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Living guidelines are developed for selected topic areas with rapidly evolving evidence that drives frequent change in recommended clinical practice. Living guidelines are updated on a regular schedule by a standing expert panel that systematically reviews the health literature on a continuous basis, as described in the ASCO Guidelines Methodology Manual . ASCO Living Guidelines follow the ASCO Conflict of Interest Policy Implementation for Clinical Practice Guidelines . Living Guidelines and updates are not intended to substitute for independent professional judgment of the treating provider and do not account for individual variation among patients. See Appendix 1 (online only) for disclaimers and other important information. Updates are published regularly and can be found at https://ascopubs.org/nsclc-da-living-guideline .

Living guidelines are developed for selected topic areas with rapidly evolving evidence that drives frequent change in recommended clinical practice. Living guidelines are updated on a regular schedule by a standing expert panel that systematically reviews the health literature on a continuous basis, as described in the ASCO Guidelines Methodology Manual . ASCO Living Guidelines follow the ASCO Conflict of Interest Policy Implementation for Clinical Practice Guidelines . Living Guidelines and updates are not intended to substitute for independent professional judgment of the treating provider and do not account for individual variation among patients. See Appendix 1 (online only) for disclaimers and other important information. Updates are published regularly and can be found at https://ascopubs.org/nsclc-non-da-living-guideline .

Over the last decade or so, there has been a paradigm shift in the oncologic care of patients with a range of solid tumour and haematologic malignancies, away from traditional cytotoxic chemotherapy and towards personalized cancer treatments, using both targeted therapy and immunotherapy. This shift has contributed to the remarkable and sustained increase in the number of cancer survivors and the longevity of patients with a cancer diagnosis. This review will focus on the cardiovascular effects of immune checkpoint inhibitors and will present a background on immune checkpoint inhibition for cancer, the epidemiology, potential mechanisms, the potential insights into cardiovascular biology, and a diagnostic and therapeutic approach to potential cases. Our understanding of the cardiovascular effects of immune checkpoint inhibitors needs to improve. However, the evolution necessarily needs to be rapid. Initial observations noted that immune checkpoint inhibitor therapy can lead to a fulminant myocarditis. Recent reports have expanded the effect of immune checkpoint inhibitor therapy on the cardiovascular system to include an increase in cardiac dysfunction without myocarditis, arrhythmias, venous thromboembolic disease, accelerated atherosclerosis, and atherosclerosis-related cardiovascular events. The association between immune checkpoint inhibitor therapy and an increase in these cardiovascular events is not only limited to events occurring within the first few weeks after starting therapy but can also include events that occur months to years after therapy. The latter observation is especially of relevance in those treated with adjuvant or neoadjuvant therapy. There needs to be a shift from recognition of an increase in cardiovascular events to currently approved immune checkpoint inhibitor therapies to understanding the mechanisms that lead to adverse cardiovascular effects, understanding who is at risk, and understanding what we can do about it.

Introduction Anti-PD-(L)1 immune checkpoint inhibitors (ICI) improve survival in patients with advanced non-small cell lung cancer (aNSCLC). The clinical features, survival, and burden of toxicities of patients with aNSCLC alive >1 year from ICI initiation are poorly understood. Materials and Methods We defined ICI survivors as patients alive >1 year after ICI start and retrospectively reviewed demographics, treatment, and immune-related adverse events (irAEs). Long-term irAEs were defined as ongoing irAEs lasting >1 year; burden of toxicity measures were based on percentage of days a patient experienced toxicity. Using linear and logistic regression, we evaluated association between demographics and disease characteristics with burden of toxicity. Results We identified 114 ICI survivors from 317 patients with aNSCLC. Half (52%) experienced an irAE of any grade, and 23.7% developed long-term irAEs. More ICI survivors with irAES in the first year had never smoked (P = .018) or received ICIs as frontline therapy (P = .015). The burden of toxicity in the first year significantly correlated with the burden of toxicity afterward (ρ = 0.72; P < .001). No patients with progressive disease had a high burden of toxicity, and they experienced 30.6% fewer days with toxicity than those with stable disease. Increased duration of therapy was associated with higher odds of experiencing toxicity. Half of ICI survivors with irAEs were still receiving treatment for unresolved irAEs at time of death or last follow-up. Conclusion Significant proportions of ICI survivors have unresolved long-term toxicities. These data support a growing need to understand long-term toxicity to optimize management of those treated with ICIs.

