Pub Date : 2024-07-27DOI: 10.1101/2024.07.26.24310994
Victor de la Oliva, Alberto Esteban-Medina, Laura Alejos, Dolores Munoyerro-Muniz, Roman Villegas, Joaquin Dopazo, Carlos Loucera
This study presents the development of an early prediction model for high-grade serous ovarian cancer (HGSOC) using real-world data from the Andalusian Health Population Database (BPS), containing electronic health records (EHR) of over 15 million patients. Leveraging the extensive data availability, the model aims to identify individuals at high risk of HGSOC without the need for specific tumor markers or prior stratification into risk groups. Utilizing an Explainable Boosting Machine (EBM) algorithm, the model incorporates diverse clinical variables including demographics, chronic diseases, symptoms, blood test results, and healthcare utilization patterns. The model was trained and validated using a total of 3,088 HGSOC patients diagnosed between 2018 and 2022 along with 114,942 controls of similar characteristics, to emulate the prevalence of the disease, achieving a sensitivity of 0.65 and a specificity of 0.85. This study underscores the importance of using patient data from the general population, demonstrating that effective early detection models can be developed from routinely collected healthcare data. The approach addresses limitations of traditional screening methods by providing a cost-effective and broadly applicable tool for early cancer detection, potentially improving patient outcomes through timely interventions. The interpretability of the early prediction model also offers insights into the most significant predictors of cancer risk, further enhancing its utility in clinical settings.
{"title":"Early prediction of ovarian cancer risk based on real world data","authors":"Victor de la Oliva, Alberto Esteban-Medina, Laura Alejos, Dolores Munoyerro-Muniz, Roman Villegas, Joaquin Dopazo, Carlos Loucera","doi":"10.1101/2024.07.26.24310994","DOIUrl":"https://doi.org/10.1101/2024.07.26.24310994","url":null,"abstract":"This study presents the development of an early prediction model for high-grade serous ovarian cancer (HGSOC) using real-world data from the Andalusian Health Population Database (BPS), containing electronic health records (EHR) of over 15 million patients. Leveraging the extensive data availability, the model aims to identify individuals at high risk of HGSOC without the need for specific tumor markers or prior stratification into risk groups. Utilizing an Explainable Boosting Machine (EBM) algorithm, the model incorporates diverse clinical variables including demographics, chronic diseases, symptoms, blood test results, and healthcare utilization patterns. The model was trained and validated using a total of 3,088 HGSOC patients diagnosed between 2018 and 2022 along with 114,942 controls of similar characteristics, to emulate the prevalence of the disease, achieving a sensitivity of 0.65 and a specificity of 0.85. This study underscores the importance of using patient data from the general population, demonstrating that effective early detection models can be developed from routinely collected healthcare data. The approach addresses limitations of traditional screening methods by providing a cost-effective and broadly applicable tool for early cancer detection, potentially improving patient outcomes through timely interventions. The interpretability of the early prediction model also offers insights into the most significant predictors of cancer risk, further enhancing its utility in clinical settings.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141785680","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 : 2024-07-27DOI: 10.1101/2024.07.26.24311051
Jialu Fang, Wenliang Wang, Guosheng Yin
The calibration-free odds type (CFO-type) of designs, as data-driven decision-making Bayesian approaches, leverage historical cumulative data across various dose levels, primarily aiming at identifying the maximum tolerated dose (MTD). Inheriting the ideas from game theory or 'tug-of-war', CFO mimics the games of force: one pushes the dose down while the other pushes it up. Extensive simulations validate that CFO-type designs maintain an optimal balance between efficiency and safety in MTD identification, with performance metrics that are comparable to, or occasionally surpass the state-of-the-art methods. This article primarily introduces the R package CFO for implementing and assessing CFO-type designs in phase I clinical trials. Besides, we propose integrating the mechanism of exploration and exploitation from reinforcement learning into the CFO design, leading to a novel approach: the randomized CFO (rCFO) design. The CFO package encompasses various variants tailored to accommodate different scenarios. Beyond the fundamental CFO design, these include the two-dimensional CFO (2dCFO) designed for drug-combination trials, accumulative CFO (aCFO) for accruing all dose information, time-to-event CFO (TITE-CFO), and fractional CFO (fCFO) which are developed to specifically address late-onset toxicity. Moreover, hybrid designs such as TITE-aCFO and f-aCFO, which integrate both late-onset toxicity and all dose information for decision making, are also included. CFO provides a robust set of functions used for determining subsequent cohort doses, selecting the MTD, and conducting simulations to evaluate design operating characteristics. The properties and results are presented to trial investigators through simple textual and graphical outputs. The user-friendly interface, adaptability to various design considerations, and the comprehensive implementation of CFO-type designs position CFO as a noteworthy tool for phase I clinical trials.
