tbuckley/cabot-search huggingface dataset:
Tertiary lymphoid structures and their association with immune checkpoint inhibitor response and survival outcomes in patients with non-small cell lung cancer.
Authors: Dmitrii Grachev, Dhruv Bansal, Ben Ponvilawan, Christopher Ward, Ammar Al-Obaidi, Vladimir Kushnarev, Konstantin Danilov, Artem Tarasov, Ivan Valiev, Konstantin Chernishev, Polina Turova, Alexander Bagaev, Nikita Kotlov, Janakiraman Subramanian
Journal: Journal of Clinical Oncology (2025)

2634 Background: Immune checkpoint inhibitor (ICI)-based therapy is currently the first-line treatment for patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) without actionable mutations. However, the commonly utilized biomarkers, including PD-L1 protein expression and tumor mutation burden, are not sufficiently accurate to predict the treatment response from ICI in this patient population. As tumor microenvironment (TME) and tertiary lymphoid structure (TLS) play a significant role in antitumor immunity, we explore these immunophenotypic factors to determine the potential biomarkers in patients with LUAD or LUSC. Methods: We evaluated all patients with LUAD or LUSC from three publicly available data and two novel retrospective cohorts for transcriptomic-based immune TME subtype classification (immune-hot vs. immune-cold) and TLS signature, along with associated clinical and genomic data. Those with other histological subtypes of non-small cell lung cancer or those who harbored EGFR mutations or ALK rearrangements were excluded from our study. The cellular decomposition within tumor samples was calculated using the deconvolutional Kassandra algorithm. Survival analysis was evaluated using log-rank test and multivariate Cox regression adjusted by PD-L1 status, KEAP1/STK11/KRAS/TP53 mutational status, immune TME subtype, and TLS signature. All statistical analyses were performed using Python. Results: A total of 514 patients were included from five cohorts, with 272 and 505 having genomic and transcriptomic data, respectively. 59% of patients with LUAD or LUSC exhibited an immune-cold phenotype, which correlated with adverse overall survival (OS) and progression-free survival (PFS) than immune-hot phenotype in LUAD. However, the ICI response rates were similar in both groups. Superior PFS and ICI response rates were observed in patients with high TLS signatures (&gt; 88th percentile) in LUAD, even after multivariate adjustments. Immune signatures that were positively associated with ICI response included the infiltration and trafficking of T and NK cells for LUAD and B-cell percentage for LUSC. In contrast, CD8 + T-cell abundance did not correlate with ICI response. The presence of KEAP1 or STK11 mutations also did not affect the response rates but were associated with shorter OS and PFS. Conclusions: Transcriptomic-based immune-hot TME and high TLS signature may serve as novel predictive and prognostic biomarkers in patients with LUAD, while the presence of KEAP1 or STK11 mutations only offered prognostic values. Further prospective studies are warranted to expand to other treatment combinations with PD-(L)1 inhibitors.
Association of medical debt with cancer incidence rates in the US.
