Identification and Treatment of Lung Cancer Oncogenic Drivers in a Diverse Safety Net Setting.

INTRODUCTION: Advances in the testing and treatment of patients with non-small cell lung cancer (NSCLC) harboring oncogenic drivers have improved outcomes. Little is known about testing and treatment patterns in diverse patient populations. METHODS: We conducted a retrospective study in a diverse cohort of patients treated in the John Peter Smith safety net healthcare system. We determined patterns of blood- and tissue-based testing and treatment of patients with EGFR and ALK alterations. Cox proportional-hazards regression models were used to assess the impact of EGFR and ALK testing. RESULTS: A total of 220 patients were included, 97 (44%) were non-Hispanic White, 72 (33%) were Black, 28 (13%) were Hispanic, and 23 (10%) were Asian. EGFR and ALK testing increased over time from 55% and 52%, respectively, in 2017 to 87% and 82%, respectively, in 2021. Frequency of EGFR alterations were highest in Asian patients (45%) and comparable among other groups (6-13%). Frequency of ALK alterations were highest in Hispanic (13%), and Asian (11%) patients, and were 2% for both Black and non-Hispanic White patients. In a multivariate model, lack of testing was associated with worse survival (aHR 1.6; P = .003) and testing positive for EGFR (aHR 0.43; P = .01) or ALK (aHR 0.28; P = .04) was associated with improved survival. Race and ethnicity were not associated with survival differences. CONCLUSION: As molecular testing for oncogenic mutations in NSCLC increases, druggable alterations such as ALK and EGFR can be identified in all race-ethnicity groups and are associated with improved outcomes.

A novel and potent MICA/B antibody is therapeutically effective in KRAS LKB1 mutant lung cancer models.

Concurrent KRAS LKB1 (STK11, KL) mutant Non-Small Cell Lung Cancers (NSCLC) is particularly difficult to treat and does not respond well to current immune checkpoint blockade (ICB) therapies. This is due to numerous mechanisms including low antigen presentation limiting T cell mediated killing. To activate anti-tumor immunity, we targeted tumor cell - natural killer (NK) cell interactions. We tested whether a novel antibody based therapeutic strategy that predominantly activates natural killer (NK) cells demonstrates efficacy in pre-clinical mouse models of KL NSCLC. NK cells rely on binding of ligands, such as Major Histocompatibility Complex (MHC) class I-related chain A or B (MICA/B), to the activating receptor NKG2D. Importantly MICA and MICB are widely expressed in elevated levels across NSCLC subtypes including KL lung cancers. Proteases with the tumor microenvironment (TME) can cleave these proteins rendering tumor cells less visible to NK cells. We therefore developed a MICA monoclonal antibody, AHA-1031, which utilizes two NK cell activating receptors. AHA1031 prevents ligand shedding without interfering with binding to NKG2D while targeting cancer cells to antibody mediated cell dependent cytotoxicity (ADCC). Our therapeutic novel antibody has significant monotherapy activity in KL cancer models including xenografts of human cell lines and patient derived xenografts. Activating NK cells through MICA/B stabilization and inducing ADCC offers an alternative and potent therapy option in KL tumors. MICA/B are shed across different tumors making this therapeutic strategy universally applicable.

Characteristics of Lung Cancer Patients With Asymptomatic or Undiagnosed SARS-CoV-2 Infections.

INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be spread by individuals unaware they are infected. Such dissemination has heightened ramifications in cancer patients, who may need to visit healthcare facilities frequently, be exposed to immune-compromising therapies, and face greater morbidity from coronavirus disease 2019 (COVID-19). We determined characteristics of (1) asymptomatic, clinically diagnosed, and (2) serologically documented but clinically undiagnosed SARS-CoV-2 infection among individuals with lung cancer. PATIENTS AND METHODS: In a multicenter registry, individuals with lung cancer (regardless of prior SARS-CoV-2 vaccination or documented infection) underwent collection of clinical data and serial blood samples, which were tested for antinucleocapsid protein antibody (anti-N Ab) or IgG (N) levels. We used multivariable logistic regression models to investigate clinical characteristics associated with the presence or absence of symptoms and the presence or absence of a clinical diagnosis among patients with their first SARS-CoV-2 infection. RESULTS: Among patients with serologic evidence or clinically documented SARS-CoV-2 infection, 80/142 (56%) had no reported symptoms at their first infection, and 61/149 (40%) were never diagnosed. Asymptomatic infection was more common among older individuals and earlier-stage lung cancer. In multivariable analysis, non-white individuals with SARS-CoV-2 serologic positivity were 70% less likely ever to be clinically diagnosed (P = .002). CONCLUSIONS: In a multicenter lung cancer population, a substantial proportion of SARS-CoV-2 infections had no associated symptoms or were never clinically diagnosed. Because such cases appear to occur more frequently in populations that may face greater COVID-19-associated morbidity, measures to limit disease spread and severity remain critical.

Concerning Safety and Efficacy of Concurrent and Consolidative Durvalumab With Thoracic Radiation Therapy in PDL1-Unselected Stage III Non-Small Cell Lung Cancer: Brief Report.

PURPOSE: Consolidative durvalumab, an anti-programmed death ligand 1 (PDL1) immune checkpoint inhibitor, administered after concurrent chemoradiation improves outcomes of patients with locally advanced non-small cell lung cancer (NSCLC) without substantially increasing toxicities. We studied a chemotherapy-free regimen of thoracic radiation therapy (RT) with concurrent and consolidative durvalumab. METHODS AND MATERIALS: This single-arm phase 2 trial enrolled patients with stage III NSCLC (regardless of tumor PDL1 expression), Eastern Cooperative Oncology Group (ECOG) performance status 0-1, adequate pulmonary function, and RT fields meeting standard organ constraints. Participants received 2 cycles of durvalumab (1500 mg every 4 weeks) concurrently with thoracic RT (60 Gy in 30 fractions), followed by up to 13 cycles of consolidative durvalumab. RESULTS: After 10 patients were enrolled, the trial was closed because of poor clinical outcomes. With a median follow-up of 12 months, 5 patients had disease progression and 8 patients died. Six patients experienced 15 treatment-related, grade ≥3 events, including 1 grade 4 acute kidney injury during consolidation and 2 fatal pulmonary events. One fatal pulmonary event occurred during the concurrent phase in an active smoker; the other occurred after the first cycle of consolidative durvalumab. The primary endpoint of progression-free survival at 12 months was 20% (50% for PDL1≥1% vs 0% for PDL1 unavailable or <1%). Median overall survival was not reached, 10.5 months, and 7 months, for PDL1 ≥1%, <1%, and unavailable, respectively. CONCLUSIONS: In PDL1 unselected stage III NSCLC, thoracic RT plus concurrent and consolidative durvalumab is associated with high-grade toxicity and early disease progression.

Time-Dependent Effects of Clinical Interventions on SARS-CoV-2 Immunity in Patients with Lung Cancer.