{"title":"CFO: Calibration-Free Odds Bayesian Designs for Dose Finding in Clinical Trials","authors":"Jialu Fang, Wenliang Wang, Guosheng Yin","doi":"10.1101/2024.07.26.24311051","DOIUrl":"https://doi.org/10.1101/2024.07.26.24311051","url":null,"abstract":"The calibration-free odds type (CFO-type) of designs, as data-driven decision-making Bayesian approaches, leverage historical cumulative data across various dose levels, primarily aiming at identifying the maximum tolerated dose (MTD). Inheriting the ideas from game theory or 'tug-of-war', CFO mimics the games of force: one pushes the dose down while the other pushes it up. Extensive simulations validate that CFO-type designs maintain an optimal balance between efficiency and safety in MTD identification, with performance metrics that are comparable to, or occasionally surpass the state-of-the-art methods. This article primarily introduces the R package <strong>CFO</strong> for implementing and assessing CFO-type designs in phase I clinical trials. Besides, we propose integrating the mechanism of exploration and exploitation from reinforcement learning into the CFO design, leading to a novel approach: the randomized CFO (rCFO) design. The <strong>CFO</strong> package encompasses various variants tailored to accommodate different scenarios. Beyond the fundamental CFO design, these include the two-dimensional CFO (2dCFO) designed for drug-combination trials, accumulative CFO (aCFO) for accruing all dose information, time-to-event CFO (TITE-CFO), and fractional CFO (fCFO) which are developed to specifically address late-onset toxicity. Moreover, hybrid designs such as TITE-aCFO and f-aCFO, which integrate both late-onset toxicity and all dose information for decision making, are also included. <strong>CFO</strong> provides a robust set of functions used for determining subsequent cohort doses, selecting the MTD, and conducting simulations to evaluate design operating characteristics. The properties and results are presented to trial investigators through simple textual and graphical outputs. The user-friendly interface, adaptability to various design considerations, and the comprehensive implementation of CFO-type designs position <strong>CFO</strong> as a noteworthy tool for phase I clinical trials.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141785683","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 : 2024-07-26DOI: 10.1101/2024.07.25.24309825
Jorge Oscanoa, Helen Ross-Adams, Abu Z M Dayem Ullah, Trupti S Kolvekar, Lavanya Sivapalan, Emanuela Gadaleta, Graeme J Thorn, Maryam Abdollahyan, Ahmet Imrali, Amina Saad, Rhiannon Roberts, Christine Hughes, PCRFTB, Hemant M Kocher, Claude Chelala
The Pancreatic Expression Database (PED) is a powerful resource dedicated to the mining and analysis of pancreatic -omics datasets. Here, we demonstrate the biological interpretations that are possible because of vital updates that have transformed PED into a dynamic analytics hub accommodating an extensive range of publicly available datasets. PED now hosts clinical and molecular datasets from four primary sources (Cancer Genome Atlas, International Cancer Genome Consortium, Cancer Cell Line Encyclopaedia and Genomics Evidence Neoplasia Information Exchange) that together form the foundation of omics profiling of pancreatic malignancies and related lesions (n=7,760 specimens). Several user-friendly analytical tools to explore and integrate the molecular data derived from these primary specimens and cell lines are now available. Crucially, PED is integrated as the data access point for Pancreatic Cancer Research Fund Tissue Bank - the only national pancreatic cancer biobank in the UK. This will pioneer a new era of biobanking to promote collaborative studies and effective sharing of multi-modal molecular, histopathology and imaging data from biobank samples (>60 000 tissue samples from >3400 cases and controls; 2,037 H&E images from 349 donors) and accelerate validation of in silico findings in patient-derived material. These updates place PED at the analytical forefront of pancreatic biomarker-based research, providing the user community with a distinct resource to facilitate hypothesis-testing on public data, validate novel research findings, and access curated, high-quality patient tissues for translational research. To demonstrate the practical utility of PED, we investigate somatic variants associated with established transcriptomic subtypes and disease prognosis: several patient-specific variants are clinically actionable and may be leveraged for precision medicine.
