Pub Date : 2024-09-12DOI: 10.1101/2024.09.11.24313334
Brian De, Prashant Dogra, Mohamed Zaid, Dalia Elganainy, Kevin Sun, Ahmed M. Amer, Charles Wang, Michael K. Rooney, Enoch Chang, Hyunseon C. Kang, Zhihui Wang, Priya Bhosale, Bruno C. Odisio, Timothy E. Newhook, Ching-Wei D. Tzeng, Hop S. Tran Cao, Yun S. Chun, Jean-Nicholas Vauthey, Sunyoung S. Lee, Ahmed Kaseb, Kanwal Raghav, Milind Javle, Bruce D. Minsky, Sonal S. Noticewala, Emma B. Holliday, Grace L. Smith, Albert C. Koong, Prajnan Das, Vittorio Cristini, Ethan B. Ludmir, Eugene Koay
Background: Although escalated doses of radiation therapy (RT) for intrahepatic cholangiocarcinoma (iCCA) are associated with durable local control (LC) and prolonged survival, uncertainties persist regarding personalized RT based on biological factors. Compounding this knowledge gap, the assessment of RT response using traditional size-based criteria via computed tomography (CT) imaging correlates poorly with outcomes. We hypothesized that quantitative measures of enhancement would more accurately predict clinical outcomes than size-based assessment alone and developed a model to optimize RT. Methods: Pre-RT and post-RT CT scans of 154 patients with iCCA were analyzed retrospectively for measurements of tumor dimensions (for RECIST) and viable tumor volume using quantitative European Association for Study of Liver (qEASL) measurements. Binary classification and survival analyses were performed to evaluate the ability of qEASL to predict treatment outcomes, and mathematical modeling was performed to identify the mechanistic determinants of treatment outcomes and to predict optimal RT protocols. Results: Multivariable analysis accounting for traditional prognostic covariates revealed that percentage change in viable volume following RT was significantly associated with OS, outperforming stratification by RECIST. Binary classification identified ≥33% decrease in viable volume to optimally correspond to response to RT. The model-derived, patient-specific tumor enhancement growth rate emerged as the dominant mechanistic determinant of treatment outcome and yielded high accuracy of patient stratification (80.5%), strongly correlating with the qEASL-based classifier. Conclusion: Following RT for iCCA, changes in viable volume outperformed radiographic size-based assessment using RECIST for OS prediction. CT-derived tumor-specific mathematical parameters may help optimize RT for resistant tumors.
{"title":"Measurable imaging-based changes in enhancement of intrahepatic cholangiocarcinoma after radiotherapy reflect physical mechanisms of response","authors":"Brian De, Prashant Dogra, Mohamed Zaid, Dalia Elganainy, Kevin Sun, Ahmed M. Amer, Charles Wang, Michael K. Rooney, Enoch Chang, Hyunseon C. Kang, Zhihui Wang, Priya Bhosale, Bruno C. Odisio, Timothy E. Newhook, Ching-Wei D. Tzeng, Hop S. Tran Cao, Yun S. Chun, Jean-Nicholas Vauthey, Sunyoung S. Lee, Ahmed Kaseb, Kanwal Raghav, Milind Javle, Bruce D. Minsky, Sonal S. Noticewala, Emma B. Holliday, Grace L. Smith, Albert C. Koong, Prajnan Das, Vittorio Cristini, Ethan B. Ludmir, Eugene Koay","doi":"10.1101/2024.09.11.24313334","DOIUrl":"https://doi.org/10.1101/2024.09.11.24313334","url":null,"abstract":"Background: Although escalated doses of radiation therapy (RT) for intrahepatic cholangiocarcinoma (iCCA) are associated with durable local control (LC) and prolonged survival, uncertainties persist regarding personalized RT based on biological factors. Compounding this knowledge gap, the assessment of RT response using traditional size-based criteria via computed tomography (CT) imaging correlates poorly with outcomes. We hypothesized that quantitative measures of enhancement would more accurately predict clinical outcomes than size-based assessment alone and developed a model to optimize RT. Methods: Pre-RT and post-RT CT scans of 154 patients with iCCA were analyzed retrospectively for measurements of tumor dimensions (for RECIST) and viable tumor volume using quantitative European Association for Study of Liver (qEASL) measurements. Binary classification and survival analyses were performed to evaluate the ability of qEASL to predict treatment outcomes, and mathematical modeling was performed to identify the mechanistic determinants of treatment outcomes and to predict optimal RT protocols. Results: Multivariable analysis accounting for traditional prognostic covariates revealed that percentage change in viable volume following RT was significantly associated with OS, outperforming stratification by RECIST. Binary classification identified ≥33% decrease in viable volume to optimally correspond to response to RT. The model-derived, patient-specific tumor enhancement growth rate emerged as the dominant mechanistic determinant of treatment outcome and yielded high accuracy of patient stratification (80.5%), strongly correlating with the qEASL-based classifier. Conclusion: Following RT for iCCA, changes in viable volume outperformed radiographic size-based assessment using RECIST for OS prediction. CT-derived tumor-specific mathematical parameters may help optimize RT for resistant tumors.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1101/2024.09.10.24313432
Daniel A. Ruiz Torres, Michael E. Bryan, Shun Hirayama, Ross D. Merkin, Luciani Evelyn, Thomas Roberts, Manisha Patel, Jong C. Park, Lori J. Wirth, Peter M. Sadow, Moshe Sade-Feldman, Shannon L. Stott, Daniel L. Faden
Immune checkpoint blockade (ICB) is the standard of care for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), yet efficacy remains low. The current approach for predicting the likelihood of response to ICB is a single proportional biomarker (PD-L1) expressed in immune and tumor cells (Combined Positive Score, CPS) without differentiation by cell type, potentially explaining its limited predictive value. Tertiary Lymphoid Structures (TLS) have shown a stronger association with ICB response than PD-L1. However, their exact composition, size, and spatial biology in HNSCC remain understudied. A detailed understanding of TLS is required for future use as a clinically applicable predictive biomarker. Methods: Pre-ICB tumor tissue sections were obtained from 9 responders (complete response, partial response, or stable disease) and 11 non-responders (progressive disease) classified via RECISTv1.1. A custom multi-immunofluorescence (mIF) staining assay was designed, optimized, and applied to characterize tumor cells (pan-cytokeratin), T cells (CD4, CD8), B cells (CD19, CD20), myeloid cells (CD16, CD56, CD163), dendritic cells (LAMP3), fibroblasts (alpha-Smooth Muscle Actin), proliferative status (Ki67) and immunoregulatory molecules (PD1). Spatial metrics were compared among groups. Serial tissue sections were scored for TLS in both H&E and mIF slides. A machine learning model was employed to measure the effect of these metrics on achieving a response to ICB (SD, PR, or CR). Results: A higher density of B lymphocytes (CD20+) was found in responders compared to non-responders to ICB (p=0.022). A positive correlation was observed between mIF and pathologist identification of TLS (R2= 0.66, p-value= <0.0001). TLS trended toward being more prevalent in responders to ICB (p=0.0906). The presence of TLS within 100 um of the tumor was associated with improved overall (p=0.04) and progression-free survival (p=0.03). A multivariate machine learning model identified TLS density as a leading predictor of response to ICB with 80% accuracy. Conclusion: Immune cell densities and TLS spatial location within the tumor microenvironment play a critical role in the immune response to HNSCC and may potentially outperform CPS as a predictor of ICB response.