In patients with lung cancer (LC), understanding factors that impact the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) anti-spike antibody (SAb) titers over time is critical, but challenging, due to evolving treatments, infections, vaccinations, and health status. The objective was to develop a time-dependent regression model elucidating individual contributions of factors influencing SAb levels in LC patients using a prospective, longitudinal, multi-institutional cohort study initiated in January 2021. The study evaluated 296 LC patients-median age 69; 55% female; 50% stage IV. Blood samples were collected every three months to measure SAb levels using FDA-approved ELISA. Asymptomatic and unreported infections were documented through measurement of anti-nucleocapsid Ab levels (Meso Scale Discovery). Associations between clinical characteristics and titers were evaluated using a time-dependent linear regression model with a generalized estimating equation (GEE), considering time-independent variables (age, sex, ethnicity, smoking history, histology, and stage) and time-dependent variables (booster vaccinations, SARS-CoV-2 infections, cancer treatment, steroid use, and influenza vaccination). Significant time-dependent effects increasing titer levels were observed for prior SARS-CoV-2 infection (p < 0.001) and vaccination/boosters (p < 0.001). Steroid use (p = 0.043) and chemotherapy (p = 0.033) reduced titer levels. Influenza vaccination was associated with increased SAb levels (p < 0.001), independent of SARS-CoV-2 vaccine boosters. Prior smoking significantly decreased titers in females (p = 0.001). Age showed no association with titers. This GEE-based linear regression model unveiled the nuanced impact of multiple variables on patient anti-spike Ab levels over time. After controlling for the major influences of vaccine and SARS-CoV-2 infections, chemotherapy and steroid use were found to have negatively affected titers. Smoking in females significantly decreased titers. Surprisingly, influenza vaccinations were also significantly associated, likely indirectly, with improved SARS-CoV-2 titers.

Cmai: Predicting Antigen-Antibody Interactions from Massive Sequencing Data.

The interaction between antigens and antibodies (B cell receptors, BCRs) is the key step underlying the function of the humoral immune system in various biological contexts. The capability to profile the landscape of antigen-binding affinity of a vast number of BCRs will provide a powerful tool to reveal novel insights at unprecedented levels and will yield powerful tools for translational development. However, current experimental approaches for profiling antibody-antigen interactions are costly and time-consuming, and can only achieve low-to-mid throughput. On the other hand, bioinformatics tools in the field of antibody informatics mostly focus on optimization of antibodies given known binding antigens, which is a very different research question and of limited scope. In this work, we developed an innovative Artificial Intelligence tool, Cmai, to address the prediction of the binding between antibodies and antigens that can be scaled to high-throughput sequencing data. Cmai achieved an AUROC of 0.91 in our validation cohort. We devised a biomarker metric based on the output from Cmai applied to high-throughput BCR sequencing data. We found that, during immune-related adverse events (irAEs) caused by immune-checkpoint inhibitor (ICI) treatment, the humoral immunity is preferentially responsive to intracellular antigens from the organs affected by the irAEs. In contrast, extracellular antigens on malignant tumor cells are inducing B cell infiltrations, and the infiltrating B cells have a greater tendency to co-localize with tumor cells expressing these antigens. We further found that the abundance of tumor antigen-targeting antibodies is predictive of ICI treatment response. Overall, Cmai and our biomarker approach filled in a gap that is not addressed by current antibody optimization works nor works such as AlphaFold3 that predict the structures of complexes of proteins that are known to bind.

Acknowledging and addressing real-world challenges to treating immune-related adverse events.

Immune checkpoint inhibitors (ICIs) have revolutionized oncology treatment. However, their success is mitigated by the recognition that ICI-induced immune-related adverse events (irAEs) pose considerable challenges to patients and clinicians. These autoimmune toxicities are heterogeneous, unpredictable, and reflect a disease state resulting from a change in the immune system of patients. This contrasts with the typical acute nature of toxicities from chemotherapy and molecularly targeted oncology therapies. Management is further complicated by the extended bioavailability of these agents in patients as well as the persistence of autoimmune pathology. Currently, irAE treatment remains suboptimal in many areas, as many expert guidelines remain vague on the optimal selection, dosing, and duration of steroids and the use of other immunosuppressive agents. This coupled with delays in diagnosis and difficulties for patients accessing effective irAE treatment results in barriers to effective irAE care. The latter is complicated by the lack of US Food and Drug Administration-approved irAE treatments that lead to insurance denials, as well as the high cost of biological immunosuppressant therapies. Fortunately, rheumatologists and other subspecialists with expertize in the management of chronic autoimmune conditions have become more involved in irAE diagnosis and management and may help navigate treatment. In this commentary, we discuss these issues and propose potential solutions to advance the field.

Differences in systemic immune parameters in individuals with lung cancer according to race.