{"title":"A central research portal for mining pancreatic clinical and molecular datasets and accessing biobanked samples","authors":"Jorge Oscanoa, Helen Ross-Adams, Abu Z M Dayem Ullah, Trupti S Kolvekar, Lavanya Sivapalan, Emanuela Gadaleta, Graeme J Thorn, Maryam Abdollahyan, Ahmet Imrali, Amina Saad, Rhiannon Roberts, Christine Hughes, PCRFTB, Hemant M Kocher, Claude Chelala","doi":"10.1101/2024.07.25.24309825","DOIUrl":"https://doi.org/10.1101/2024.07.25.24309825","url":null,"abstract":"The Pancreatic Expression Database (PED) is a powerful resource dedicated to the mining and analysis of pancreatic -omics datasets. Here, we demonstrate the biological interpretations that are possible because of vital updates that have transformed PED into a dynamic analytics hub accommodating an extensive range of publicly available datasets. PED now hosts clinical and molecular datasets from four primary sources (Cancer Genome Atlas, International Cancer Genome Consortium, Cancer Cell Line Encyclopaedia and Genomics Evidence Neoplasia Information Exchange) that together form the foundation of omics profiling of pancreatic malignancies and related lesions (n=7,760 specimens). Several user-friendly analytical tools to explore and integrate the molecular data derived from these primary specimens and cell lines are now available. Crucially, PED is integrated as the data access point for Pancreatic Cancer Research Fund Tissue Bank - the only national pancreatic cancer biobank in the UK. This will pioneer a new era of biobanking to promote collaborative studies and effective sharing of multi-modal molecular, histopathology and imaging data from biobank samples (>60 000 tissue samples from >3400 cases and controls; 2,037 H&E images from 349 donors) and accelerate validation of in silico findings in patient-derived material. These updates place PED at the analytical forefront of pancreatic biomarker-based research, providing the user community with a distinct resource to facilitate hypothesis-testing on public data, validate novel research findings, and access curated, high-quality patient tissues for translational research. To demonstrate the practical utility of PED, we investigate somatic variants associated with established transcriptomic subtypes and disease prognosis: several patient-specific variants are clinically actionable and may be leveraged for precision medicine.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141775918","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 : 2024-07-24DOI: 10.1101/2024.07.24.24310921
Jenifer Brea-Iglesias, Ana Oitaben, Sonia Zumalave, Bernardo Rodriguez-Martin, Maria Gallardo-Gomez, Martin Santamarina, Ana Pequeno-Valtierra, Laura Juaneda-Magdalena, Ramon Garcia-Escudero, Jose Luis Lopez-Cedrun, Maximo Fraga, Jose MC Tubio, Monica Martinez-Fernandez
The relevant role of LINE-1 (L1) retrotransposition in cancer has been recurrently demonstrated in recent years. However, their repetitive nature hampers their identification and detection, hence remaining inaccessible for clinical practice. Also, its clinical relevance for cancer patients is still limited. Here, we develop a new method to quantify L1 activation, called RetroTest, based on targeted sequencing and a sophisticated bioinformatic pipeline, allowing its application in tumor biopsies. First, we performed the benchmarking of the method and confirmed its high specificity and reliability. Then, we unravel the L1 activation in HNSCC according to a more extensive cohort including all the HNSCC tumor stages. Our results confirm that RetroTest is remarkably efficient for L1 detection in tumor biopsies, reaching a high sensitivity and specificity. In addition, L1 retrotransposition estimation reveals a surprisingly early activation in HNSCC progression, contrary to its classical association with advanced tumor stages. This early activation together with the genomic mutational profiling of normal adjacent tissues supports field cancerization process in this tumor. These results underline the importance of estimating L1 retrotransposition in clinical practice towards an earlier and more efficient diagnosis in HNSCC.
{"title":"RetroTest unravels LINE-1 retrotransposition in Head and Neck Squamous Cell Carcinoma","authors":"Jenifer Brea-Iglesias, Ana Oitaben, Sonia Zumalave, Bernardo Rodriguez-Martin, Maria Gallardo-Gomez, Martin Santamarina, Ana Pequeno-Valtierra, Laura Juaneda-Magdalena, Ramon Garcia-Escudero, Jose Luis Lopez-Cedrun, Maximo Fraga, Jose MC Tubio, Monica Martinez-Fernandez","doi":"10.1101/2024.07.24.24310921","DOIUrl":"https://doi.org/10.1101/2024.07.24.24310921","url":null,"abstract":"The relevant role of LINE-1 (L1) retrotransposition in cancer has been recurrently demonstrated in recent years. However, their repetitive nature hampers their identification and detection, hence remaining inaccessible for clinical practice. Also, its clinical relevance for cancer patients is still limited. Here, we develop a new method to quantify L1 activation, called RetroTest, based on targeted sequencing and a sophisticated bioinformatic pipeline, allowing its application in tumor biopsies. First, we performed the benchmarking of the method and confirmed its high specificity and reliability. Then, we unravel the L1 activation in HNSCC according to a more extensive cohort including all the HNSCC tumor stages. Our results confirm that RetroTest is remarkably efficient for L1 detection in tumor biopsies, reaching a high sensitivity and specificity. In addition, L1 retrotransposition estimation reveals a surprisingly early activation in HNSCC progression, contrary to its classical association with advanced tumor stages. This early activation together with the genomic mutational profiling of normal adjacent tissues supports field cancerization process in this tumor. These results underline the importance of estimating L1 retrotransposition in clinical practice towards an earlier and more efficient diagnosis in HNSCC.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141775874","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 : 2024-07-24DOI: 10.1101/2024.07.23.24310891
Alexander Dean Sherry, Pavlos Msaouel, Gabrielle Kupferman, Timothy Lin, Joseph Abi Jaoude, Ramez Kouzy, Molly B. El Alam, Roshal Patel, Alex Koong, Christine Lin, Adina Passy, Avital Miller, Esther Beck, Clifton David Fuller, Tomer Meirson, Zachary David McCaw, Ethan B. Ludmir
Most oncology trials define superiority of an experimental therapy compared to a control therapy according to frequentist significance thresholds, which are widely misinterpreted. Posterior probability distributions computed by Bayesian inference may be more intuitive measures of uncertainty, particularly for measures of clinical benefit such as the minimum clinically important difference (MCID). Here, we manually reconstructed 194,129 individual patient-level outcomes across 230 phase III, superiority-design, oncology trials. Posteriors were calculated by Markov Chain Monte Carlo sampling using standard priors. All trials interpreted as positive had probabilities > 90% for marginal benefits (HR < 1). However, 38% of positive trials had ≤ 90% probabilities of achieving the MCID (HR < 0.8), even under an enthusiastic prior. A subgroup analysis of 82 trials that led to regulatory approval showed 30% had ≤ 90% probability for meeting the MCID under an enthusiastic prior. Conversely, 24% of negative trials had > 90% probability of achieving marginal benefits, even under a skeptical prior, including 12 trials with a primary endpoint of overall survival. Lastly, a phase III oncology-specific prior from a previous work, which uses published summary statistics rather than reconstructed data to compute posteriors, validated the individual patient-level data findings. Taken together, these results suggest that Bayesian models add considerable unique interpretative value to phase III oncology trials and provide a robust solution for overcoming the discrepancies between refuting the null hypothesis and obtaining a MCID.