{"title":"Immune Cell Densities Predict Response to Immune Checkpoint-Blockade in Head and Neck Cancer","authors":"Daniel A. Ruiz Torres, Michael E. Bryan, Shun Hirayama, Ross D. Merkin, Luciani Evelyn, Thomas Roberts, Manisha Patel, Jong C. Park, Lori J. Wirth, Peter M. Sadow, Moshe Sade-Feldman, Shannon L. Stott, Daniel L. Faden","doi":"10.1101/2024.09.10.24313432","DOIUrl":"https://doi.org/10.1101/2024.09.10.24313432","url":null,"abstract":"Immune checkpoint blockade (ICB) is the standard of care for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), yet efficacy remains low. The current approach for predicting the likelihood of response to ICB is a single proportional biomarker (PD-L1) expressed in immune and tumor cells (Combined Positive Score, CPS) without differentiation by cell type, potentially explaining its limited predictive value. Tertiary Lymphoid Structures (TLS) have shown a stronger association with ICB response than PD-L1. However, their exact composition, size, and spatial biology in HNSCC remain understudied. A detailed understanding of TLS is required for future use as a clinically applicable predictive biomarker. Methods: Pre-ICB tumor tissue sections were obtained from 9 responders (complete response, partial response, or stable disease) and 11 non-responders (progressive disease) classified via RECISTv1.1. A custom multi-immunofluorescence (mIF) staining assay was designed, optimized, and applied to characterize tumor cells (pan-cytokeratin), T cells (CD4, CD8), B cells (CD19, CD20), myeloid cells (CD16, CD56, CD163), dendritic cells (LAMP3), fibroblasts (alpha-Smooth Muscle Actin), proliferative status (Ki67) and immunoregulatory molecules (PD1). Spatial metrics were compared among groups. Serial tissue sections were scored for TLS in both H&E and mIF slides. A machine learning model was employed to measure the effect of these metrics on achieving a response to ICB (SD, PR, or CR). Results: A higher density of B lymphocytes (CD20+) was found in responders compared to non-responders to ICB (p=0.022). A positive correlation was observed between mIF and pathologist identification of TLS (R2= 0.66, p-value= <0.0001). TLS trended toward being more prevalent in responders to ICB (p=0.0906). The presence of TLS within 100 um of the tumor was associated with improved overall (p=0.04) and progression-free survival (p=0.03). A multivariate machine learning model identified TLS density as a leading predictor of response to ICB with 80% accuracy. Conclusion: Immune cell densities and TLS spatial location within the tumor microenvironment play a critical role in the immune response to HNSCC and may potentially outperform CPS as a predictor of ICB response.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"95 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1101/2024.09.11.24313382
Jayant S Vaidya, Max Bulsara, Uma J Vaidya, David Morgan, Michael Douek, Marcelle Bernstein, Chris Brew-Graves, Norman R Williams, Jeffrey S Tobias
In many breast cancer radiotherapy trials, the results are presented in the form of cumulative incidence rates of local recurrence or Kaplan-Meier plots, in which deaths are censored. Censoring - using patients' length of follow up until the point when they had last been seen alive - is included in the statistical model, under the correct assumption that they will continue to have a risk of developing a local recurrence. Censoring should be non-informative and balanced. However, if shorter follow up is unbalanced between treatments, or if shorter follow up is due to death (from whatever cause), these assumptions and therefore the model is no longer valid. It is therefore ambiguous to statistically ignore deaths when reporting local recurrence, by censoring them. We illustrate, with examples from randomised trials, why and how such graphs cannot give patients and clinicians a clear indication of the effects of treatments or prognosis. For instance, in one of these examples, 60% of patients were alive at 10 years, so those alive without a local recurrence should inevitably be lower than 60%, rather than the 90% estimated using the above method. The simple way to avoid this error is to turn the analysis on its head, by reporting chances of success rather than failure, by reporting the probability of being free of local recurrence (i.e. both death and local recurrence are events). This estimate truly represents what really happens to patients in terms of local control and the relative effectiveness of treatment(s) comprehensively. It also conforms with the recommendations of ICH-GCP, European (DATECAN) and American (STEEP) guidelines.
