Immunotherapy, including use of checkpoint inhibitors against PD-1, PD-L1, and CTLA-4, forms the backbone of oncologic management for the majority of non-small cell lung carcinoma patients. However, ...response to these therapies varies widely, from patients who have complete resolution of metastatic disease and long-term remission, to those who rapidly progress and succumb to their cancer despite use of the newest checkpoint inhibitors. While PD-L1 protein expression by immunohistochemistry serves as the principle predictive biomarker for immunotherapy response, neither the sensitivity nor the specificity of this approach is optimal, and clinical PD-L1 testing is plagued by concerns around result reproducibility and confusion born from the proliferation of different companion diagnostic assays. At the same time, insights into tumor and host immune-specific factors that inform both prognosis and response prediction are beginning to define better immunotherapy biomarkers. Beyond immune checkpoint expression status, common themes in analyses of immunotherapy response prediction include cancer neoantigen production, the state of the antigen presentation pathway in both tumor and antigen presenting cells, the admixture of effector and suppressor immune cells in the tumor microenvironment, and the genomic drivers and comutations that can influence the all of these variables. This review will address the state of PD-L1 testing in lung cancer, the role for tumor mutation burden as a predictive biomarker, the evolving status of human leukocyte antigen/major histocompatibility complex expression as a marker of antigen presentation, approaches to tumor immune cell quantitation including by multiplex immunofluorescence, and the importance of tumor genomic profiling to ascertain oncogenic driver (EGFR, ALK, KRAS, MET, etc.) and co-mutation (STK11, KEAP1, SMARCA4) status.
Stromal cells within the tumor microenvironment are essential for tumor progression and metastasis. Surprisingly little is known about the factors that drive the transcriptional reprogramming of ...stromal cells within tumors. We report that the transcriptional regulator heat shock factor 1 (HSF1) is frequently activated in cancer-associated fibroblasts (CAFs), where it is a potent enabler of malignancy. HSF1 drives a transcriptional program in CAFs that complements, yet is completely different from, the program it drives in adjacent cancer cells. This CAF program is uniquely structured to support malignancy in a non-cell-autonomous way. Two central stromal signaling molecules—TGF-β and SDF1—play a critical role. In early-stage breast and lung cancer, high stromal HSF1 activation is strongly associated with poor patient outcome. Thus, tumors co-opt the ancient survival functions of HSF1 to orchestrate malignancy in both cell-autonomous and non-cell-autonomous ways, with far-reaching therapeutic implications.
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•Reprogramming of cancer-associated fibroblasts by HSF1 enables malignant progression•Distinct transcriptional programs are driven by HSF1 in stromal versus malignant cells•HSF1 activation in tumor stroma is strongly associated with poor patient outcome•Dual roles in both tumor cells and stroma make HSF1 an attractive anticancer target
HSF1 drives a transcriptional program in stromal cells that potentiates tumor cell malignancy and is associated with poor patient outcomes.
The analysis of molecular biomarkers in lung adenocarcinoma (ACA) is now a central component of pathologic diagnosis and oncologic care. The identification of an EGFR mutation or ALK rearrangement in ...advanced-stage lung ACA will dictate a change in first-line treatment from standard chemotherapy to targeted inhibition of these oncogenic alterations. Viable approaches to therapeutic targeting of KRAS-mutated ACA are now under investigation, raising the possibility that this too will become an important predictive marker in this tumor type. The recognized array of less common oncogenic alterations in lung ACA, including in the ROS1, RET, BRAF, and ERBB2 genes, is growing rapidly. The therapeutic implications of these findings are, in many cases, still under investigation.
To focus on the major molecular biomarkers in lung ACA, recommended testing strategies, the implications for targeted therapies, and the mechanisms that drive development of resistance.
Our current understanding of predictive and prognostic markers in lung ACA is derived from a decade of technical advances, clinical trials, and epidemiologic studies. Many of the newest discoveries have emerged from application of high-throughput next-generation sequencing and gene expression analyses in clinically and pathologically defined cohorts of human lung tumors.
Best practices require a solid understanding of relevant biomarkers for diagnosis and treatment of patients with lung ACA.