大多数肿瘤试验都是根据频数显著性阈值来定义实验疗法与对照疗法的优劣，而频数显著性阈值被广泛误读。通过贝叶斯推理计算出的后验概率分布可能更能直观地衡量不确定性，特别是对于最小临床重要性差异（MCID）等临床获益指标。在此，我们手动重建了230项III期、优越性设计、肿瘤试验中的194129个患者水平结果。后验是通过使用标准先验的马尔可夫链蒙特卡洛抽样计算得出的。所有被解释为阳性的试验的边际效益（HR <1）概率均为 90%。然而，38%的阳性试验达到MCID（HR <0.8）的概率≤90%，即使在热情先验条件下也是如此。对 82 项获得监管部门批准的试验进行的分组分析表明，在积极先验条件下，30% 的试验达到 MCID 的概率不超过 90%。相反，24%的阴性试验即使在怀疑先验条件下也有 90% 的概率达到边际效益，其中包括 12 项主要终点为总生存期的试验。最后，前一项研究中针对 III 期肿瘤的先验数据验证了单个患者层面的数据结果，该先验数据使用的是已发表的汇总统计数据而不是重构数据来计算后验值。总之，这些结果表明，贝叶斯模型为 III 期肿瘤学试验增加了相当大的独特解释价值，并为克服反驳零假设和获得 MCID 之间的差异提供了一个稳健的解决方案。
{"title":"Towards Treatment Effect Interpretability: A Bayesian Re-analysis of 194,129 Patient Outcomes Across 230 Oncology Trials","authors":"Alexander Dean Sherry, Pavlos Msaouel, Gabrielle Kupferman, Timothy Lin, Joseph Abi Jaoude, Ramez Kouzy, Molly B. El Alam, Roshal Patel, Alex Koong, Christine Lin, Adina Passy, Avital Miller, Esther Beck, Clifton David Fuller, Tomer Meirson, Zachary David McCaw, Ethan B. Ludmir","doi":"10.1101/2024.07.23.24310891","DOIUrl":"https://doi.org/10.1101/2024.07.23.24310891","url":null,"abstract":"Most oncology trials define superiority of an experimental therapy compared to a control therapy according to frequentist significance thresholds, which are widely misinterpreted. Posterior probability distributions computed by Bayesian inference may be more intuitive measures of uncertainty, particularly for measures of clinical benefit such as the minimum clinically important difference (MCID). Here, we manually reconstructed 194,129 individual patient-level outcomes across 230 phase III, superiority-design, oncology trials. Posteriors were calculated by Markov Chain Monte Carlo sampling using standard priors. All trials interpreted as positive had probabilities > 90% for marginal benefits (HR < 1). However, 38% of positive trials had ≤ 90% probabilities of achieving the MCID (HR < 0.8), even under an enthusiastic prior. A subgroup analysis of 82 trials that led to regulatory approval showed 30% had ≤ 90% probability for meeting the MCID under an enthusiastic prior. Conversely, 24% of negative trials had > 90% probability of achieving marginal benefits, even under a skeptical prior, including 12 trials with a primary endpoint of overall survival. Lastly, a phase III oncology-specific prior from a previous work, which uses published summary statistics rather than reconstructed data to compute posteriors, validated the individual patient-level data findings. Taken together, these results suggest that Bayesian models add considerable unique interpretative value to phase III oncology trials and provide a robust solution for overcoming the discrepancies between refuting the null hypothesis and obtaining a MCID.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141775919","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 : 2024-07-24DOI: 10.1101/2024.07.24.24310768
Yannik Severin, Yasmin Festl, Tobias Matthieu Benoit, Rebekka Wegmann, Benjamin D. Hale, Michael Roiss, Anne-Kathrin Kienzler, Thomas Pabst, Michael Scharl, Shinichi Sunagawa, Markus G. Manz, Antonia M.S. Mueller, Berend Snijder
Acute myeloid leukemia (AML) is characterized by malignant myeloid precursors that span a cellular hierarchy from dedifferentiated leukemic stem cells to mature blasts. While the diagnostic and prognostic importance of AML blast maturation is increasingly recognized, personalized therapies are currently not tailored to a patients individual makeup of this cellular hierarchy. In this study, we use multiplexed image-based ex vivo drug screening (pharmacoscopy) to systematically quantify the drug sensitivity across the cellular hierarchy of AML patients. We analyzed 174 prospective and longitudinal patient samples from 44 newly diagnosed AML patients, which indicated that differences in the AML hierarchy significantly identified poor responses to first-line therapy, outperforming European LeukemiaNet (ELN) criteria. Critically, drug response profiling across the AML hierarchy of each patient improved the accuracy of predicting patient response to first-line therapy (AUC 0.91), and revealed alternative individualized treatment options targeting the complete AML hierarchy of non-responding patients. We confirmed these findings in an independent cohort of 26 relapsed/refractory AML patients, for whom pan-hierarchy response profiling improved response predictions post hoc. Overall, our results quantify the clinical importance of therapeutically targeting the complete cellular hierarchy of newly diagnosed AML, and identify multiplexed image-based ex vivo drug screening to enable quantification and targeting of the AML maturation hierarchy for improved personalized treatment.