For patients with non–small-cell lung cancer (NSCLC), the outcomes for patients with resectable disease are historically poor compared with other solid organ malignancies. In recent years, there have been significant advances in multidisciplinary care, which have resulted in improved outcomes. Innovations in surgical oncology include the use of limited resection and minimally invasive techniques. Recent data in radiation oncology have suggested refinements in pre- and postoperative radiation therapy, resulting in optimization of techniques in the curative setting. Finally, the success of immune checkpoint inhibitors and targeted therapies in the advanced setting has paved the way for inclusion in the adjuvant and neoadjuvant settings, resulting in recent regulatory approvals for four regimens (CheckMate-816, IMpower010, PEARLS, ADAURA). In this review, we will provide an overview of the seminal studies informing advancements in optimal surgical resection, radiation treatment, and systemic therapy for resectable NSCLC. We will summarize the key data on survival outcomes, biomarker analyses, and future directions for perioperative studies.

Supplementary Figure S1. Computed Tomographic (CT) findings in patient CGLU116. Supplementary Figure S2. CT findings in patient CGLU127. Supplementary Figure S3. CT findings in patient CGLU161. Supplementary Figure S4. Tumor burden kinetics. Supplementary Figure S5. Neoantigen-specific TCR expansion in stimulated T cell cultures for patient CGLU127. Supplementary Figure S6. Neoantigen-specific TCR expansion in stimulated T cell cultures for patient CGLU161. Supplementary Figure S7. Loss of heterozygosity analyses for patient CGLU116. Supplementary Figure S8. Loss of heterozygosity analyses for patient CGLU117. Supplementary Figure S9. Loss of heterozygosity analyses for patient CGLU127. Supplementary Figure S10. Loss of heterozygosity analyses for patient CGLU161. Supplementary Figure S11. TCR clonality analyses for patients CGLU127 and CGLU161. Supplementary Figure S12. TCR clonality analyses for a responder and a non-responder to PD-1 blockade. Supplementary Figure S13. PD-L1 expression in responsive and resistant tumors. Supplementary Figure S14. Eliminated mutations for case CGHN2. Supplementary Figure S15. Comparison of five methods for estimation of tumor purity. Supplementary Figure S16. CD8+ T cell density in resistant tumors.

This contains the figure and legend showing clonotypic expansions after 10 and 20 days

Mutation-associated neoantigens (MANA) are a target of antitumor T-cell immunity. Sensitive, simple, and standardized assays are needed to assess the repertoire of functional MANA-specific T cells in oncology. Assays analyzing in vitro cytokine production such as ELISpot and intracellular cytokine staining have been useful but have limited sensitivity in assessing tumor-specific T-cell responses and do not analyze antigen-specific T-cell repertoires. The FEST (Functional Expansion of Specific T cells) assay described herein integrates T-cell receptor sequencing of short-term, peptide-stimulated cultures with a bioinformatic platform to identify antigen-specific clonotypic amplifications. This assay can be adapted for all types of antigens, including MANAs via tumor exome-guided prediction of MANAs. Following in vitro identification by the MANAFEST assay, the MANA-specific CDR3 sequence can be used as a molecular barcode to detect and monitor the dynamics of these clonotypes in blood, tumor, and normal tissue of patients receiving immunotherapy. MANAFEST is compatible with high-throughput routine clinical and lab practices. Cancer Immunol Res; 6(8); 888–99. ©2018 AACR.

Supplementary Table S1. Summary of Patient and Sample Characteristics. Supplementary Table S2. Summary of Next-Generation Sequencing Analyses. Supplementary Table S3. Somatic Sequence Alterations*. Supplementary Table S4. Somatic Copy Number Alterations. Supplementary Table S5. Neoantigen Predictions. Supplementary Table S6. Characteristics of a Subset of Eliminated Candidate Neoantigens in the NSCLC patients*. Supplementary Table S7. Summary of Functionally Validated Eliminated, Gained, and Retained cMANAs. Supplementary Table S8. Eliminated MANA-specific T Cell Clonotypes in CGLU116, CGLU127 and CGLU161. Supplementary Table S9. Retained and Gained MANA-specific T Cell Clonotypes in CGLU116. Supplementary Table S10. Allelic Imbalance Analysis and Cellularity Estimates for CGLU116. Supplementary Table S11. Allelic Imbalance Analysis and Cellularity Estimates for CGLU117. Supplementary Table S12. Allelic Imbalance Analysis and Cellularity Estimates for CGLU127. Supplementary Table S13. Allelic Imbalance Analysis and Cellularity Estimates for CGLU161. Supplementary Table S14. Summary of Eliminated Neoantigens in NSCLC Cases. Supplementary Table S15. TCR-beta Sequencing Analysis. Supplementary Table S16. PD-L1 and CD8 Immunohistochemistry. Supplementary Table S17. Regions of Allelic Imbalance. Supplementary Table S18. Tumor Purity and Ploidy Estimates.