Authors: Jiazhang Xing, Xuesong Han, Ryan David Nipp, Robin Yabroff, Changchuan Jiang
Journal: Journal of Clinical Oncology (2025)

11012 Background: Medical debt is a growing challenge for U.S. patients and has been associated with higher cancer mortality. However, whether medical debt is associated with advanced-stage cancer diagnoses – a strong predictor of worse prognosis – remains unclear. This study investigated the association between medical debt in collections and overall and advanced stage cancer incidence rates in the U.S. Methods: We conducted an ecological study using 2019 county-level medical debt in collections data from the Urban Institute Debt in America project and age-adjusted cancer incidence rates (2017-2021) from the CDC State Cancer Profiles dataset. Outcomes were age-adjusted cancer incidence and age-adjusted late-stage (i.e., regional or distant) incidence rates for ten common cancers with screening tests or early signs. We used generalized linear mixed models, adjusting for urban-rural status, county-level Social Vulnerability Index, county population size, primary care physician density, median population age and state. Results: We included data from all 3,143 counties. The county-level percentage of medical debt in collections ranged from 0.0% to 23.5%. One percentage increase in medical debt was associated with a 74.80 (95% CI=43.45-106.19) increase in overall cancer incidence rate per 100,000 person-years. Regarding late-stage cancer incidence, one percentage increase in medical debt was associated with a 5.16 (95% CI=1.05-9.28) increase in late-stage incidence rate for colorectal, a 2.11 increase (95% CI=1.04-3.18) for bladder, a 2.73 (95% CI=1.08-4.39) increase for kidney and renal pelvis, a 28.69 (95% CI=22.22-35.17) for lung and bronchus, and a 0.14 (95% CI=0.01-0.26) increase for oral cavity and pharynx cancers, and a 2.99 (95% CI=1.53-4.44) increase for melanoma of the skin. No significant association was noticed in other cancer types. Conclusions: Medical debt in collections is associated with higher overall incidence and late-stage rates for multiple cancer types, suggesting financial barriers may impede both cancer screening and timely symptom evaluation. Adjusted association of percent of population with medical debt in collections with cancer incidence rate at the county level (per 100,000). Incidence Late stage incidence All cancers 74.80 (43.45-106.19) - Colorectal 9.48 (3.68-15.28) 5.16 (1.05-9.28) Kidney and renal pelvis 9.84 (6.48-13.22) 2.73 (1.08-4.39) Lung and bronchus 41.02 (32.46-49.58) 28.69 (22.22-35.17) Cervix 0.64(0.40-0.88) 0.17(0.00-0.34) Melanoma -5.73(-11.52-0.05) 2.99 (1.53-4.44) Bladder -0.73(-4.01-2.51) 2.11 (1.04-3.18) Prostate -13.70(-29.67-2.29) -5.28(-11.10-0.42) Breast -1.43(-13.83-10.89) 4.57(-1.71-10.88) Oral Cavity and Pharynx 0.16(0.01-0.31) 0.14(0.01-0.26) Data for late-stage incidence of all cancer sites combined not available from CDC.
The predictive role of TRAIL gene expression in immune checkpoint inhibitor (ICI)-treated patients (pts).
Authors: Obada Ehab Ababneh, Daisuke Nishizaki, Hirotaka Miyashita, Suzanna Lee, Paul DePietro, Sarabjot Pabla, Taylor J. Jensen, Shumei Kato, Razelle Kurzrock
Journal: Journal of Clinical Oncology (2025)

2637 Background: Despite FDA-approved molecular biomarkers such as PD-L1 levels, tumor mutation burden (TMB), and microsatellite instability (MSI) status, only ~30% of matched cancer pts respond to ICI. TRAIL, a protein product of TNFSF10 gene, is a member of the TNF superfamily involved in regulating immune responses and inducing apoptosis when bound to either Death Receptor 4 or 5 (DR4/5) especially in cancer cells. While TRAIL has been studied for its prognostic roles in cancer, its predictive value for pts treated with ICI remains unclear. This study investigates the association between TRAIL expression and outcomes in ICI-treated pan-cancer pts. Methods: RNA expression levels of TRAIL were assessed in a cohort of 217 pan-cancer pts treated with ICIs at the University of California San Diego (UCSD) Moores Cancer Center. RNA transcripts were normalized using an internal housekeeping gene profile of 735 tumors and 35 histologies. Transcript abundances were percentile-ranked (0–100) and categorized as high (≥75th percentile) or low ( &lt; 75th percentile). Associations between TRAIL expression and overall survival (OS) and progression-free survival (PFS) were analyzed. Statistical significance was defined as p-value ≤ 0.05. Results: Among the 217 ICI-treated pts, the median age was 61.9 years, and 56.2% were female. The most common cancer types were colorectal (24.9%), breast (8.8%), ovarian (8.3%), pancreatic (7.4%), and lung (6.5%) cancers. FDA-approved ICI biomarkers favorable rates were PD-L1 ≥1% in 40.1%, TMB-high (≥10 mut/Mb) in 11.5%, and MSI-high in 4.8%. Based on the ICI type used, 91.7% received anti-PD-(L)1 while 7.8% received anti-CTLA-4 with anti-PD-1. Pts with high TRAIL expression (24%) had similar PD-L1, TMB, MSI profiles (p &gt; 0.05). Pts with high levels of TRAIL expression achieved better OS (HR = 0.41, 95%CI:0.25-0.69, p = 0.0004) and PFS (HR = 0.67, 95%CI:0.47-0.96, p = 0.027). After adjusting for age, sex, cancer type, PD-L1 IHC level (≥1% vs. &lt; 1%), TMB (≥10 mut/Mb vs. &lt; 10mut/Mb), MSI status (stable vs. unstable), KRAS, TP53 and CDKN2A/B alteration status and immune checkpoints genes expression, overall survival remained significantly associated with better survival in TRAIL high pts compared to TRAIL low pts (HR = 0.38, 95%CI:0.19-0.76, p = 0.006). However, no difference was found between both groups in regard to progression-free survival (HR = 0.68, 95%CI:0.42-1.10, p = 0.11). Conclusions: High TRAIL expression is associated with improved overall survival in ICI-treated pan-cancer pts, independent of cancer type or other predictive biomarkers. These findings suggest TRAIL as a potential biomarker for ICI benefit. Larger studies in diverse and real-world settings are warranted to validate these findings.
