Epidermal growth factor receptor (EGFR) mutations typically occur in exons 18–21 and are established driver mutations in non-small cell lung cancer (NSCLC)^1–3. Targeted therapies are approved for patients with ‘classical’ mutations and a small number of other mutations^4–6. However, effective therapies have not been identified for additional EGFR mutations. Furthermore, the frequency and effects of atypical EGFR mutations on drug sensitivity are unknown^1,3,7–10. Here we characterize the mutational landscape in 16,715 patients with EGFR-mutant NSCLC, and establish the structure–function relationship of EGFR mutations on drug sensitivity. We found that EGFR mutations can be separated into four distinct subgroups on the basis of sensitivity and structural changes that retrospectively predict patient outcomes following treatment with EGFR inhibitors better than traditional exon-based groups. Together, these data delineate a structure-based approach for defining functional groups of EGFR mutations that can effectively guide treatment and clinical trial choices for patients with EGFR-mutant NSCLC and suggest that a structure–function-based approach may improve the prediction of drug sensitivity to targeted therapies in oncogenes with diverse mutations.

Limited clinical data are available regarding the efficacy of EGFR tyrosine kinase inhibitors (EGFR TKIs) in patients with NSCLC harboring uncommon EGFR mutations. This pooled analysis assessed the activity of afatinib in 693 patients with tumors harboring uncommon EGFR mutations treated in randomized clinical trials, compassionate-use and expanded-access programs, phase IIIb trials, noninterventional trials, and case series or studies. Patients had uncommon EGFR mutations, which were categorized as follows: (1) T790M; (2) exon 20 insertions; (3) "major" uncommon mutations (G719X, L861Q, and S768I, with or without any other mutation except T790M or an exon 20 insertion); (4) compound mutations; and (5) other uncommon mutations. Key end points were overall response rate (ORR), duration of response, and time to treatment failure (TTF). In EGFR TKI-naive patients (n = 315), afatinib demonstrated activity against major uncommon mutations (median TTF = 10.8 mo; 95% confidence interval [CI]: 8.1-16.6; ORR = 60.0%), compound mutations (median TTF = 14.7 mo; 95% CI: 6.8-18.5; ORR = 77.1%), other uncommon mutations (median TTF = 4.5 mo; 95% CI: 2.9-9.7; ORR = 65.2%), and some exon 20 insertions (median TTF = 4.2 mo; 95% CI: 2.8-5.3; ORR = 24.3%). The median duration of response for major uncommon mutations, compound mutations, other uncommon mutations, and some exon 20 insertions was 17.1, 16.6, 9.0, and 11.9 months, respectively. Activity of afatinib was also observed in EGFR TKI-pretreated patients (n = 378). A searchable database of these outcomes by individual genotype was generated. Afatinib has clinical activity in NSCLC against major uncommon and compound EGFR mutations. It also has broad activity against other uncommon EGFR mutations and some exon 20 insertions. The data support the use of afatinib in these settings.

The emergence of immunotherapy in oncology requires the discovery, validation and subsequent adoption of robust, sensitive and specific predictive and prognostic biomarkers for daily practice. Until now, anti-PD-L1 immunohistochemistry (IHC) on tissue sections has been the only validated companion diagnostic test for first-line immunotherapy for advanced and metastatic cancer, notably non-small-cell lung cancer (NSCLC). However, detection of this biomarker presents limitations that have stimulated the development of other biomarkers and other approaches. Within this context, the use of a liquid biopsy (LB) could provide an important complementary or alternative added value to PD-L1 IHC. In this review, we discuss how LBs have been used in the field of immuno-oncology (I-O) to predict response, relapse or adverse advents for patients undergoing immune-checkpoint inhibitor (ICI) therapy (anti-PD-1/PD-L1 and CTLA-4) and we highlight recent developments. Circulating tumor cells (CTCs), cell-free DNA (cfDNA), proteins and cytokines detected in plasma as well as circulating T-lymphocytes are discussed as potential sources for developing new I-O biomarkers. The quantification of cfDNA as a predictive biomarker, as well as its sequencing for the determination of tumor mutational burden, is already well advanced. Additionally, the quantification of PD-L1 from CTCs, bound on exosomes or free in plasma, as well as the determination of cytokines, are also being actively investigated with promising results having recently been published. Lastly, analysis of T-lymphocytes, especially by analyzing the T-cell receptor, has recently emerged as a valuable biomarker that might become relevant for the prediction of response to ICIs. While LBs have not yet been implemented in routine I-O clinical practice, recent promising data and rapidly advancing technologies indicate that this approach has the potential to soon personalize the clinical management of cancer patients receiving ICIs.

Small cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, but implementing subtyping in the clinic has remained challenging, particularly due to limited tissue availability. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that subtype-specific patterns of DNA methylation could be detected in tumor or blood from SCLC patients. Using genomic-wide reduced-representation bisulfite sequencing (RRBS) in two cohorts totaling 179 SCLC patients and using machine learning approaches, we report a highly accurate DNA methylation-based classifier (SCLC-DMC) that can distinguish SCLC subtypes. We further adjust the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC), we demonstrate that SCLC phenotypes can evolve during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and might guide precision SCLC therapy.

Commonly, circulating tumor cells (CTCs) are described as source of metastasis in cancer patients. However, in this process cancer cells of the primary tumor site need to survive the physical and biological challenges in the blood stream before leaving the circulation to become the seed of a new metastatic site in distant parenchyma. Most of the CTCs released in the blood stream will not resist those challenges and will consequently fail to induce metastasis. A few of them, however, interact closely with other blood cells, such as neutrophils, platelets, and/or macrophages to survive in the blood stream. Recent studies demonstrated that the interaction and modulation of the blood microenvironment by CTCs is pivotal for the development of new metastasis, making it an interesting target for potential novel treatment strategies. This review will discuss the recent research on the processes in the blood microenvironment with CTCs and will outline currently investigated treatment strategies.