{"title":"Cumulative local recurrence rate is a misleading and non-representative outcome measure for early breast cancer trials","authors":"Jayant S Vaidya, Max Bulsara, Uma J Vaidya, David Morgan, Michael Douek, Marcelle Bernstein, Chris Brew-Graves, Norman R Williams, Jeffrey S Tobias","doi":"10.1101/2024.09.11.24313382","DOIUrl":"https://doi.org/10.1101/2024.09.11.24313382","url":null,"abstract":"In many breast cancer radiotherapy trials, the results are presented in the form of cumulative incidence rates of local recurrence or Kaplan-Meier plots, in which deaths are censored. Censoring - using patients' length of follow up until the point when they had last been seen alive - is included in the statistical model, under the correct assumption that they will continue to have a risk of developing a local recurrence. Censoring should be non-informative and balanced. However, if shorter follow up is unbalanced between treatments, or if shorter follow up is due to death (from whatever cause), these assumptions and therefore the model is no longer valid. It is therefore ambiguous to statistically ignore deaths when reporting local recurrence, by censoring them. We illustrate, with examples from randomised trials, why and how such graphs cannot give patients and clinicians a clear indication of the effects of treatments or prognosis. For instance, in one of these examples, 60% of patients were alive at 10 years, so those alive without a local recurrence should inevitably be lower than 60%, rather than the 90% estimated using the above method. The simple way to avoid this error is to turn the analysis on its head, by reporting chances of success rather than failure, by reporting the probability of being free of local recurrence (i.e. both death and local recurrence are events). This estimate truly represents what really happens to patients in terms of local control and the relative effectiveness of treatment(s) comprehensively. It also conforms with the recommendations of ICH-GCP, European (DATECAN) and American (STEEP) guidelines.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"184 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1101/2024.09.10.24313450
Emma Hazelwood, Daffodil M Canson, Xuemin Wang, Pik Fang Kho, Danny Legge, Andrei-Emil Constantinescu, Matthew A Lee, D. Timothy Bishop, Andrew T Chan, Stephen B Gruber, Jochen Hampe, Loic Le Marchand, Michael O Woods, Rish K Pai, Stephanie L Schmit, Jane C Figueiredo, Wei Zheng, Jeroen R Huyghe, Neil Murphy, Marc J Gunter, Tom G Richardson, Vicki L Whitehall, Emma E Vincent, Dylan M Glubb, Tracy A O'Mara
Numerous potential susceptibility genes have been identified for colorectal cancer (CRC). However, it remains unclear which genes have a causal role in CRC risk, whether these genes are associated with specific types of CRC, and if they have potential for therapeutic targeting. We performed a multi-tissue transcriptome-wide association study (TWAS) across six relevant normal tissues (n=187-670) and applied a causal framework (involving Mendelian randomisation and genetic colocalisation) to prioritise causal associations between gene expression or splicing events and CRC risk (52,775 cases; 45,940 controls), incorporating sex- and anatomical subsite-specific analyses. We identified 35 genes with robust evidence for a potential causal role in CRC, including ten genes not previously identified by TWAS. Among these genes, SEMA4D emerged as a significant discovery; it is not located at any established CRC genome-wide association study (GWAS) risk locus and its encoded protein is targeted by an antibody currently being clinically studied for CRC treatment. Several genes showed increased expression associated with CRC risk and evidence of CRC cell dependency in CRISPR screen analyses, highlighting their potential as targets for therapeutic inhibition. A female-specific association with CRC risk was observed for CCM2 expression, which is involved in progesterone signalling pathways. Subsite-specific associations were also found, including a link between rectal cancer risk and expression of LAMC1, which encodes a target for a clinically approved drug. Additionally, we performed a focused analysis of established drug targets to further identify potential therapies for CRC, revealing PDCD1, the product of which (PD-1) is targeted by a clinically approved CRC immunotherapy. In summary, our comprehensive analysis provides valuable insights into the molecular underpinnings of CRC and supports promising avenues for therapeutic intervention.
{"title":"Integrating multi-tissue expression and splicing data to prioritise anatomical subsite- and sex-specific colorectal cancer susceptibility genes with therapeutic potential","authors":"Emma Hazelwood, Daffodil M Canson, Xuemin Wang, Pik Fang Kho, Danny Legge, Andrei-Emil Constantinescu, Matthew A Lee, D. Timothy Bishop, Andrew T Chan, Stephen B Gruber, Jochen Hampe, Loic Le Marchand, Michael O Woods, Rish K Pai, Stephanie L Schmit, Jane C Figueiredo, Wei Zheng, Jeroen R Huyghe, Neil Murphy, Marc J Gunter, Tom G Richardson, Vicki L Whitehall, Emma E Vincent, Dylan M Glubb, Tracy A O'Mara","doi":"10.1101/2024.09.10.24313450","DOIUrl":"https://doi.org/10.1101/2024.09.10.24313450","url":null,"abstract":"Numerous potential susceptibility genes have been identified for colorectal cancer (CRC). However, it remains unclear which genes have a causal role in CRC risk, whether these genes are associated with specific types of CRC, and if they have potential for therapeutic targeting. We performed a multi-tissue transcriptome-wide association study (TWAS) across six relevant normal tissues (n=187-670) and applied a causal framework (involving Mendelian randomisation and genetic colocalisation) to prioritise causal associations between gene expression or splicing events and CRC risk (52,775 cases; 45,940 controls), incorporating sex- and anatomical subsite-specific analyses. We identified 35 genes with robust evidence for a potential causal role in CRC, including ten genes not previously identified by TWAS. Among these genes, SEMA4D emerged as a significant discovery; it is not located at any established CRC genome-wide association study (GWAS) risk locus and its encoded protein is targeted by an antibody currently being clinically studied for CRC treatment. Several genes showed increased expression associated with CRC risk and evidence of CRC cell dependency in CRISPR screen analyses, highlighting their potential as targets for therapeutic inhibition. A female-specific association with CRC risk was observed for CCM2 expression, which is involved in progesterone signalling pathways. Subsite-specific associations were also found, including a link between rectal cancer risk and expression of LAMC1, which encodes a target for a clinically approved drug. Additionally, we performed a focused analysis of established drug targets to further identify potential therapies for CRC, revealing PDCD1, the product of which (PD-1) is targeted by a clinically approved CRC immunotherapy. In summary, our comprehensive analysis provides valuable insights into the molecular underpinnings of CRC and supports promising avenues for therapeutic intervention.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1101/2024.09.11.24313485
MD Anderson Head and Neck Cancer Symptom Working Group, Serageldin Kamel, Laia Humbert-Vidan, Zaphanlene Kaffey, Abdulrahman Abusaif, David T.A. Fuentes, Kareem A Wahid, Cem Dede, Mohamed A Naser, Renjie He, Ahmed W Moawad, Khaled M Elsayes, Melissa M Chen, Adegbenga O Otun, Jillian Rigert, Mark Chambers, Andrew Hope, Erin Watson, Kristy K Brock, Katherine A Hutcheson, Lisanne V van Dijk, Amy C Moreno, Stephen Y Lai, Clifton D Fuller, Abdallah SR Mohamed