Biological determinants of immune exclusion in non-small cell lung cancer: An analysis of the precision medicine BIP study.
Authors: Jean-Philippe Guegan, Florent Peyraud, Christophe Rey, Sophie Cousin, Sofiane Taleb, Natalie Karpinich, Jaegil Kim, Racha Cheikh, Sapna Yadavilli, Alban Bessede, Antoine Italiano
Journal: Journal of Clinical Oncology (2025)

2636 Background: Immune exclusion has been associated with resistance to immunotherapy in NSCLC. However, its biological determinants remain largely unknown. Instead of relying on preclinical models, high-throughput profiling of patient samples using spatial transcriptomics (ST) and multiplex immunofluorescence (m-IF) offers a powerful approach to dissect immune profiles and uncover key drivers of immune response and resistance. Methods: Tumor samples collected from NSCLC patients enrolled in the BIP precision medicine study (NCT02534649) prior to initiation of ICI therapy and divided into Discovery and Validation cohorts (n = 148 and 117, respectively). Response to treatment was assessed as per RECIST criteria. Multiplex immunohistochemistry (mIHC) with CD8 and panCK markers was used to classify tumors as desert, excluded or inflamed through pathologist assessment (PA) and image analysis ST using the NanoString GeoMx Whole Transcriptome Atlas compared gene expression profiles between inflamed and excluded tumors Spatially resolved T-cell receptor (TCR) profiling assessed clonal diversity and repertoire to evaluate T-cell functionality. m-IF was used for proteomic validation. Results: In both the training and validation cohorts, excluded tumors demonstrated lower objective response rates (ORR), progression-free survival (PFS), and overall survival (OS) compared to inflamed tumors (Table 1), independent of PD-L1 expression in multivariate analysis. ST identified marked overexpression of HLA-A/B (MHC class I) and CD74 (involved in MHC class II processing) in inflamed tumors versus excluded tumors, underscoring their crucial roles in antigen presentation. These results were validated by m-IF. Spatially resolved TCR profiling demonstrated higher Gini coefficients and lower Shannon entropy in excluded tumors, indicating a more oligoclonal TCR repertoire dominated by fewer T-cell clones. These findings suggest impaired antigen recognition and restricted T-cell diversity in excluded tumors. Conclusions: Our classification approach using mIHC and IA offers a practical, and clinically actionable biomarker for predicting response to ICI therapy. Immune exclusion, prevalent in NSCLC, is associated with resistance to ICI and characterized by reduced expression of key antigen presentation molecules such as HLA-A/B and CD74 and a restricted TCR repertoire highlighting the need for novel strategies to overcome this immune barrier. Phenotype Objective Response Rate (ORR) PFS (Median, Months) Discovery Inflamed(n=32) 58% 12.8 (95% CI: 6.16-NA) Excluded(n=65) 38.7% 4.1 (95% CI: 2.4-10.3) Desert(n=51) 20% 2.8 (95% CI: 1.9-6.9) Validation Inflamed (n=40) 57.5% 11.3 (95% CI: 4.6-NA) Excluded(n=30) 43.3% 6.1 (95% CI: 3.4-14.9) Desert(n=47) 31.9% 4.4 (95% CI: 2.3-7.2)
Predication of clinical outcomes of advanced cutaneous squamous cell carcinoma to PD1 inhibition directly from histopathology slides using inferred transcriptomics.
