Infiltrating inflammatory cells are highly prevalent within the tumor microenvironment and mediate many processes associated with tumor progression; however, the contribution of specific populations remains unclear. For example, the nature and function of tumor-associated neutrophils (TANs) in the cancer microenvironment is largely unknown. The goal of this study was to provide a phenotypic and functional characterization of TANs in surgically resected lung cancer patients. We found that TANs constituted 5%-25% of cells isolated from the digested human lung tumors. Compared with blood neutrophils, TANs displayed an activated phenotype (CD62L(lo)CD54(hi)) with a distinct repertoire of chemokine receptors that included CCR5, CCR7, CXCR3, and CXCR4. TANs produced substantial quantities of the proinflammatory factors MCP-1, IL-8, MIP-1α, and IL-6, as well as the antiinflammatory IL-1R antagonist. Functionally, both TANs and neutrophils isolated from distant nonmalignant lung tissue were able to stimulate T cell proliferation and IFN-γ release. Cross-talk between TANs and activated T cells led to substantial upregulation of CD54, CD86, OX40L, and 4-1BBL costimulatory molecules on the neutrophil surface, which bolstered T cell proliferation in a positive-feedback loop. Together our results demonstrate that in the earliest stages of lung cancer, TANs are not immunosuppressive, but rather stimulate T cell responses.

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Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous largescale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.Cancer forms and progresses through a series of critical transitions-from pre-malignant to malignant states, from locally contained to metastatic disease, and from treatment-responsive to treatment-resistant tumors (Figure 1). Although specifics differ across tumor types and patients, all transitions involve complex dynamic interactions between diverse pre-malignant, malignant, and non-malignant cells (e.g., stroma cells and immune cells), often organized in specific patterns within the tumor

Purpose The expanding number of targeted therapeutics for non-small cell lung cancer (NSCLC) necessitates real-time tumor genotyping, yet tissue biopsies are difficult to perform serially and often yield inadequate DNA for next-generation sequencing (NGS). We evaluated the feasibility of using cell-free circulating tumor DNA (ctDNA) NGS as a complement or alternative to tissue NGS. Experimental Design 112 plasma samples obtained from a consecutive study of 102 prospectively enrolled patients with advanced NSCLC were subjected to ultra-deep sequencing of up to 70 genes and matched with tissue samples, when possible. Results We detected 275 alterations in 45 genes, and at least one alteration in the ctDNA for 86 of 102 patients (84%), with EGFR variants being most common. ctDNA NGS detected 50 driver and 12 resistance mutations, and mutations in 22 additional genes for which experimental therapies, including clinical trials, are available. While ctDNA NGS was completed for 102 consecutive patients, tissue sequencing was only successful for 50 patients (49%). Actionable EGFR mutations were detected in 24 tissue and 19 ctDNA samples, yielding concordance of 79%, with a shorter time interval between tissue and blood collection associated with increased concordance (p=0.038). ctDNA sequencing identified 8 patients harboring a resistance mutation who developed progressive disease while on targeted therapy, and for whom tissue sequencing wasn’t possible. Conclusions Therapeutically targetable driver and resistance mutations can be detected by ctDNA NGS, even when tissue is unavailable, thus allowing more accurate diagnosis, improved patient management, and serial sampling to monitor disease progression and clonal evolution.

Lung cancer is the leading cause of cancer death in the U.S. with survival restricted to a subset of those patients able to undergo surgical resection. However, even with surgery, recurrence rates range from 30% to 60%, depending on the pathologic stage. With the advent of partially effective, but potentially toxic adjuvant chemotherapy, it has become increasingly important to discover biomarkers that will identify those patients who have the highest likelihood of recurrence and who thus might benefit most from adjuvant chemotherapy. Hundreds of papers have appeared over the past several decades proposing a variety of molecular markers or proteins that may have prognostic significance in non^small cell lung cancer. This review analyzes the largest and most rigorous of these studies with the aim of compiling the most important prognostic markers in early stage non^small cell lung cancer. In this review, we focused on biomarkers primarily involved in one of three major pathways: cell cycle regulation, apoptosis, and angiogenesis. Although no single marker has yet been shown to be perfect in predicting patient outcome, a profile based on the best of these markers may prove useful in directing patient therapy. The markers with the strongest evidence as independent predictors of patient outcome include cyclin E, cyclin B1, p21, p27, p16, survivin, collagen XVIII, and vascular endothelial cell growth factor.