Purpose. This study aims to identify radiomic features extracted from contrast-enhanced CT scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in patients with head and neck cancer (HNC) treated with radiotherapy (RT). Materials and Methods. Contrast-enhanced CT (CECT) images were collected for 150 patients (80% train, 20% test) with confirmed ORN diagnosis at The University of Texas MD Anderson Cancer Center between 2008 and 2018. Using PyRadiomics, radiomic features were extracted from manually segmented ORN regions and the corresponding automated control regions, the later defined as the contralateral healthy mandible region. A subset of pre-selected features was obtained based on correlation analysis (r > 0.95) and used to train a Random Forest (RF) classifier with Recursive Feature Elimination. Model explainability SHapley Additive exPlanations (SHAP) analysis was performed on the 20 most important features identified by the trained RF classifier. Results. From a total of 1316 radiomic features extracted, 810 features were excluded due to high collinearity. From a set of 506 pre-selected radiomic features, the optimal subset resulting on the best discriminative accuracy of the RF classifier consisted of 67 features. The RF classifier was well calibrated (Log Loss 0.296, ECE 0.125) and achieved an accuracy of 88% and a ROC AUC of 0.96. The SHAP analysis revealed that higher values of Wavelet-LLH First-order Mean and Median were associated with ORN of the jaw (ORNJ). Conversely, higher Exponential GLDM Dependence Entropy and lower Square First-order Kurtosis were more characteristic of normal mandibular tissue. Conclusion. This study successfully developed a CECT-based radiomics model for differentiating ORNJ from healthy mandibular tissue in HNC patients after RT. Future work will focus on the detection of subclinical ORNJ regions to guide earlier interventions.
{"title":"Computed tomography radiomics-based cross-sectional detection of mandibular osteoradionecrosis in head and neck cancer survivors","authors":"MD Anderson Head and Neck Cancer Symptom Working Group, Serageldin Kamel, Laia Humbert-Vidan, Zaphanlene Kaffey, Abdulrahman Abusaif, David T.A. Fuentes, Kareem A Wahid, Cem Dede, Mohamed A Naser, Renjie He, Ahmed W Moawad, Khaled M Elsayes, Melissa M Chen, Adegbenga O Otun, Jillian Rigert, Mark Chambers, Andrew Hope, Erin Watson, Kristy K Brock, Katherine A Hutcheson, Lisanne V van Dijk, Amy C Moreno, Stephen Y Lai, Clifton D Fuller, Abdallah SR Mohamed","doi":"10.1101/2024.09.11.24313485","DOIUrl":"https://doi.org/10.1101/2024.09.11.24313485","url":null,"abstract":"Purpose. This study aims to identify radiomic features extracted from contrast-enhanced CT scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in patients with head and neck cancer (HNC) treated with radiotherapy (RT).\u0000Materials and Methods. Contrast-enhanced CT (CECT) images were collected for 150 patients (80% train, 20% test) with confirmed ORN diagnosis at The University of Texas MD Anderson Cancer Center between 2008 and 2018. Using PyRadiomics, radiomic features were extracted from manually segmented ORN regions and the corresponding automated control regions, the later defined as the contralateral healthy mandible region. A subset of pre-selected features was obtained based on correlation analysis (r > 0.95) and used to train a Random Forest (RF) classifier with Recursive Feature Elimination. Model explainability SHapley Additive exPlanations (SHAP) analysis was performed on the 20 most important features identified by the trained RF classifier.\u0000Results. From a total of 1316 radiomic features extracted, 810 features were excluded due to high collinearity. From a set of 506 pre-selected radiomic features, the optimal subset resulting on the best discriminative accuracy of the RF classifier consisted of 67 features. The RF classifier was well calibrated (Log Loss 0.296, ECE 0.125) and achieved an accuracy of 88% and a ROC AUC of 0.96. The SHAP analysis revealed that higher values of Wavelet-LLH First-order Mean and Median were associated with ORN of the jaw (ORNJ). Conversely, higher Exponential GLDM Dependence Entropy and lower Square First-order Kurtosis were more characteristic of normal mandibular tissue.\u0000Conclusion. This study successfully developed a CECT-based radiomics model for differentiating ORNJ from healthy mandibular tissue in HNC patients after RT. Future work will focus on the detection of subclinical ORNJ regions to guide earlier interventions.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1101/2024.09.10.24313042
Deondre D Do, Mariluz Rojo Domingo, Christopher C Conlin, Ian Matthews, Karoline Kallis, Madison T Baxter, Courtney Ollison, Yuze Song, George Xu, Allison Y Zhong, Aditya Bagrodia, Tristan Barrett, Matthew Cooperberg, Felix Feng, Michael E Hahn, Mukesh Harisinghani, Gary Hollenberg, Juan Javier-Desloges, Sophia C Kamran, Christopher J Kane, Dimitri Kessler, Joshua Kuperman, Kang-Lung Lee, Jonathan Levine, Michael A Liss, Daniel JA Margolis, Paul M Murphy, Nabih Nakrour, Michael A Ohliger, Thomas Osinski, Anthony J Pamatmat, Isabella R Pompa, Rebecca Rakow-Penner, Jacob L Roberts, Karan Santhosh, Ahmed S Shabaik, David Song, Clare M Tempany, Shaun Trecarten, Natasha Wehrli, Eric P Weinberg, Sean Woolen, Anders M Dale, Tyler M Seibert
Introduction Prostate multiparametric magnetic resonance imaging (mpMRI) has greatly improved the detection of clinically significant prostate cancer (csPCa). However, the limited number of expert sub-specialist radiologists capable of interpreting conventional prostate mpMRI is a bottleneck for universal access to this healthcare advance. A reliable and reproducible quantitative imaging biomarker could facilitate implementation of accurate prostate MRI at clinical sites with limited experience, thus ensuring more equitable patient care. Restriction Spectrum Imaging restriction score (RSIrs) is an MRI biomarker that has shown the ability to enhance the qualitative and quantitative interpretation of prostate MRI. However, patient-level factors (age, race, ethnicity, prostate volume, and 5-alpha-reductase inhibitor (5-ARI) use) and acquisition-level factors (scanner manufacturer/model and protocol parameters) can affect prostate mpMRI, and their impact on quantitative RSIrs is unknown. Methods RSI data from patients with known or suspected csPCa were collected from seven centers. We estimated effects of patient and acquisition factors on prostate voxels overall (Method 1: benign patients only) and on only the maximum RSIrs within each prostate (RSIrsmax; Method 2: benign and csPCa patients) using linear models. We then tested whether adjusting for any estimated systematic biases would improve performance of RSIrs for patient-level detection of csPCa, as measured by area under the ROC curve (AUC). Results Using both Method 1 and Method 2, we observed statistically significant effects on RSIrs of age and acquisition group (p < 0.05). Prostate volume had significant effects using only Method 2. All of these effects were small, and adjusting for them did not improve csPCa detection performance (p ≥ 0.05). AUC of RSIrsmax for patient-level csPCa detection was 0.77 (95% CI: 0.75, 0.79) unadjusted, compared to 0.77 (0.76, 0.79) and 0.74 (0.72, 0.76) after adjustment using Method 1 and 2 respectively. Conclusion Age, prostate volume, and imaging acquisition factors may lead to systematic differences in RSIrs, but these effects are small and have minimal impact on performance of RSIrs for detection of csPCa. RSIrs can be used as a reliable biomarker across a wide range of patients, centers, scanners, and acquisition factors.