{"title":"The cellular hierarchy of acute myeloid leukemia informs personalized treatment","authors":"Yannik Severin, Yasmin Festl, Tobias Matthieu Benoit, Rebekka Wegmann, Benjamin D. Hale, Michael Roiss, Anne-Kathrin Kienzler, Thomas Pabst, Michael Scharl, Shinichi Sunagawa, Markus G. Manz, Antonia M.S. Mueller, Berend Snijder","doi":"10.1101/2024.07.24.24310768","DOIUrl":"https://doi.org/10.1101/2024.07.24.24310768","url":null,"abstract":"Acute myeloid leukemia (AML) is characterized by malignant myeloid precursors that span a cellular hierarchy from dedifferentiated leukemic stem cells to mature blasts. While the diagnostic and prognostic importance of AML blast maturation is increasingly recognized, personalized therapies are currently not tailored to a patients individual makeup of this cellular hierarchy. In this study, we use multiplexed image-based ex vivo drug screening (pharmacoscopy) to systematically quantify the drug sensitivity across the cellular hierarchy of AML patients. We analyzed 174 prospective and longitudinal patient samples from 44 newly diagnosed AML patients, which indicated that differences in the AML hierarchy significantly identified poor responses to first-line therapy, outperforming European LeukemiaNet (ELN) criteria. Critically, drug response profiling across the AML hierarchy of each patient improved the accuracy of predicting patient response to first-line therapy (AUC 0.91), and revealed alternative individualized treatment options targeting the complete AML hierarchy of non-responding patients. We confirmed these findings in an independent cohort of 26 relapsed/refractory AML patients, for whom pan-hierarchy response profiling improved response predictions post hoc. Overall, our results quantify the clinical importance of therapeutically targeting the complete cellular hierarchy of newly diagnosed AML, and identify multiplexed image-based ex vivo drug screening to enable quantification and targeting of the AML maturation hierarchy for improved personalized treatment.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141775875","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 : 2024-07-24DOI: 10.1101/2024.07.23.24310860
Bowen Du, Han Hong, Chaopeng Tang, Li Fan, Jie Dong, JIngping Ge, Xuejun Shang
Background: Evidence from observational studies suggests an association between mental disorders and cancers. However, the causality of this association remains unclear. Methods: We collected genome-wide association study (GWAS) summary statistics of five mental disorders from the Psychiatric Genomics Consortium (PGC, 72,517 to 500,199 participants), paired with GWAS summary statistics of the risks of 18 cancer types from the UK Biobank (167,020 to 361,194 participants) and FinnGen database (110,521 to 264,701 participants). We conducted univariable and multivariable Mendelian randomization (MR) analyses to explore the causal relationships. Results: We identified ten causal associations between mental disorders and cancer risks. Notably, anorexia nervosa (AN) exhibits a causal association with a decreased risk of prostate cancer (β = -0.30, p = 1.61 × 10-6) and an elevated risk for stomach cancer (β = 0.47, p = 5.3 × 10-3). Bipolar disorder (BD) is causally linked to a reduced risk of pancreatic cancer (β = -5.13 × 10-4, p = 3.2 × 10-3). Major depression disorder (MDD) is causally associated with an elevated risk of bladder cancer (β = 1.84 × 10-3, p = 5.0 × 10-4) and kidney cancer (β = 1.40 × 10-3, p = 4.9 × 10-3). Additionally, we found the causal effect of skin melanoma on BD (β = -10.39, p = 2.1 × 10-4) and Schizophrenia (SCZ, β = -7.42, p = 3.3 × 10-4) with a bi-directional MR analysis. Moreover, we identified leukocyte count as a causal mediator of a causal association between AN and stomach cancer with a two-step MR analysis. Conclusions: In summary, our MR analysis reveals that mental disorders were causally associated with cancer risks.