This is the FEST analysis output file for patient JH124

This is the FEST analysis output file for donor JH014

Supplementary Figure Legends

Supplementary Tables containing information pertaining to the viral epitopes evaluated, expanded clonotypes, clonotypes detected in pentamer-sorted populations, and the MANAs evaluated in patient JH124

Despite the initial successes of immunotherapy, there is an urgent clinical need for molecular assays that identify patients more likely to respond. Here, we report that ultrasensitive measures of circulating tumor DNA (ctDNA) and T-cell expansion can be used to assess responses to immune checkpoint blockade in metastatic lung cancer patients (N = 24). Patients with clinical response to therapy had a complete reduction in ctDNA levels after initiation of therapy, whereas nonresponders had no significant changes or an increase in ctDNA levels. Patients with initial response followed by acquired resistance to therapy had an initial drop followed by recrudescence in ctDNA levels. Patients without a molecular response had shorter progression-free and overall survival compared with molecular responders [5.2 vs. 14.5 and 8.4 vs. 18.7 months; HR 5.36; 95% confidence interval (CI), 1.57–18.35; P = 0.007 and HR 6.91; 95% CI, 1.37–34.97; P = 0.02, respectively], which was detected on average 8.7 weeks earlier and was more predictive of clinical benefit than CT imaging. Expansion of T cells, measured through increases of T-cell receptor productive frequencies, mirrored ctDNA reduction in response to therapy. We validated this approach in an independent cohort of patients with early-stage non–small cell lung cancer (N = 14), where the therapeutic effect was measured by pathologic assessment of residual tumor after anti-PD1 therapy. Consistent with our initial findings, early ctDNA dynamics predicted pathologic response to immune checkpoint blockade. These analyses provide an approach for rapid determination of therapeutic outcomes for patients treated with immune checkpoint inhibitors and have important implications for the development of personalized immune targeted strategies.Significance: Rapid and sensitive detection of circulating tumor DNA dynamic changes and T-cell expansion can be used to guide immune targeted therapy for patients with lung cancer.See related commentary by Zou and Meyerson, p. 1038

Figure S6. Differential abundance analysis of antibiotic resistance genes and virulence factors between R and NR for WGS datasets.

Correlative assays performed on each sample

Supplementary Table 6: Complete list of potentially-actionable alterations

Purpose:Neoadjuvant PD-1 blockade is a promising treatment for resectable non–small cell lung cancer (NSCLC), yet immunologic mechanisms contributing to tumor regression and biomarkers of response are unknown. Using paired tumor/blood samples from a phase II clinical trial (NCT02259621), we explored whether the peripheral T-cell clonotypic dynamics can serve as a biomarker for response to neoadjuvant PD-1 blockade.Experimental Design:T-cell receptor (TCR) sequencing was performed on serial peripheral blood, tumor, and normal lung samples from resectable NSCLC patients treated with neoadjuvant PD-1 blockade. We explored the temporal dynamics of the T-cell repertoire in the peripheral and tumoral compartments in response to neoadjuvant PD-1 blockade by using the TCR as a molecular barcode.Results:Higher intratumoral TCR clonality was associated with reduced percent residual tumor at the time of surgery, and the TCR repertoire of tumors with major pathologic response (MPR; Conclusions:Our study suggests that exchange of T-cell clones between tumor and blood represents a key correlate of pathologic response to neoadjuvant immunotherapy and shows that the periphery may be a previously underappreciated originating compartment for effective antitumor immunity.See related commentary by Henick, p. 1205

Supplementary Table 4: Complete list of mutations and copy number alterations detected by MSK-IMPACT in 45 LCNECs

Figures S1-S8

Supplementary Table 7: Detailed chemosensitivity data

Figure S5. Limited gut microbiome compositional changes are observed during treatment with checkpoint inhibitors.