{"title":"Robustness of a Restriction Spectrum Imaging (RSI) quantitative MRI biomarker for prostate cancer: assessing for systematic bias due to age, race, ethnicity, prostate volume, medication use, or imaging acquisition parameters","authors":"Deondre D Do, Mariluz Rojo Domingo, Christopher C Conlin, Ian Matthews, Karoline Kallis, Madison T Baxter, Courtney Ollison, Yuze Song, George Xu, Allison Y Zhong, Aditya Bagrodia, Tristan Barrett, Matthew Cooperberg, Felix Feng, Michael E Hahn, Mukesh Harisinghani, Gary Hollenberg, Juan Javier-Desloges, Sophia C Kamran, Christopher J Kane, Dimitri Kessler, Joshua Kuperman, Kang-Lung Lee, Jonathan Levine, Michael A Liss, Daniel JA Margolis, Paul M Murphy, Nabih Nakrour, Michael A Ohliger, Thomas Osinski, Anthony J Pamatmat, Isabella R Pompa, Rebecca Rakow-Penner, Jacob L Roberts, Karan Santhosh, Ahmed S Shabaik, David Song, Clare M Tempany, Shaun Trecarten, Natasha Wehrli, Eric P Weinberg, Sean Woolen, Anders M Dale, Tyler M Seibert","doi":"10.1101/2024.09.10.24313042","DOIUrl":"https://doi.org/10.1101/2024.09.10.24313042","url":null,"abstract":"<strong>Introduction</strong>\u0000Prostate multiparametric magnetic resonance imaging (mpMRI) has greatly improved the detection of clinically significant prostate cancer (csPCa). However, the limited number of expert sub-specialist radiologists capable of interpreting conventional prostate mpMRI is a bottleneck for universal access to this healthcare advance. A reliable and reproducible quantitative imaging biomarker could facilitate implementation of accurate prostate MRI at clinical sites with limited experience, thus ensuring more equitable patient care. Restriction Spectrum Imaging restriction score (RSIrs) is an MRI biomarker that has shown the ability to enhance the qualitative and quantitative interpretation of prostate MRI. However, patient-level factors (age, race, ethnicity, prostate volume, and 5-alpha-reductase inhibitor (5-ARI) use) and acquisition-level factors (scanner manufacturer/model and protocol parameters) can affect prostate mpMRI, and their impact on quantitative RSIrs is unknown. <strong>Methods</strong>\u0000RSI data from patients with known or suspected csPCa were collected from seven centers. We estimated effects of patient and acquisition factors on prostate voxels overall (Method 1: benign patients only) and on only the maximum RSIrs within each prostate (RSIrs<sub>max</sub>; Method 2: benign and csPCa patients) using linear models. We then tested whether adjusting for any estimated systematic biases would improve performance of RSIrs for patient-level detection of csPCa, as measured by area under the ROC curve (AUC). <strong>Results</strong>\u0000Using both Method 1 and Method 2, we observed statistically significant effects on RSIrs of age and acquisition group (p < 0.05). Prostate volume had significant effects using only Method 2. All of these effects were small, and adjusting for them did not improve csPCa detection performance (p ≥ 0.05). AUC of RSIrs<sub>max</sub> for patient-level csPCa detection was 0.77 (95% CI: 0.75, 0.79) unadjusted, compared to 0.77 (0.76, 0.79) and 0.74 (0.72, 0.76) after adjustment using Method 1 and 2 respectively. <strong>Conclusion</strong>\u0000Age, prostate volume, and imaging acquisition factors may lead to systematic differences in RSIrs, but these effects are small and have minimal impact on performance of RSIrs for detection of csPCa. RSIrs can be used as a reliable biomarker across a wide range of patients, centers, scanners, and acquisition factors.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1101/2024.09.11.24313475
Rammah Elnour, Ingjerd Helstrup Hindenes, Malene Faerevaag, Ingrid Benedicte Moss Kolseth, Liv Cecilie Vestrheim Thomsen, Anne Christine Johannessen, Daniela Elena Costea, Line Bjorge, Harsh Nitin Dongre
Background: While the prognostic value of immune-related biomarkers is well characterized in many solid tumors, their significance in vulva squamous cell carcinoma (VSCC) remains unclear. Here, we report a comprehensive analysis of programmed death-ligand 1 (PD-L1) and immune cell infiltrates in VSCC and establish immunoscore models for classification of the disease. Methods: Archival tissues, immunohistochemistry, and digital quantification were used to investigate the number of CD4+, CD8+, CD68+, CD14+, FoxP3+, and PD-L1+ cells in epithelial and stromal compartments of VSCC (n=117). Immunoscores were developed by using these parameters and applying the least absolute shrinkage and selection operator (LASSO) to identify predictors of survival. Immunoscores were then integrated with HPV status, as determined by mRNA in situ hybridization, to construct internally validated nomograms. The models were assessed using Harrell's concordance-index (c-index), calibration plots, Kaplan-Meier curves, and decision curve analysis. Results: Advanced VSCC (FIGO stage III/IV) was characterized by high numbers of CD68+ macrophages and PD-L1+ cells (Spearmans correlation, ρ>0.80) in the epithelium. PD-L1 status independently predicted poor progression free survival (PFS) (HR=1.80, (95% CI (1.024-3.170), p=0.041). High stromal CD68+ or CD14+ myeloid cell infiltration was associated with poor PFS and disease specific survival (DSS) (p<0.05). Immunological parameters were used to determine immunoscores. ImmunoscorePFS and immunoscoreDSS were independent prognosticators of PFS (p=0.001) and DSS (p=0.007) respectively. Integrating immunoscores with HPV status (IS-HPV index) improved the prognostic impact of the models. The c-index of IS-HPV indexPFS was 0.750 for prediction of PFS compared to 0.666 for HPV status and 0.667 for immunoscorePFS. The c-index of IS-HPV indexDSS was 0.752 for predicting DSS compared to 0.631 for HPV status and 0.715 for immunoscoreDSS. Conclusion: In summary, an index based on HPV status and an immunoscore built on PD-L1 expression and immune cell infiltrates could potentially serve as a prognostic tool to refine risk stratification in VSCC. Further validation is warranted to demonstrate clinical utility.