A grading system for pulmonary adenocarcinoma has not been established. The International Association for the Study of Lung Cancer pathology panel evaluated a set of histologic criteria associated ...with prognosis aimed at establishing a grading system for invasive pulmonary adenocarcinoma.
A multi-institutional study involving multiple cohorts of invasive pulmonary adenocarcinomas was conducted. A cohort of 284 stage I pulmonary adenocarcinomas was used as a training set to identify histologic features associated with patient outcomes (recurrence-free survival RFS and overall survival OS). Receiver operating characteristic curve analysis was used to select the best model, which was validated (n = 212) and tested (n = 300, including stage I–III) in independent cohorts. Reproducibility of the model was assessed using kappa statistics.
The best model (area under the receiver operating characteristic curve AUC = 0.749 for RFS and 0.787 for OS) was composed of a combination of predominant plus high-grade histologic pattern with a cutoff of 20% for the latter. The model consists of the following: grade 1, lepidic predominant tumor; grade 2, acinar or papillary predominant tumor, both with no or less than 20% of high-grade patterns; and grade 3, any tumor with 20% or more of high-grade patterns (solid, micropapillary, or complex gland). Similar results were seen in the validation (AUC = 0.732 for RFS and 0.787 for OS) and test cohorts (AUC = 0.690 for RFS and 0.743 for OS), confirming the predictive value of the model. Interobserver reproducibility revealed good agreement (k = 0.617).
A grading system based on the predominant and high-grade patterns is practical and prognostic for invasive pulmonary adenocarcinoma.
Characterizing the tumor immune microenvironment enables the identification of new prognostic and predictive biomarkers, the development of novel therapeutic targets and strategies, and the ...possibility to guide first-line treatment algorithms. Although the driving elements within the tumor microenvironment of individual primary organ sites differ, many of the salient features remain the same. The presence of a robust antitumor milieu characterized by an abundance of CD8+ cytotoxic T-cells, Th1 helper cells, and associated cytokines often indicates a degree of tumor containment by the immune system and can even lead to tumor elimination. Some of these features have been combined into an 'Immunoscore', which has been shown to complement the prognostic ability of the current TNM staging for early stage colorectal carcinomas. Features of the immune microenvironment are also potential therapeutic targets, and immune checkpoint inhibitors targeting the PD-1/PD-L1 axis are especially promising. FDA-approved indications for anti-PD-1/PD-L1 are rapidly expanding across numerous tumor types and, in certain cases, are accompanied by companion or complimentary PD-L1 immunohistochemical diagnostics. Pathologists have direct visual access to tumor tissue and in-depth knowledge of the histological variations between and within tumor types and thus are poised to drive forward our understanding of the tumor microenvironment. This review summarizes the key components of the tumor microenvironment, presents an overview of and the challenges with PD-L1 antibodies and assays, and addresses newer candidate biomarkers, such as CD8+ cell density and mutational load. Characteristics of the local immune contexture and current pathology-related practices for specific tumor types are also addressed. In the future, characterization of the host antitumor immune response using multiplexed and multimodality biomarkers may help predict which patients will respond to immune-based therapies.
Upfront tumour genotyping is now considered an essential step in guiding treatment decision-making in the management of patients with advanced-stage non-small-cell lung cancer (NSCLC) in light of the ...ever-expanding toolbox of targeted therapies and immune-checkpoint inhibitors. However, genotyping of tumour biopsy samples is not feasible for all patients and, therefore, genomic analysis of circulating tumour DNA (ctDNA) has emerged as a compelling non-invasive option. Current guidelines universally recommend genotyping and support the use of ctDNA testing in certain settings, although they often omit the detail necessary for integrating these tests into clinical care on an individual basis. In this Perspective, we describe the rationale, promise and challenges associated with ctDNA-based NSCLC genotyping and suggest a framework for the implementation of these assays into routine clinical practice. We also offer considerations for the interpretation of ctDNA genotyping results, which, particularly when using next-generation sequencing panels, can be nuanced. Through the addition of this new approach to clinical practice, we propose that oncologists might finally be able to utilize effective genotyping in nearly all patients with advanced-stage NSCLC.