Authors: Johnathan Arnon, Gal Dinstag, Bohdana Chayen, Omer Tirosh, Yaron Kinar, Doreen S. Ben-Zvi, Tuvik Beker, Anna Elia, Eli Pikarsky, Rottenberg Yakir, Ranit Aharonov, Aron Popovtzer
Journal: Journal of Clinical Oncology (2025)

2630 Background: Metastatic or locally advanced cutaneous squamous cell carcinoma (CSCC) not amenable to local therapy is treated with programmed death (PD)-1 inhibitors, namely Cemiplimab . While response rates are relatively high at approximately 45%, no predictive biomarkers for PD-1 inhibition have been validated in advanced CSCC subjecting some patients, especially older, to unnecessary immune-related adverse events (irAE). We present a retrospective analysis of ENLIGHT-DP, a novel biomarker for response to PD-1 inhibition in advanced CSCC, calculated directly from histopathological slides. Methods: We retrospectively examined high resolution hematoxylin and eosin (H&amp;E) slide scans from archived tumor-tissue samples of advanced CSCC patients treated with Cemiplimab to generate an individual prediction score to PD-1 inhibitors using ENLIGHT-DP. This is composed of two main steps: (I) prediction of individual mRNA expression directly from H&amp;E slides using the digital pathology-based DeepPT algorithm (II) use these values as input to ENLIGHT, a transcriptomics-based precision oncology platform for prediction of response to cancer therapies. We then unblinded clinical outcomes and assessed the predictive values of ENLIGHT-DP. Results: We evaluated 39 cases of advanced CSCC (tumors from various origins) at a median age of 81 years old (range 57-100). Of them, 32 cases (82%) were initially treated with surgery or radiotherapy with curative intent, but ultimately suffered disease progression. The objective response rate (ORR) was 69%, median progression free survival (PFS) was 11 months (CI 95% 10.4-17.6) and 6 patients (15%) suffered from severe irAE necessitating treatment cessation. ENLIGHT-DP was predictive of response with ROC AUC = 0.67. Using a binary threshold for classification, calibrated on previous lung and head and neck cohorts, ENLIGHT-DP displays promising biomarker characteristics: 81.8% PPV, 66.6% sensitivity and 4.0 OR (p = 0.04) for matched vs. unmatched patients. ENLIGHT-DP successfully stratified PFS with HR 0.02 (CI 95% 0.0005-0.96, p = 0.05). Importantly, omitting cases in which ENLIGHT-DP was calculated on samples which were taken more than 6 months prior to Cemiplimab therapy (i.e., during diagnostic excisions) did not influence the results. No other patient characteristics (e.g., age, stage, co-morbidities, previous treatments) were associated with outcomes. Conclusions: ENLIGHT-DP demonstrates high predictive values for clinical outcomes of PD-1 inhibition in advanced CSCC, relying solely on easily accessible archived H&amp;E slides.
Complex interplay between housing insecurity and forgone care among U.S. cancer survivors.