Purpose: Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a highly aggressive neoplasm, whose biologic relationship to small cell lung carcinoma (SCLC) versus non-SCLC (NSCLC) remains unclear, contributing to uncertainty regarding optimal clinical management. To clarify these relationships, we analyzed genomic alterations in LCNEC compared with other major lung carcinoma types.Experimental Design: LCNEC (n = 45) tumor/normal pairs underwent targeted next-generation sequencing of 241 cancer genes by Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) platform and comprehensive histologic, immunohistochemical, and clinical analysis. Genomic data were compared with MSK-IMPACT analysis of other lung carcinoma histologies (n = 242).Results: Commonly altered genes in LCNEC included TP53 (78%), RB1 (38%), STK11 (33%), KEAP1 (31%), and KRAS (22%). Genomic profiles segregated LCNEC into 2 major and 1 minor subsets: SCLC-like (n = 18), characterized by TP53+RB1 co-mutation/loss and other SCLC-type alterations, including MYCL amplification; NSCLC-like (n = 25), characterized by the lack of coaltered TP53+RB1 and nearly universal occurrence of NSCLC-type mutations (STK11, KRAS, and KEAP1); and carcinoid-like (n = 2), characterized by MEN1 mutations and low mutation burden. SCLC-like and NSCLC-like subsets revealed several clinicopathologic differences, including higher proliferative activity in SCLC-like tumors (P < 0.0001) and exclusive adenocarcinoma-type differentiation marker expression in NSCLC-like tumors (P = 0.005). While exhibiting predominant similarity with lung adenocarcinoma, NSCLC-like LCNEC harbored several distinctive genomic alterations, including more frequent mutations in NOTCH family genes (28%), implicated as key regulators of neuroendocrine differentiation.Conclusions: LCNEC is a biologically heterogeneous group of tumors, comprising distinct subsets with genomic signatures of SCLC, NSCLC (predominantly adenocarcinoma), and rarely, highly proliferative carcinoids. Recognition of these subsets may inform the classification and management of LCNEC patients. Clin Cancer Res; 22(14); 3618–29. ©2016 AACR.

Clinical and genomic data for patients included in our study

Supplemental figure and table legends

Supplementary Table S1. Summary of patient characteristics Supplementary Table S2. Summary of plasma samples analyzed Supplementary Table S3. Summary of peripheral blood lymphocytes and tumor infiltrating lymphocytes samples analyzed Supplementary Table S4. Genes analyzed by TEC-Seq Supplementary Table S5. Summary of TEC-Seq Characteristics Supplementary Table S6. Somatic sequence alterations detected in cfDNA Supplementary Table S7. Mutation Cellularity Analysis for tumor-specific cfDNA variants Supplementary Table S8. TCR-beta sequencing analysis Supplementary Table S9. Differentially abundant clones for CGLU111 Supplementary Table S10. Differentially abundant clones for CGLU117 Supplementary Table S11. Differentially abundant clones for CGLU127 Supplementary Table S12. Differentially abundant clones for CGLU212 Supplementary Table S13. Differentially abundant clones for CGLU135 Supplementary Table S14. Differentially abundant clones for CGLU161 Supplementary Table S15. Differentially abundant clones for CGLU159 Supplementary Table S16. Differentially abundant clones for CGLU162 Supplementary Table S17. Differentially abundant clones for CGLU203 Supplementary Table S18. Differentially abundant clones for CGLU243

Table S1

The supplementary tables for "Early noninvasive detection of response to targeted therapy in non-small cell lung cancer" report clinical characteristics of patients analyzed (1), serial timepoints analyzed (2), genes analyzed (3), a summary of genomic analyses (4), somatic sequence alterations detected in cfDNA (5), somatic structural alterations detected in cfDNA (6), and blood cell proliferation alterations detected in cfDNA (7).

Supplementary Table 2: Immunohistochemical antibodies and protocols

Figure S4: Sensitivity and specificity analysis of the Integrated Microbiome Prediction Index for response prediction in responder (R) vs nonresponder (NR), further stratified by inclusion (+) or exclusion (-) of stable disease (SD) if available.

Figure S7. 16S rRNA amplicon data processing for predicted functional elements using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) based on taxonomic composition.

Figure S3. Complete species list for each index identified through re-analysis of raw sequencing data or as reported by the authors.

Supplementary Table 3: Clinicopathologic characteristics

Table S3

Supplementary Table 5: Detailed summary of NOTCH gene family mutations

Purpose:While immune checkpoint inhibitors (ICI) have revolutionized the treatment of cancer by producing durable antitumor responses, only 10%–30% of treated patients respond and the ability to predict clinical benefit remains elusive. Several studies, small in size and using variable analytic methods, suggest the gut microbiome may be a novel, modifiable biomarker for tumor response rates, but the specific bacteria or bacterial communities putatively impacting ICI responses have been inconsistent across the studied populations.Experimental Design:We have reanalyzed the available raw 16S rRNA amplicon and metagenomic sequencing data across five recently published ICI studies (n = 303 unique patients) using a uniform computational approach.Results:Herein, we identify novel bacterial signals associated with clinical responders (R) or nonresponders (NR) and develop an integrated microbiome prediction index. Unexpectedly, the NR-associated integrated index shows the strongest and most consistent signal using a random effects model and in a sensitivity and specificity analysis (P < 0.01). We subsequently tested the integrated index using validation cohorts across three distinct and diverse cancers (n = 105).Conclusions:Our analysis highlights the development of biomarkers for nonresponse, rather than response, in predicting ICI outcomes and suggests a new approach to identify patients who would benefit from microbiome-based interventions to improve response rates.