背景：虽然免疫相关生物标记物在许多实体瘤中的预后价值已得到了很好的描述，但它们在外阴鳞状细胞癌（VSCC）中的意义仍不明确。在此，我们报告了对VSCC中程序性死亡配体1（PD-L1）和免疫细胞浸润的全面分析，并建立了用于疾病分类的免疫评分模型。研究方法采用档案组织、免疫组化和数字量化技术研究 VSCC（n=117）上皮和基质中 CD4+、CD8+、CD68+、CD14+、FoxP3+ 和 PD-L1+ 细胞的数量。通过使用这些参数并应用最小绝对缩小和选择算子（LASSO）来确定生存预测因子，从而建立免疫分数。然后将免疫评分与通过 mRNA 原位杂交确定的 HPV 状态相结合，构建出经过内部验证的提名图。使用哈雷尔一致性指数（c-index）、校准图、卡普兰-梅耶曲线和决策曲线分析对模型进行评估。结果晚期VSCC（FIGO III/IV期）的特征是上皮细胞中有大量CD68+巨噬细胞和PD-L1+细胞（Spearmans相关性，ρ>0.80）。PD-L1 状态可独立预测较差的无进展生存期（PFS）（HR=1.80，（95% CI (1.024-3.170)，p=0.041）。高基质CD68+或CD14+髓系细胞浸润与不良无进展生存期和疾病特异性生存期（DSS）相关（p<0.05）。免疫学参数用于确定免疫评分。免疫评分PFS和免疫评分DSS分别是PFS（p=0.001）和DSS（p=0.007）的独立预后指标。将免疫评分与人乳头瘤病毒状态（IS-HPV 指数）相结合可提高模型对预后的影响。IS-HPV indexPFS 预测 PFS 的 c 指数为 0.750，而 HPV 状态预测 PFS 的 c 指数为 0.666，免疫评分预测 PFS 的 c 指数为 0.667。IS-HPV indexDSS 预测 DSS 的 c 指数为 0.752，而 HPV 状态为 0.631，免疫评分 DSS 为 0.715。结论总之，基于HPV状态的指数和基于PD-L1表达和免疫细胞浸润的免疫评分有可能成为一种预后工具，用于完善VSCC的风险分层。要证明其临床实用性，还需要进一步验证。
{"title":"Prognostic value of an integrated human papilloma virus and immunoscore model to predict survival in vulva squamous cell carcinoma","authors":"Rammah Elnour, Ingjerd Helstrup Hindenes, Malene Faerevaag, Ingrid Benedicte Moss Kolseth, Liv Cecilie Vestrheim Thomsen, Anne Christine Johannessen, Daniela Elena Costea, Line Bjorge, Harsh Nitin Dongre","doi":"10.1101/2024.09.11.24313475","DOIUrl":"https://doi.org/10.1101/2024.09.11.24313475","url":null,"abstract":"Background: While the prognostic value of immune-related biomarkers is well characterized in many solid tumors, their significance in vulva squamous cell carcinoma (VSCC) remains unclear. Here, we report a comprehensive analysis of programmed death-ligand 1 (PD-L1) and immune cell infiltrates in VSCC and establish immunoscore models for classification of the disease. Methods: Archival tissues, immunohistochemistry, and digital quantification were used to investigate the number of CD4+, CD8+, CD68+, CD14+, FoxP3+, and PD-L1+ cells in epithelial and stromal compartments of VSCC (n=117). Immunoscores were developed by using these parameters and applying the least absolute shrinkage and selection operator (LASSO) to identify predictors of survival. Immunoscores were then integrated with HPV status, as determined by mRNA in situ hybridization, to construct internally validated nomograms. The models were assessed using Harrell's concordance-index (c-index), calibration plots, Kaplan-Meier curves, and decision curve analysis. Results: Advanced VSCC (FIGO stage III/IV) was characterized by high numbers of CD68+ macrophages and PD-L1+ cells (Spearmans correlation, ρ>0.80) in the epithelium. PD-L1 status independently predicted poor progression free survival (PFS) (HR=1.80, (95% CI (1.024-3.170), p=0.041). High stromal CD68+ or CD14+ myeloid cell infiltration was associated with poor PFS and disease specific survival (DSS) (p<0.05). Immunological parameters were used to determine immunoscores. Immunoscore<sup>PFS</sup> and immunoscore<sup>DSS</sup> were independent prognosticators of PFS (p=0.001) and DSS (p=0.007) respectively. Integrating immunoscores with HPV status (IS-HPV index) improved the prognostic impact of the models. The c-index of IS-HPV index<sup>PFS</sup> was 0.750 for prediction of PFS compared to 0.666 for HPV status and 0.667 for immunoscore<sup>PFS</sup>. The c-index of IS-HPV index<sup>DSS</sup> was 0.752 for predicting DSS compared to 0.631 for HPV status and 0.715 for immunoscore<sup>DSS</sup>. Conclusion: In summary, an index based on HPV status and an immunoscore built on PD-L1 expression and immune cell infiltrates could potentially serve as a prognostic tool to refine risk stratification in VSCC. Further validation is warranted to demonstrate clinical utility.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.1101/2024.09.10.24313423
Sogol Alikarami, Hamid Harandi, Ali Jahanshahi, Seyed Sina Zakavi, Negin Frounchi, Mehrdad Mahalleh, Sarah Momtazmanesh
Background: In recent years, the tumor management strategies have focused on less invasive methods, aiming to yield optimal efficacy while minimizing further complications and enhancing the overall outcome of patients. High-intensity focused ultrasound (HIFU), a known thermal ablative technique, has shown promising results in breast cancer treatment. Therefore, we performed this systematic review and meta-analysis to assess the clinical, histopathologic, immunologic, and radiologic outcomes of HIFU ablative therapy and its complications in patients with primary breast cancer. Methods: We searched PubMed and Scopus databases to identify the eligible articles. Data extraction was conducted by two independent authors. A random effect model was employed to pool the proportion of remaining tumor after HIFU therapy in breast cancer. Pooled CD4/CD8 ratio mean difference between HIFU and radical mastectomy was ,measured using a fixed effect model. Results: We included 26 studies and 677 participants in the systematic review. Tumor necrosis rates varied, with 4 studies reporting less than 50% complete necrosis and 5 more than 50%. Two studies observed HIFU-induced disturbances in