Authors: Shreya Kondle, Naveen Premnath
Journal: Journal of Clinical Oncology (2025)

11010 Background: One-sixth of U.S. cancer survivors experience housing insecurity, and one-fifth report delaying or forgoing care due to costs. The role of social determinants of health (SDoH) among cancer survivors is complex and poorly understood. We sought to examine the prevalence of housing insecurity and forgone care, and their effects in conjunction with SDoH among cancer survivors. Methods: The Medication Expenditure Panel Survey (MEPS) is an annual survey representative of the civilian, non-institutionalized United States population. The 2022 MEPS survey included a Preventative Care Self-Administered Questionnaire measuring late rent/mortgage payments indicative of housing insecurity in the past year. We considered adult cancer survivors (excluding non-melanoma skin cancer) who reported an inability to afford or had to delay any medical/dental care or prescription medications, as forgoing care. We extracted sociodemographic and clinical characteristics. Survey-adjusted Wilcoxon rank-sum tests and chi-square tests assessed differences in continuous and categorical variables, respectively, for individuals with and without housing insecurity, cancer, and forgone care. A multivariate regression analysis adjusting for age, sex, race, and poverty status identified predictors of forgoing medical care. We conducted weighted analyses to account for the complex survey design and considered p &lt; 0.05 as statistically significant. Results: A total of 1,776 cancer survivors (weighted (w): 21,088,176) and 20,655 individuals with no cancer (w: 311,965,067) were identified. Among cancer survivors, 13% (w: 1,315,879) reported housing insecurity while 19% (w: 4,063,946) were forgoing care. Housing insecurity was reported in 8% of female survivors compared to 4% of male survivors, while 22% of women forwent care compared to 16% of men. Black Americans were less likely to report forgone care (13%) compared to White (19%) and Hispanic cancer survivors (25%), while housing insecurity was higher among all minorities (~10%) compared to White survivors (5%). Younger cancer survivors aged 18-45 years reported four-fold rates of housing insecurity and 1.5 times the rate of forgone care compared to survivors over 65. Multivariate logistic regression revealed forgoing medical care was more prevalent in individuals aged 45–64 (aOR = 1.6), women (aOR = 1.5), the near poor (aOR = 2.9), and those experiencing housing insecurity (aOR = 4.1), but less prevalent in Black Americans (aOR = 0.5) (all p &lt; 0.05). Conclusions: This is the first nationally representative study to report the complex interplay between housing insecurity and forgone care among cancer survivors. The interactions highlight some unexpected high-risk groups like women, near-poor individuals, and middle-aged individuals, who are more likely to forgo care when forced to choose or prioritize care. Future policies need to target these high-risk groups.
Trends in racial and regional disparities in breast cancer mortality among the female population in the United States: A retrospective database analysis from 1999-2020.
Authors: Maryam Masood, Khawaja Talha Aziz, Woo Joo Lee, Muhammad Sohaib Asghar
Journal: Journal of Clinical Oncology (2025)

e13824 Background: Breast cancer mortality in the United States has been decreasing since the last decade of the 20th century due to improvements in treatment and early detection. However, racial and ethnic disparities in breast cancer death rates still persist. We aimed to explore racial and regional disparities among females who have died from breast cancer in the United States. Methods: Breast cancer patients were identified using CDC WONDER, a national mortality database that includes data from more than 99% of death certificates collected through the National Vital Statistics System. ICD-10 codes for breast cancer (C50.0 – C50.9) were used. Data were stratified by race, urbanization, census region and states with highest mortality rates represented per 100,000 population. Joinpoint regression analysis was used to trend the age-adjusted mortality rates (AAMR) during the study period from 1999 to 2020. Results: Between 1999 and 2020, a total of 1,113,711 female patients died from breast cancer with an AAMR of 32.3 per 100,000 in 1999 and decreasing to 24.2 per 100,000 in 2020. Among racial differences, the White female population had the highest proportional mortality of about 78.4% with AAMR of 27.3, however highest AAMR was observed in the Black population (35.8). In terms of regional differences, the South region had the highest number of death (36%) with an AAMR of 26.7, followed by the Midwest with the highest AAMR of 27.98. Large metropolitan areas recorded the highest number of deaths despite non-metropolitan areas with slightly higher AAMR. States in the top 90th percentile of AAMR are District of Columbia, Ohio, Mississippi, Oklahoma, Maryland and Louisiana. Conclusions: There have been significant reductions in mortality rates among breast cancer patients in the United States, however, with persistent racial disparities. This requires for continued vigilance in public health efforts and awareness for targeted screening and prevention strategies. Mortality proportions among sociodemographic patterns in the United States. Variables Age adjusted mortality rates Proportional mortality (%) Female (all) 26.96 per 100,000 100.0% White 27.30 per 100,000 78.40% Hispanic 16.94 per 100,000 5.33% Black 35.80 per 100,000 13.48% Asians 13.87 per 100,000 2.19% Natives 18.79 per 100,000 0.40% Large Metropolitan 27.03 per 100,000 52.60% Medium-Small metropolitan 26.71 per 100,000 30.38% Non-metropolitan 27.24 per 100,000 17.03% Northeast 27.34 per 100,000 20.15% Midwest 27.98 per 100,000 23.38% South 26.70 per 100,000 36.03% West 25.90 per 100,000 20.44%