Supplementary Figure 1: Example of KRAS-mutated LCNEC: Illustration of NGS and histopathologic features; Supplementary Figure 2: Dot plots for total number of genomic alterations in carcinoid-like vs other LCNECs; Supplementary Figure 3: OncoPrints for chromatin modifier, neurogenesis/neural function, DNA replication/repair and PI3K-AKT-mTOR pathway genes; Supplementary Figure 4: Map of NOTCH mutations; Supplementary Figure 5: Expression of NE genes in transcriptional subgroups of lung adenocarcinoma in TCGA series; Supplementary Figure 6: Kaplan-Meier survival curves

Supplementary Figure S1. ctDNA clonal dynamics during anti-PD1 for patients with a clinical response. Supplementary Figure S2. ctDNA clonal dynamics during anti-PD1 for patients with molecular primary resistance. Supplementary Figure S3. ctDNA molecular responses precede radiologic responses. Supplementary Figure S4. Molecular responses correlate with a favorable prognosis. Supplementary Figure S5. Duration of molecular response correlates with progression-free and overall survival. Supplementary Figure S6. Disease prognostication by radiologic imaging at first assessment. Supplementary Figure S7. Differential clinical outcome by molecular responses in patients with radiologically stable disease. Supplementary Figure S8. Disease prognostication by tumor mutation burden. Supplementary Figure S9. ctDNA molecular response more accurately distinguish clinical responders from non-responders compared to clonal tumor mutation burden. Supplementary Figure S10. Molecular responses mirror pathologic responses to anti-PD1 therapy in early stage operable NSCLC. Supplementary Figure S11. TCR clonal dynamics during response and acquired resistance to anti-PD1. Supplementary Figure S12. TCR clonal dynamics for patients with clinical primary resistance. Supplementary Figure S13. Differential VJ gene usage for a patient with clinical response compared to a patient with clinical primary resistance. Supplementary Figure S14. Dynamic changes in pro-inflammatory and immunosuppressive cytokines in early stage NSCLC patients with differential responses to anti-PD1 therapy.

Table S4

Figure S2. Alpha diversity analysis across all cohorts using Chao1, observed species, Shannon, and Simpson Reciprocal measures.

Supplementary Table 1: Next-generation sequencing (MSK-IMPACT) gene panel

Table S5

The supplementary figures for "Early noninvasive detection of response to targeted therapy in non-small cell lung cancer" depict dynamic changes of ctDNA during therapy (S1), copy number changes identified in cfDNA from analyses of whole-genome data (S2), concordance between alterations observed with TEC-Seq in the plasma and clinical NGS analyses in the tumor tissue or plasma (S3), timeline of ctDNA analyses, CT assessments, and treatment response (S4), CT imaging assessments for responders and non-responders (S5), and cell-free tumor load and change in RECIST SLD (S6) for patients analyzed.

Figure S1. Correlation analysis between 16S rRNA amplicon and metagenomic taxonomic relative abundance estimates.

With the advent of precision oncology, there is an urgent need to develop improved methods for rapidly detecting responses to targeted therapies. Here, we have developed an ultrasensitive measure of cell-free tumor load using targeted and whole-genome sequencing approaches to assess responses to tyrosine kinase inhibitors in patients with advanced lung cancer. Analyses of 28 patients treated with anti-EGFR or HER2 therapies revealed a bimodal distribution of cell-free circulating tumor DNA (ctDNA) after therapy initiation, with molecular responders having nearly complete elimination of ctDNA (>98%). Molecular nonresponders displayed limited changes in ctDNA levels posttreatment and experienced significantly shorter progression-free survival (median 1.6 vs. 13.7 months, P < 0.0001; HR = 66.6; 95% confidence interval, 13.0–341.7), which was detected on average 4 weeks earlier than CT imaging. ctDNA analyses of patients with radiographic stable or nonmeasurable disease improved prediction of clinical outcome compared with CT imaging. These analyses provide a rapid approach for evaluating therapeutic response to targeted therapies and have important implications for the management of patients with cancer and the development of new therapeutics.Significance: Cell-free tumor load provides a novel approach for evaluating longitudinal changes in ctDNA during systemic treatment with tyrosine kinase inhibitors and serves an unmet clinical need for real-time, noninvasive detection of tumor response to targeted therapies before radiographic assessment.See related commentary by Zou and Meyerson, p. 1038