Introduction: Racial and ethnic disparities in the presentation and outcomes of lung cancer are widely known. To evaluate potential factors contributing to these observations, we measured systemic immune parameters in Black and White patients with lung cancer. Methods: Patients scheduled to receive cancer immunotherapy were enrolled in a multi-institutional prospective biospecimen collection registry. Clinical and demographic information were obtained from electronic medical records. Pre-treatment peripheral blood samples were collected and analyzed for cytokines using a multiplex panel and for immune cell populations using mass cytometry. Differences between Black and White patients were determined and corrected for multiple comparisons. Results: A total of 187 patients with non-small cell lung cancer (Black, 19; White, 168) were included in the analysis. There were no significant differences in baseline characteristics between Black and White patients. Compared to White patients, Black patients had significantly lower levels of CCL23 and CCL27, and significantly higher levels of CCL8, CXCL1, CCL26, CCL25, CCL1, IL-1 b, CXCL16, and IFN-γ (all P <0.05, FDR<0.1). Black patients also exhibited greater populations of non-classical CD16+ monocytes, NKT-like cells, CD4+ cells, CD38+ monocytes, and CD57+ gamma delta T cells (all P <0.05). Conclusions: Black and White patients with lung cancer exhibit several differences in immune parameters, with Black patients exhibiting greater levels of numerous pro-inflammatory cytokines and cell populations. The etiology and clinical significance of these differences warrant further evaluation.

Distinct autoantibody profiles across checkpoint inhibitor types and toxicities.

Immune checkpoint inhibitors (ICI) are increasingly used in combination. To understand the effects of different ICI categories, we characterized changes in circulating autoantibodies in patients enrolled in the E4412 trial (NCT01896999) of brentuximab vedotin (BV) plus ipilimumab, BV plus nivolumab, or BV plus ipilimumab-nivolumab for Hodgkin Lymphoma. Cycle 2 Day 1 (C2D1) autoantibody levels were compared to pre-treatment baseline. Across 112 autoantibodies tested, we generally observed increases in ipilimumab-containing regimens, with decreases noted in the nivolumab arm. Among 15 autoantibodies with significant changes at C2D1, all nivolumab cases exhibited decreases, with more than 90% of ipilimumab-exposed cases showing increases. Autoantibody profiles also showed differences according to immune-related adverse event (irAE) type, with rash generally featuring increases and liver toxicity demonstrating decreases. We conclude that dynamic autoantibody profiles may differ according to ICI category and irAE type. These findings may have relevance to clinical monitoring and irAE treatment.

Use of artificial intelligence chatbots in clinical management of immune-related adverse events.

BACKGROUND: Artificial intelligence (AI) chatbots have become a major source of general and medical information, though their accuracy and completeness are still being assessed. Their utility to answer questions surrounding immune-related adverse events (irAEs), common and potentially dangerous toxicities from cancer immunotherapy, are not well defined. METHODS: We developed 50 distinct questions with answers in available guidelines surrounding 10 irAE categories and queried two AI chatbots (ChatGPT and Bard), along with an additional 20 patient-specific scenarios. Experts in irAE management scored answers for accuracy and completion using a Likert scale ranging from 1 (least accurate/complete) to 4 (most accurate/complete). Answers across categories and across engines were compared. RESULTS: Overall, both engines scored highly for accuracy (mean scores for ChatGPT and Bard were 3.87 vs 3.5, p<0.01) and completeness (3.83 vs 3.46, p<0.01). Scores of 1-2 (completely or mostly inaccurate or incomplete) were particularly rare for ChatGPT (6/800 answer-ratings, 0.75%). Of the 50 questions, all eight physician raters gave ChatGPT a rating of 4 (fully accurate or complete) for 22 questions (for accuracy) and 16 questions (for completeness). In the 20 patient scenarios, the average accuracy score was 3.725 (median 4) and the average completeness was 3.61 (median 4). CONCLUSIONS: AI chatbots provided largely accurate and complete information regarding irAEs, and wildly inaccurate information ("hallucinations") was uncommon. However, until accuracy and completeness increases further, appropriate guidelines remain the gold standard to follow.