microvasculature of the targeted tissue. Six noted no contrast enhancement in successfully treated areas, two observed a thin rim indicating necrosis or fibrosis, and four reported a persistent enhancement in MRI images associated with a residual viable tumor. The weighted proportion of patients with residual tumor was 58.45 (95% C: 45.48, 71.42). The CD4/CD8 ratio was higher in the HIFU group, with a weighted mean difference of 0.6 (95% CI: 0.41, 0.78). The most prevalent side effects were pain (47.14%) and skin burn (2.59%). Conclusions: HIFU is a relatively safe procedure for treatment of breast cancer as an independent or conjugated therapy and its effectiveness is promising regarding histopathological response, immunological reactivity, and vascular damage in the targeted area.
{"title":"High-intensity focused ultrasound in treatment of primary breast cancer: a systematic review and meta-analysis","authors":"Sogol Alikarami, Hamid Harandi, Ali Jahanshahi, Seyed Sina Zakavi, Negin Frounchi, Mehrdad Mahalleh, Sarah Momtazmanesh","doi":"10.1101/2024.09.10.24313423","DOIUrl":"https://doi.org/10.1101/2024.09.10.24313423","url":null,"abstract":"Background: In recent years, the tumor management strategies have focused on less invasive methods, aiming to yield optimal efficacy while minimizing further complications and enhancing the overall outcome of patients. High-intensity focused ultrasound (HIFU), a known thermal ablative technique, has shown promising results in breast cancer treatment. Therefore, we performed this systematic review and meta-analysis to assess the clinical, histopathologic, immunologic, and radiologic outcomes of HIFU ablative therapy and its complications in patients with primary breast cancer.\u0000Methods: We searched PubMed and Scopus databases to identify the eligible articles. Data extraction was conducted by two independent authors. A random effect model was employed to pool the proportion of remaining tumor after HIFU therapy in breast cancer. Pooled CD4/CD8 ratio mean difference between HIFU and radical mastectomy was ,measured using a fixed effect model. Results: We included 26 studies and 677 participants in the systematic review. Tumor necrosis rates varied, with 4 studies reporting less than 50% complete necrosis and 5 more than 50%. Two studies observed HIFU-induced disturbances in microvasculature of the targeted tissue. Six noted no contrast enhancement in successfully treated areas, two observed a thin rim indicating necrosis or fibrosis, and four reported a persistent enhancement in MRI images associated with a residual viable tumor. The weighted proportion of patients with residual tumor was 58.45 (95% C: 45.48, 71.42). The CD4/CD8 ratio was higher in the HIFU group, with a weighted mean difference of 0.6 (95% CI: 0.41, 0.78). The most prevalent side effects were pain (47.14%) and skin burn (2.59%).\u0000Conclusions: HIFU is a relatively safe procedure for treatment of breast cancer as an independent or conjugated therapy and its effectiveness is promising regarding histopathological response, immunological reactivity, and vascular damage in the targeted area.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.1101/2024.09.09.24313374
Yehuda Brody, Ben Yellin, Itamar Sela, Yehonatan Elon, Igor Puzanov, Hovav Nechushtan, Alona Zerkuch, Maya Gottfried, Rivka Katzenelson, Mor Moskovitz, Adva Levy-Barda, Michal Lotem, Raya Leibowitz, Yanyan Lou, Adam Dicker, David R Gandara, Kimberly McGregor
Despite the approval of PD-(L)1 inhibitors for the first-line treatment of all metastatic, driver- negative, non-small cell lung cancer patients (mNSCLC) in the United States since 2018, there still is a lack discerning biomarkers to predict which patients will derive significant benefit. Tumor expression of programmed-death ligand 1 (PD-L1), measured as the tumor proportion score (TPS), is a standard biomarker approved for the selection of initial therapy. Tumor mutational burden (TMB), a promising biomarker, thought to represent the tumors ability to engage the hosts immune system, has demonstrated clinical utility primarily in the context of immunotherapy monotherapy. PROphetNSCLC, a test developed through proteomic analysis and machine learning, provides a novel approach by capturing biological processes from both tumor and host. In a previously published study, PROphetNSCLC, was validated to correlate with the probability of clinical benefit, independent of but also complementary to PD-L1 expression levels predicting specific treatment-related survival outcomes. Utilizing available tumor TMB measurements from this investigation, we sought to assess the correlation between the PROphetNSCLC clinical benefit probability score and TMB measurement. PROphetNSCLC demonstrated a correlation with various outcomes from PD-(L)1 inhibitor treatment independent of TMB status, whereas TMB did not exhibit an association with outcomes. This finding emphasizes the significance in of novel systemic biomarkers in refining personalized treatment strategies for mNSCLC.
