage is the computational characterization of the tumor immune microenvironment extracted from H&E-stained preparations. Association with clinical outcome and major non-small cell lung cancer (NSCLC) histology variants was studied in baseline tumor specimens from 1,774 lung cancer patients treated with immunotherapy and/or chemotherapy, including the clinical trial Checkmate 057 (NCT01673867).

Breaking the Invisible Barriers: Unleashing the Full Potential of Immune Checkpoint Inhibitors in Oncogene-Driven Lung Adenocarcinoma

The rapid development of targeted therapy paved the way toward personalized medicine for advanced non-small cell lung cancer (NSCLC). Lung adenocarcinoma (ADC) harboring actionable genetic alternations including epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), Kirsten rat sarcoma virus (ALK) and c-ros oncogene 1 (ROS1) treated with tyrosine kinase inhibitors (TKIs) incurred lesser treatment toxicity but better therapeutic responses compared with systemic chemotherapy. Angiogenesis inhibitors targeting vascular endothelial growth factor (VEGF) have also shown an increase in overall survival (OS) for NSCLC patients. However, acquired resistance to these targeted therapies remains a major obstacle to long-term maintenance treatment for lung ADC patients. The emergence of immune checkpoint inhibitors (ICIs) against programmed cell death protein 1 (PD-1) or programmed cell death-ligand 1 (PD-L1) has changed the treatment paradigm for NSCLC tumors without actionable genetic alternations. Clinical studies have suggested, however, that there are no survival benefits with the combination of targeted therapy and ICIs. In this review, we will summarize and discuss the current knowledge on the tumor immune microenvironment and the dynamics of immune phenotypes, which could be crucial in extending the applicability of ICIs for this subpopulation of lung ADC patients.

Lung adenocarcinoma patients with malignant pleural effusions in hot adaptive immunity status have a longer overall survival

Malignant pleural effusion (MPE) is a common complication of lung adenocarcinoma (LADC) which is associated with a dismal prognosis. We investigated the prognostic role of PD-L1 and other immunomodulators expression in the immune compartment of MPE immune composition. MPE cytologic cell blocks of 83 LADC patients were analysed for the mRNA expression of 770 cancer-immune genes by the NanoString nCounter platform. The expression of relevant immune cell lineage markers was validated by immunohistochemistry (IHC) using quantitative pathology. The mRNA immune profiling identified four MPE patient clusters (C). C1/2 (adaptive+, hot) showed better overall survival (OS) than C3/4 (adaptive-, cold). Additionally, cold immunity profiles (adaptive-), C4 (innate+) were associated with worse OS than C3 (innate-). High PD-L1 expression was linked to the regulation of T cell activation and interferon signalling pathways. Genes of pattern recognition receptor and type I interferon signalling pathways were specifically upregulated in the long-survival (≥90 days) patient group. Moreover, immunomodulators were co-activated and highly expressed in hot adaptive immunity patient clusters, whereas CD274 (PD-L1), TNFRSF9 (4-1BB), VEGFA (VEGF-A) and CD276 (B7-H3) were upregulated in the groups referred as cold. The patient cluster, age and PD-L1 expression were independent prognosticators for LADC MPE patients (p-value < 0.05). Our study sheds light on the variances of immune contexture regarding different PD-L1 expression and survival conditions. It revealed four distinct prognostic patient clusters with specific immune cell components and immunomodulator expression profiles, which, collectively, is supportive for future therapeutic and prognosis for cancer management.

TP53 Status, Patient Sex, and the Immune Response as Determinants of Lung Cancer Patient Survival

Lung cancer poses the greatest cancer-related death risk and males have poorer outcomes than females, for unknown reasons. Patient sex is not a biological variable considered in lung cancer standard of care. Correlating patient genetics with outcomes is predicted to open avenues for improved management. Using a bioinformatics approach across non-small cell lung cancer (NSCLC) subtypes, we identified where patient sex, mutation of the major tumor suppressor gene, Tumour protein P53 (TP53), and immune signatures stratified outcomes in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), among datasets of The Cancer Genome Atlas (TCGA). We exposed sex and TP53 gene mutations as prognostic for LUAD survival. Longest survival in LUAD occurred among females with wild-type (wt) TP53 genes, high levels of immune infiltration and enrichment for pathway signatures of Interferon Gamma (INF-γ), Tumour Necrosis Factor (TNF) and macrophages-monocytes. In contrast, poor survival in men with LUAD and wt TP53 genes corresponded with enrichment of Transforming Growth Factor Beta 1 (TGFB1, hereafter TGF-β) and wound healing signatures. In LUAD with wt TP53 genes, elevated gene expression of immune checkpoint CD274 (hereafter: PD-L1) and also protein 53 (p53) negative-regulators of the Mouse Double Minute (MDM)-family predict novel avenues for combined immunotherapies. LUSC is dominated by male smokers with TP53 gene mutations, while a minor population of TCGA LC patients with wt TP53 genes unexpectedly had the poorest survival, suggestive of a separate etiology. We conclude that advanced approaches to LUAD and LUSC therapy lie in the consideration of patient sex, TP53 gene mutation status and immune signatures.

Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer

Two graph theoretic concepts—clique and bipartite graphs—are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After initiation, the disease signal goes to the next group of genes related to the second stage of a cancer, which can be represented as a bipartite graph. In other words, bipartite graphs represent the cross-talk among the genes between two disease stages. To prove this hypothesis, gene expression values for three cancers— breast invasive carcinoma (BRCA), colorectal adenocarcinoma (COAD) and glioblastoma multiforme (GBM)—are used for analysis. First, a co-expression gene network is generated with highly correlated gene pairs with a Pearson correlation coefficient ≥ 0.9. Second, clique structures of all sizes are isolated from the co-expression network. Then combining these cliques, three different biomarker modules are developed—maximal clique-like modules, 2-clique-1-bipartite modules, and 3-clique-2-bipartite modules. The list of biomarker genes discovered from these network modules are validated as the essential genes for causing a cancer in terms of network properties and survival analysis. This list of biomarker genes will help biologists to design wet lab experiments for further elucidating the complex mechanism of cancer.

Failed immune responses across multiple pathologies share pan-tumor and circulating lymphocytic targets

Rationale Tumor infiltrating lymphocytes are widely associated with positive outcomes, yet carry key indicators of a systemic failed immune response against unresolved cancer. Cancer immunotherapies can reverse their tolerance phenotypes, while preserving tumor-reactivity and neoantigen-specificity shared with circulating immune cells. Objectives We performed comprehensive transcriptomic analyses to identify gene signatures common to circulating and tumor infiltrating lymphocytes in the context of clear cell renal cell carcinoma. Modulated genes also associated with disease outcome were validated in other cancer types. Findings Using bioinformatics, we identified practical diagnostic markers and actionable targets of the failed immune response. On circulating lymphocytes, three genes, LEF1, FASLG, and MMP9, could efficiently stratify patients from healthy control donors. From their associations with resistance to cancer immunotherapies and microbial infections, we uncovered not only pan-cancer, but pan-pathology failed immune response profiles. A prominent lymphocytic matrix metallopeptidase cell migration pathway, is central to a panoply of diseases and tumor immunogenicity, correlates with multi-cancer recurrence, and identifies a feasible, non-invasive approach to pan-pathology diagnoses. Conclusions The non-invasive differently expressed genes we have identified warrant future investigation towards the development of their potential in precision diagnostics and precision pan-disease immunotherapeutics.