{"title":"Causal Relationship between Mental Disorders and Cancers: a Mendelian Randomization Study","authors":"Bowen Du, Han Hong, Chaopeng Tang, Li Fan, Jie Dong, JIngping Ge, Xuejun Shang","doi":"10.1101/2024.07.23.24310860","DOIUrl":"https://doi.org/10.1101/2024.07.23.24310860","url":null,"abstract":"Background: Evidence from observational studies suggests an association between mental disorders and cancers. However, the causality of this association remains unclear. Methods: We collected genome-wide association study (GWAS) summary statistics of five mental disorders from the Psychiatric Genomics Consortium (PGC, 72,517 to 500,199 participants), paired with GWAS summary statistics of the risks of 18 cancer types from the UK Biobank (167,020 to 361,194 participants) and FinnGen database (110,521 to 264,701 participants). We conducted univariable and multivariable Mendelian randomization (MR) analyses to explore the causal relationships. Results: We identified ten causal associations between mental disorders and cancer risks. Notably, anorexia nervosa (AN) exhibits a causal association with a decreased risk of prostate cancer (β = -0.30, p = 1.61 × 10-6) and an elevated risk for stomach cancer (β = 0.47, p = 5.3 × 10-3). Bipolar disorder (BD) is causally linked to a reduced risk of pancreatic cancer (β = -5.13 × 10-4, p = 3.2 × 10-3). Major depression disorder (MDD) is causally associated with an elevated risk of bladder cancer (β = 1.84 × 10-3, p = 5.0 × 10-4) and kidney cancer (β = 1.40 × 10-3, p = 4.9 × 10-3). Additionally, we found the causal effect of skin melanoma on BD (β = -10.39, p = 2.1 × 10-4) and Schizophrenia (SCZ, β = -7.42, p = 3.3 × 10-4) with a bi-directional MR analysis. Moreover, we identified leukocyte count as a causal mediator of a causal association between AN and stomach cancer with a two-step MR analysis. Conclusions: In summary, our MR analysis reveals that mental disorders were causally associated with cancer risks.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141775879","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 : 2024-07-22DOI: 10.1101/2024.07.22.24310819
Aaron J. Bernstein, Renske Keeman, Amber Hurson, Fiona M. Blows, Manjeet K. Bolla, Jodi L. Miller, Roger Milne, Hugo Horlings, Alexandra J. van den Broek, Clara Bodelon, James Hodge, Alpa Patel, Lauren R. Teras, Federico Canzian, Rudolf Kaaks, Hermann Brenner, Ben Schoettker, Sabine Behrens, Jenny Chang-Claude, Tabea Maurer, Nadia Obi, Fergus Couch, H. Raza Ali, Carlos Caldas, Irene Andrulis, Gord Glendon, Anna Marie Mulligan, Wilma Mesker, Agnes Jager, Annette Heemskerk-Gerritsen, Peter Devilee, Scott M. Lawrence, Jolanta Lissowska, Karun Mutreja, Thomas Ahearn, Stephen Chanock, Maire A. Duggan, Diana Eccles, J. Louise Jones, Will Tapper, Antoinette Hollestelle, Maartje Hooning, John Martens, Carolien H.M. van Deurzen, Angela Cox, Simon S. Cross, Mikael Hartman, Jingmei Li, Thomas C. Putti, Ute Hamann, Ania Jakubowska, Nicki Camp, Melissa H. Cessna, Amy Berrington de Gonzalez, Katarzyna Bialkowska, Jacek Gronwald, Jan Lubi_ski, Siddhartha Yadav, Pietro Lio, Doug F. Easton, Mustapha Abubakar, Montse Garcia-Closas, Paul D.P. Pharoah, Marjanka K. Schmidt
Background The immune response in breast tumors has an important role in prognosis, but the role of spatial localization of immune cells and of interaction between subtypes is not well characterized. We evaluated the association between spatially resolved tissue infiltrating immune cells (TIICs) and breast cancer specific survival (BCSS) in a large multicenter study. Patients and methods Tissue microarrays with tumor cores from 17,265 breast cancer patients of European descent were stained for CD8, FOXP3, CD20, and CD163. We developed a machine learning based tissue segmentation and immune cell detection algorithm using Halo to score each image for the percentage of marker positive cells by compartment (overall, stroma, or tumor). We assessed the association between log transformed TIIC scores and BCSS using Cox regression. Results Total CD8+ and CD20+ TIICs (stromal and intra-tumoral) were associated with better BCSS in women with ER-negative (HR per standard deviation = 0.91 [95% CI 0.85 - 0.98] and 0.89 [0.84 - 0.94] respectively) and ER-positive disease (HR = 0.92 [95% CI 0.87 - 0.98] and 0.93 [0.86 - 0.99] respectively) in multi-marker models. In contrast, CD163+ macrophages were associated with better BCSS in ER-negative disease (0.94 [0.87 - 1.00]) and a poorer BCSS in ER-positive disease 1.04 [0.99 - 1.10]. There was no association between FOXP3 and BCSS. The observed associations tended to be stronger for intra-tumoral than stromal compartments for all markers. However, the TIIC markers account for only 7.6 percent of the variation in BCSS explained by the multi-marker fully-adjusted model for ER-negative cases and 3.0 percent for ER-positive cases. Conclusions The presence of intra-tumoral and stromal TIICs is associated with better BCSS in both ER-negative and ER-positive breast cancer. This may have implications for the use of immunotherapy. However, the addition of TIICs to existing prognostic models would only result in a small improvement in model performance.