{"title":"Plasma proteome-based test (PROphetNSCLC) predicts response to immune checkpoint inhibitors (ICI) independent of tumor programmed death-ligand 1(PD-L1) expression and tumor mutational burden (TMB)","authors":"Yehuda Brody, Ben Yellin, Itamar Sela, Yehonatan Elon, Igor Puzanov, Hovav Nechushtan, Alona Zerkuch, Maya Gottfried, Rivka Katzenelson, Mor Moskovitz, Adva Levy-Barda, Michal Lotem, Raya Leibowitz, Yanyan Lou, Adam Dicker, David R Gandara, Kimberly McGregor","doi":"10.1101/2024.09.09.24313374","DOIUrl":"https://doi.org/10.1101/2024.09.09.24313374","url":null,"abstract":"Despite the approval of PD-(L)1 inhibitors for the first-line treatment of all metastatic, driver- negative, non-small cell lung cancer patients (mNSCLC) in the United States since 2018, there still is a lack discerning biomarkers to predict which patients will derive significant benefit. Tumor expression of programmed-death ligand 1 (PD-L1), measured as the tumor proportion score (TPS), is a standard biomarker approved for the selection of initial therapy. Tumor mutational burden (TMB), a promising biomarker, thought to represent the tumors ability to engage the hosts immune system, has demonstrated clinical utility primarily in the context of immunotherapy monotherapy. PROphetNSCLC, a test developed through proteomic analysis and machine learning, provides a novel approach by capturing biological processes from both tumor and host. In a previously published study, PROphetNSCLC, was validated to correlate with the probability of clinical benefit, independent of but also complementary to PD-L1 expression levels predicting specific treatment-related survival outcomes. Utilizing available tumor TMB measurements from this investigation, we sought to assess the correlation between the PROphetNSCLC clinical benefit probability score and TMB measurement. PROphetNSCLC demonstrated a correlation with various outcomes from PD-(L)1 inhibitor treatment independent of TMB status, whereas TMB did not exhibit an association with outcomes. This finding emphasizes the significance in of novel systemic biomarkers in refining personalized treatment strategies for mNSCLC.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.1101/2024.09.10.24313417
Alind Gupta, Luke Segars, David Singletary, Johan Liseth Hansen, Kirk Geale, Anmol Arora, Manuel Gomes, Sreeram Ramagopalan, Winson Cheung, Paul Arora
Single-arm trials supplemented with external comparator arm(s) (ECA) derived from real-world data are sometimes used when randomized trials are infeasible. However, due to data sharing restrictions, privacy/security concerns, or for logistical reasons, patient-level real-world data may not be available to researchers for analysis. Instead, it may be possible to use generative models to construct synthetic data from the real-world dataset that can then be freely shared with researchers. Although the use of generative models and synthetic data is gaining prominence, the extent to which a synthetic data ECA can replace original data while preserving patient privacy in small samples is unclear. Objective: To compare the efficacy of nivolumab + ipilimumab combination therapy ('experimental arm') versus nivolumab monotherapy ('control arm') in patients with metastatic non-small cell lung cancer (mNSCLC) using real-world data from two real-world databases ('original ECA'), and synthetic data versions of these datasets ('synthetic ECA'), with the aim of validating synthetic data for use in ECA analysis. Study design: Non-randomized analyses of treatment efficacy comparing the experimental arm to the (i) original ECA and (ii) synthetic ECA, with baseline confounding adjustment. Data sources: The experimental arm is from the Lung-MAP no-match substudy S1400I (NCT02785952) provided by National Clinical Trials Network (NCTN) in the United States. The real-world data source for the ECA is data from population-based oncology data from the Canadian province of Alberta, and from Nordic countries in Europe, specifically Denmark and Norway.
{"title":"External Control Arm with Synthetic Real-world Data for Comparative Oncology using Single Trial Arm Evidence (ECLIPSE): A Case Study using Lung-MAP S1400I","authors":"Alind Gupta, Luke Segars, David Singletary, Johan Liseth Hansen, Kirk Geale, Anmol Arora, Manuel Gomes, Sreeram Ramagopalan, Winson Cheung, Paul Arora","doi":"10.1101/2024.09.10.24313417","DOIUrl":"https://doi.org/10.1101/2024.09.10.24313417","url":null,"abstract":"Single-arm trials supplemented with external comparator arm(s) (ECA) derived from real-world data are sometimes used when randomized trials are infeasible. However, due to data sharing restrictions, privacy/security concerns, or for logistical reasons, patient-level real-world data may not be available to researchers for analysis. Instead, it may be possible to use generative models to construct synthetic data from the real-world dataset that can then be freely shared with researchers. Although the use of generative models and synthetic data is gaining prominence, the extent to which a synthetic data ECA can replace original data while preserving patient privacy in small samples is unclear.\u0000Objective: To compare the efficacy of nivolumab + ipilimumab combination therapy ('experimental arm') versus nivolumab monotherapy ('control arm') in patients with metastatic non-small cell lung cancer (mNSCLC) using real-world data from two real-world databases ('original ECA'), and synthetic data versions of these datasets ('synthetic ECA'), with the aim of validating synthetic data for use in ECA analysis.\u0000Study design: Non-randomized analyses of treatment efficacy comparing the experimental arm to the (i) original ECA and (ii) synthetic ECA, with baseline confounding adjustment.\u0000Data sources: The experimental arm is from the Lung-MAP no-match substudy S1400I (NCT02785952) provided by National Clinical Trials Network (NCTN) in the United States. The real-world data source for the ECA is data from population-based oncology data from the Canadian province of Alberta, and from Nordic countries in Europe, specifically Denmark and Norway.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"232 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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