Highly variable timing renders immunotherapy efficacy and toxicity impractical biomarkers of one another in clinical practice.

Background: A useful clinical biomarker requires not only association but also a consistent temporal relationship. For instance, chemotherapy-induced neutropenia and epidermal growth-factor inhibitor-related acneiform rash both occur within weeks of treatment initiation, thereby providing information prior to efficacy assessment. Although immune checkpoint inhibitor (ICI)-associated immune-related adverse events (irAE) have been associated with therapeutic benefit, irAE may have delayed and highly variable onset. To determine whether ICI efficacy and irAE could serve as clinically useful biomarkers for predicting each other, we determined the temporal relationship between initial efficacy assessment and irAE onset in a diverse population treated with ICI. Methods: Using two-sided Fisher exact and Cochran-Armitage tests, we determined the relative timing of initial efficacy assessment and irAE occurrence in a cohort of 155 ICI-treated patients (median age 68 years, 40% women). Results: Initial efficacy assessment was performed a median of 50 days [interquartile range (IQR) 39-59 days] after ICI initiation; median time to any irAE was 77 days (IQR 28-145 days) after ICI initiation. Median time to first irAE was 42 days (IQR 20-88 days). Overall, 58% of any irAE and 47% of first irAE occurred after initial efficacy assessment. For clinically significant (grade ≥2) irAE, 60% of any and 53% of first occurred after initial efficacy assessment. The likelihood of any future irAE did not differ according to response (45% for complete or partial response vs. 47% for other cases; P=1). In landmark analyses controlling for clinical and toxicity follow-up, patients demonstrating greater tumor shrinkage at initial efficacy assessment were more likely to develop future grade ≥2 (P=0.05) and multi-organ (P=0.02) irAE. Conclusions: In contrast to that seen with chemotherapy and molecularly targeted therapies, the temporal relationship between ICI efficacy and toxicity is complex and bidirectional. In practice, neither parameter can be routinely relied on as a clinical biomarker to predict the other.

The evolution of lung adenocarcinoma precursors is associated with chromosomal instability and transition from innate to adaptive immune response/evasion.

Studying lung adenocarcinoma (LUAD) early carcinogenesis is challenging, primarily due to the lack of LUAD precursors specimens. We amassed multi-omics data from 213 LUAD and LUAD precursors to identify molecular features underlying LUAD precancer evolution. We observed progressively increasing mutations, chromosomal aberrations, whole genome doubling and genomic instability from precancer to invasive LUAD, indicating aggravating chromosomal instability (CIN). Telomere shortening, a crucial genomic alteration linked to CIN, emerged at precancer stage. Moreover, later-stage lesions demonstrated increasing cancer stemness and decreasing alveolar identity, suggesting epithelial de-differentiation during early LUAD carcinogenesis. The innate immune cells progressively diminished from precancer to invasive LUAD, concomitant with a gradual recruitment of adaptive immune cells (except CD8+ and gamma-delta T cells that decreased in later stages) and upregulation of numerous immune checkpoints, suggesting LUAD precancer evolution is associated with a shift from innate to adaptive immune response and immune evasion mediated by various mechanisms.

Transferring care to enhance access to early-phase cancer clinical trials: Protocol to evaluate a novel program.