{"title":"Spatially-resolved tumour infiltrating immune cells and prognosis in breast cancer","authors":"Aaron J. Bernstein, Renske Keeman, Amber Hurson, Fiona M. Blows, Manjeet K. Bolla, Jodi L. Miller, Roger Milne, Hugo Horlings, Alexandra J. van den Broek, Clara Bodelon, James Hodge, Alpa Patel, Lauren R. Teras, Federico Canzian, Rudolf Kaaks, Hermann Brenner, Ben Schoettker, Sabine Behrens, Jenny Chang-Claude, Tabea Maurer, Nadia Obi, Fergus Couch, H. Raza Ali, Carlos Caldas, Irene Andrulis, Gord Glendon, Anna Marie Mulligan, Wilma Mesker, Agnes Jager, Annette Heemskerk-Gerritsen, Peter Devilee, Scott M. Lawrence, Jolanta Lissowska, Karun Mutreja, Thomas Ahearn, Stephen Chanock, Maire A. Duggan, Diana Eccles, J. Louise Jones, Will Tapper, Antoinette Hollestelle, Maartje Hooning, John Martens, Carolien H.M. van Deurzen, Angela Cox, Simon S. Cross, Mikael Hartman, Jingmei Li, Thomas C. Putti, Ute Hamann, Ania Jakubowska, Nicki Camp, Melissa H. Cessna, Amy Berrington de Gonzalez, Katarzyna Bialkowska, Jacek Gronwald, Jan Lubi_ski, Siddhartha Yadav, Pietro Lio, Doug F. Easton, Mustapha Abubakar, Montse Garcia-Closas, Paul D.P. Pharoah, Marjanka K. Schmidt","doi":"10.1101/2024.07.22.24310819","DOIUrl":"https://doi.org/10.1101/2024.07.22.24310819","url":null,"abstract":"Background\u0000The immune response in breast tumors has an important role in prognosis, but the role of spatial localization of immune cells and of interaction between subtypes is not well characterized. We evaluated the association between spatially resolved tissue infiltrating immune cells (TIICs) and breast cancer specific survival (BCSS) in a large multicenter study.\u0000Patients and methods\u0000Tissue microarrays with tumor cores from 17,265 breast cancer patients of European descent were stained for CD8, FOXP3, CD20, and CD163. We developed a machine learning based tissue segmentation and immune cell detection algorithm using Halo to score each image for the percentage of marker positive cells by compartment (overall, stroma, or tumor). We assessed the association between log transformed TIIC scores and BCSS using Cox regression. Results\u0000Total CD8+ and CD20+ TIICs (stromal and intra-tumoral) were associated with better BCSS in women with ER-negative (HR per standard deviation = 0.91 [95% CI 0.85 - 0.98] and 0.89 [0.84 - 0.94] respectively) and ER-positive disease (HR = 0.92 [95% CI 0.87 - 0.98] and 0.93 [0.86 - 0.99] respectively) in multi-marker models. In contrast, CD163+ macrophages were associated with better BCSS in ER-negative disease (0.94 [0.87 - 1.00]) and a poorer BCSS in ER-positive disease 1.04 [0.99 - 1.10]. There was no association between FOXP3 and BCSS. The observed associations tended to be stronger for intra-tumoral than stromal compartments for all markers. However, the TIIC markers account for only 7.6 percent of the variation in BCSS explained by the multi-marker fully-adjusted model for ER-negative cases and 3.0 percent for ER-positive cases.\u0000Conclusions\u0000The presence of intra-tumoral and stromal TIICs is associated with better BCSS in both ER-negative and ER-positive breast cancer. This may have implications for the use of immunotherapy. However, the addition of TIICs to existing prognostic models would only result in a small improvement in model performance.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141775876","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}
Advancements in cancer treatment and survivorship rely on participation in research and access to health records. This study explored preferences for data access and sharing in 14 workshops with 42 community members, most of whom were a cancer survivor or carer. Various scenarios for data access and sharing were presented and discussed, with participants' preferences summarized using descriptive statistics. Reasons underlying these preferences were identified through a thematic analysis of workshop transcripts. Most participants indicated a willingness for researchers to use their self-reported data and current health records for a specific research project (86%). Many were also willing for their self-reported data and current (62%) or all future (44%) heath records to be shared with other researchers for use in other studies if made aware of this. Willingness to consent to data access and sharing data in cancer research was influenced by: (i) the potential for data sharing to advance medical discoveries and benefit people impacted by cancer in the future, (ii) transparency around researchers' credibility and their intentions for data sharing, (iii) level of ownership and control over data sharing, and (iv) protocols for privacy and confidentiality in data sharing. Based on these themes, we present practical strategies for optimizing data access and sharing in cancer research.