Immune-checkpoint inhibitors targeting PD-1 or PD-L1 have already substantially improved the outcomes of patients with many types of cancer, although only 20-40% of patients derive benefit from these ...new therapies. PD-L1, quantified using immunohistochemistry assays, is currently the most widely validated, used and accepted biomarker to guide the selection of patients to receive anti-PD-1 or anti-PD-L1 antibodies. However, many challenges remain in the clinical use of these assays, including the necessity of using different companion diagnostic assays for specific agents, high levels of inter-assay variability in terms of both performance and cut-off points, and a lack of prospective comparisons of how PD-L1
disease diagnosed using each assay relates to clinical outcomes. In this Review, we describe the current role of PD-L1 immunohistochemistry assays used to inform the selection of patients to receive anti-PD-1 or anti-PD-L1 antibodies, we discuss the various technical and clinical challenges associated with these assays, including regulatory issues, and we provide some perspective on how to optimize PD-L1 as a selection biomarker for the future treatment of patients with solid tumours.
STK11 and KEAP1 mutations (STK11 mutant STK11MUT and KEAP1MUT) are among the most often mutated genes in lung adenocarcinoma (LUAD). Although STK11MUT has been associated with resistance to ...programmed death-(ligand)1 (PD-L1) inhibition in KRASMUT LUAD, its impact on immunotherapy efficacy in KRAS wild-type (KRASWT) LUAD is currently unknown. Whether KEAP1MUT differentially affects outcomes to PD-(L)1 inhibition in KRASMUT and KRASWT LUAD is also unknown.
Clinicopathologic and genomic data were collected from September 2013 to September 2020 from patients with advanced LUAD at the Dana-Farber Cancer Institute/Massachusetts General Hospital cohort and the Memorial Sloan Kettering Cancer Center/MD Anderson Cancer Center cohort. Clinical outcomes to PD-(L)1 inhibition were analyzed according to KRAS, STK11, and KEAP1 mutation status in two independent cohorts. The Cancer Genome Atlas transcriptomic data were interrogated to identify differences in tumor gene expression and tumor immune cell subsets, respectively, according to KRAS/STK11 and KRAS/KEAP1 comutation status.
In the combined cohort (Dana-Farber Cancer Institute/Massachusetts General Hospital + Memorial Sloan Kettering Cancer Center/MD Anderson Cancer Center) of 1261 patients (median age = 61 y range: 22–92, 708 women 56.1%, 1065 smokers 84.4%), KRAS mutations were detected in 536 cases (42.5%), and deleterious STK11 and KEAP1 mutations were found in 20.6% (260 of 1261) and 19.2% (231 of 1202) of assessable cases, respectively. In each independent cohort and in the combined cohort, STK11 and KEAP1 mutations were associated with significantly worse progression-free (STK11 hazard ratio HR = 2.04, p < 0.0001; KEAP1 HR = 2.05, p < 0.0001) and overall (STK11 HR = 2.09, p < 0.0001; KEAP1 HR = 2.24, p < 0.0001) survival to immunotherapy uniquely among KRASMUT but not KRASWT LUADs. Gene expression ontology and immune cell enrichment analyses revealed that the presence of STK11 or KEAP1 mutations results in distinct immunophenotypes in KRASMUT, but not in KRASWT, lung cancers.
STK11 and KEAP1 mutations confer worse outcomes to immunotherapy among patients with KRASMUT but not among KRASWT LUAD. Tumors harboring concurrent KRAS/STK11 and KRAS/KEAP1 mutations display distinct immune profiles in terms of gene expression and immune cell infiltration.
The relationship of SARS-CoV-2 pulmonary infection and severity of disease is not fully understood. Here we show analysis of autopsy specimens from 24 patients who succumbed to SARS-CoV-2 infection ...using a combination of different RNA and protein analytical platforms to characterize inter-patient and intra-patient heterogeneity of pulmonary virus infection. There is a spectrum of high and low virus cases associated with duration of disease. High viral cases have high activation of interferon pathway genes and a predominant M1-like macrophage infiltrate. Low viral cases are more heterogeneous likely reflecting inherent patient differences in the evolution of host response, but there is consistent indication of pulmonary epithelial cell recovery based on napsin A immunohistochemistry and RNA expression of surfactant and mucin genes. Using a digital spatial profiling platform, we find the virus corresponds to distinct spatial expression of interferon response genes demonstrating the intra-pulmonary heterogeneity of SARS-CoV-2 infection.
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