Involving diverse populations in early-phase (phase I and II) cancer clinical trials is critical to informed therapeutic development. However, given the growing costs and complexities of early-phase trials, trial activation and enrollment barriers may be greatest for these studies at healthcare facilities that provide care to the most diverse patient groups, including those in historically underserved communities (e.g., safety-net healthcare systems). To promote diverse and equitable access to early-phase cancer clinical trials, we are implementing a novel program for the transfer of care to enhance access to early-phase cancer clinical trials. We will then perform a mixed-methods study to determine perceptions and impact of the program. Specifically, we will screen, recruit, and enroll diverse patients from an urban, integrated safety-net healthcare system to open and active early-phase clinical trials being conducted in a university-based cancer center. To evaluate this novel program, we will: (1) determine program impact and efficiency; and (2) determine stakeholder experience with and perceptions of the program. To achieve these goals, we will conduct preliminary cost analyses of the program. We will also conduct surveys and interviews with patients and caregivers to elucidate program impact, challenges, and areas for improvement. We hypothesize that broadening access to early-phase cancer trials conducted at experienced centers may improve equity and diversity. In turn, such efforts may enhance the efficiency and generalizability of cancer clinical research.

Geographic Accessibility and Completion of Initial Low-Dose CT-Based Lung Cancer Screening in an Urban Safety-Net Population.

BACKGROUND: Recent modifications to low-dose CT (LDCT)-based lung cancer screening guidelines increase the number of eligible individuals, particularly among racial and ethnic minorities. Because these populations disproportionately live in metropolitan areas, we analyzed the association between travel time and initial LDCT completion within an integrated, urban safety-net health care system. METHODS: Using Esri's StreetMap Premium, OpenStreetMap, and the r5r package in R, we determined projected private vehicle and public transportation travel times between patient residence and the screening facility for LDCT ordered in March 2017 through December 2022 at Parkland Memorial Hospital in Dallas, Texas. We characterized associations between travel time and LDCT completion in univariable and multivariable analyses. We tested these associations in a simulation of 10,000 permutations of private vehicle and public transportation distribution. RESULTS: A total of 2,287 patients were included in the analysis, of whom 1,553 (68%) completed the initial ordered LDCT. Mean age was 63 years, and 73% were underrepresented minorities. Median travel time from patient residence to the LDCT screening facility was 17 minutes by private vehicle and 67 minutes by public transportation. There was a small difference in travel time to the LDCT screening facility by public transportation for patients who completed LDCT versus those who did not (67 vs 66 min, respectively; P=.04) but no difference in travel time by private vehicle for these patients (17 min for both; P=.67). In multivariable analysis, LDCT completion was not associated with projected travel time to the LDCT facility by private vehicle (odds ratio, 1.01; 95% CI, 0.82-1.25) or public transportation (odds ratio, 1.14; 95% CI, 0.89-1.44). Similar results were noted across travel-type permutations. Black individuals were 29% less likely to complete LDCT screening compared with White individuals. CONCLUSIONS: In an urban population comprising predominantly underrepresented minorities, projected travel time is not associated with initial LDCT completion in an integrated health care system. Other reasons for differences in LDCT completion warrant investigation.

Effect of Patient Navigation on Completion of Lung Cancer Screening in Vulnerable Populations.

BACKGROUND: Although low-dose, CT-based lung cancer screening (LCS) can decrease lung cancer mortality in high-risk individuals, the process may be complex and pose challenges to patients, particularly those from minority underinsured and uninsured populations. We conducted a randomized controlled trial of telephone-based navigation for LCS within an integrated, urban, safety-net health care system. PATIENTS AND METHODS: Patients eligible for LCS were randomized (1:1) to usual care with or without navigation at Parkland Health in Dallas, Texas. The primary endpoint was completion of the first 3 consecutive steps in a patient's LCS process. We explored differences in completion of LCS steps between navigation and usual care groups, controlling for patient characteristics using the chi-square test. RESULTS: Patients (N=447) were randomized to either navigation (n=225) or usual care (n=222). Mean patient age was 62 years, 46% were female, and 69% were racial/ethnic minorities. There was no difference in completion of the first 3 steps of the LCS algorithm between arms (12% vs 9%, respectively; P=.30). For ordered LCS steps, completion rates were higher among patients who received navigation (86% vs 79%; P=.03). The primary reason for step noncompletion was lack of order placement. CONCLUSIONS: In this study, lack of order placement was a key reason for incomplete LCS steps. When orders were placed, patients who received navigation had higher rates of completion. Clinical team education and enhanced electronic health record processes to simplify order placement, coupled with patient navigation, may improve LCS in safety-net health care systems.

Emerging Strategies in Lung Cancer Screening: Blood and Beyond.

BACKGROUND: Although low dose computed tomography (LDCT)-based lung cancer screening (LCS) can decrease lung cancer-related mortality among high-risk individuals, it remains an imperfect and substantially underutilized process. LDCT-based LCS may result in false-positive findings, which can lead to invasive procedures and potential morbidity. Conversely, current guidelines may fail to capture at-risk individuals, particularly those from under-represented minority populations. To address these limitations, numerous biomarkers have emerged to complement LDCT and improve early lung cancer detection. CONTENT: This review focuses primarily on blood-based biomarkers, including protein, microRNAs, circulating DNA, and methylated DNA panels, in current clinical development for LCS. We also examine other emerging biomarkers-utilizing airway epithelia, exhaled breath, sputum, and urine-under investigation. We highlight challenges and limitations of biomarker testing, as well as recent strategies to integrate molecular strategies with imaging technologies. SUMMARY: Multiple biomarkers are under active investigation for LCS, either to improve risk-stratification after nodule detection or to optimize risk-based patient selection for LDCT-based screening. Results from ongoing and future clinical trials will elucidate the clinical utility of biomarkers in the LCS paradigm.

Incidence, Correlates, and Prognostic Significance of Mixed Response in Advanced Non-small Cell Lung Cancer.

BACKGROUND: Mixed response (MR), a scenario featuring discordant tumor changes, has been reported primarily with targeted therapies or immunotherapy. We determined the incidence and prognostic significance of MR in advanced non-small cell lung cancer (NSCLC) treated with cytotoxic chemotherapy. PATIENTS AND METHODS: We analyzed patient-level data from ECOG-ACRIN E5508 (carboplatin-paclitaxel + bevacizumab induction followed by randomization to maintenance therapy regimens). For patients with at least 2 target lesions and available measurements after cycle 2, we characterized response as homogeneous response (HR, similar behavior of all lesions), MR (similar behavior but >30% difference in magnitude of best and least responding lesions), or true mixed response (TMR, best and least responding lesions showing different behavior: ≥10% growth versus ≥10% shrinkage). We compared category characteristics using Mann-Whitney U and Chi-square tests, and overall survival (OS) using log-rank test and Cox models. RESULTS: Among 965 evaluable patients, HR occurred in 609 patients (63%), MR in 208 (22%), and TMR in 148 (15%). Median OS was 13.6 months for HR, 12.0 months for MR, and 7.6 months for TMR (P < .001). Compared to HR, TMR had inferior OS among stable disease cases (HR 1.62; 95% CI, 1.23-2.12; P < .001) and a trend toward inferior OS among progressive disease cases (HR 1.39; 95% CI, 0.83-2.33; P = .2). In multivariate analysis, TMR was associated with worse OS (HR 1.48; 95% CI, 1.22-1.79; P < .001). CONCLUSION: True mixed response occurs in a substantial minority of lung cancer cases treated with chemotherapy and independently confers poor prognosis.

Telemedicine and Cancer Clinical Research: Opportunities for Transformation.

ABSTRACT: Telemedicine represents an established mode of patient care delivery that has and will continue to transform cancer clinical research. Through telemedicine, opportunities exist to improve patient care, enhance access to novel therapies, streamline data collection and monitoring, support communication, and increase trial efficiency. Potential challenges include disparities in technology access and literacy, physical examination performance, biospecimen collection, privacy and security concerns, coverage of services by insurance, and regulatory considerations. Coupled with artificial intelligence, telemedicine may offer ways to reach geographically dispersed candidates for narrowly focused cancer clinical trials, such as those targeting rare genomic subsets. Collaboration among clinical trial staff, clinicians, regulators, professional societies, patients, and their advocates is critical to optimize the benefits of telemedicine for clinical cancer research.