{"title":"Data sharing in cancer research: A qualitative study exploring community members' preferences","authors":"Elizabeth Johnston, Xanthia Bourdaniotis, Susannah Ayre, Leah Zajdlewicz, Vanessa Beesley, Belinda Goodwin","doi":"10.1101/2024.07.21.24310665","DOIUrl":"https://doi.org/10.1101/2024.07.21.24310665","url":null,"abstract":"Advancements in cancer treatment and survivorship rely on participation in research and access to health records. This study explored preferences for data access and sharing in 14 workshops with 42 community members, most of whom were a cancer survivor or carer. Various scenarios for data access and sharing were presented and discussed, with participants' preferences summarized using descriptive statistics. Reasons underlying these preferences were identified through a thematic analysis of workshop transcripts. Most participants indicated a willingness for researchers to use their self-reported data and current health records for a specific research project (86%). Many were also willing for their self-reported data and current (62%) or all future (44%) heath records to be shared with other researchers for use in other studies if made aware of this. Willingness to consent to data access and sharing data in cancer research was influenced by: (i) the potential for data sharing to advance medical discoveries and benefit people impacted by cancer in the future, (ii) transparency around researchers' credibility and their intentions for data sharing, (iii) level of ownership and control over data sharing, and (iv) protocols for privacy and confidentiality in data sharing. Based on these themes, we present practical strategies for optimizing data access and sharing in cancer research.","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141776010","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 : 2024-07-22DOI: 10.1101/2024.07.19.24310599
Xue Bai, Ling Zhang
Abstract Background: Type 1 diabetes (T1D) has been associated with a higher risk of Ovarian cancer (OC), albeit the mechanisms underlying this association remain elusive. A better understanding of the relationship between T1D and OC may contribute to improved primary prevention of OC. We aimed to investigate the putative causal role of T1D on OC, and to identify the potentially mediatory effects of the usage of insulin product underlying this relationship. Methods: We performed a two-sample Mendelian randomization (MR) analysis using genetic variants associated with T1D and OC from genome-wide association studies. Then, a multivariable MR analysis was conducted to investigate whether T1DM has an independent effect on OC after adjusting for potential confounders. Finally, the mediating role of insulin product was subsequently explored using mediation analysis via two-step MR. Results: the MR estimated based on IVW method indicated a causal association between genetically determined T1D and Ovarian cancer (OC) (OR: 1.0006, 95% CI 1.0001em dash1.0011; P=0.0164). After adjusting for body mass index , Smoking , physical activity , age at menopause and age at menarche, respectively ,we found that a causal relationship between T1DM and OC was still statistically significant (OR>1, P<0.05) .The two-step MR analysis revealed that insulin product acted as a mediating moderator between the T1D and OC (mediated proportion, 1.07%). Conclusions: Our findings suggest that T1D may confer a risk effect to OC, mediated in part by therapeutic insulin product. Therefore, precise dosage of insulin product or an alternative to insulin in T1D patients have a profound significance in terms of the prevention of OC. Keywords: Type 1 diabetes (T1D), Insulin product ,Ovarian cancer (OC), Mendelian randomization (MR)
{"title":"A Mendelian randomization study of insulin therapy for type 1 diabetes increasing the potential risk of ovarian cancer","authors":"Xue Bai, Ling Zhang","doi":"10.1101/2024.07.19.24310599","DOIUrl":"https://doi.org/10.1101/2024.07.19.24310599","url":null,"abstract":"Abstract\u0000Background: Type 1 diabetes (T1D) has been associated with a higher risk of Ovarian cancer (OC), albeit the mechanisms underlying this association remain elusive. A better understanding of the relationship between T1D and OC may contribute to improved primary prevention of OC. We aimed to investigate the putative causal role of T1D on OC, and to identify the potentially mediatory effects of the usage of insulin product underlying this relationship.\u0000Methods: We performed a two-sample Mendelian randomization (MR) analysis using genetic variants associated with T1D and OC from genome-wide association studies. Then, a multivariable MR analysis was conducted to investigate whether T1DM has an independent effect on OC after adjusting for potential confounders. Finally, the mediating role of insulin product was subsequently explored using mediation analysis via two-step MR.\u0000Results: the MR estimated based on IVW method indicated a causal association between genetically determined T1D and Ovarian cancer (OC) (OR: 1.0006, 95% CI 1.0001em dash1.0011; P=0.0164). After adjusting for body mass index , Smoking , physical activity , age at menopause and age at menarche, respectively ,we found that a causal relationship between T1DM and OC was still statistically significant (OR>1, P<0.05) .The two-step MR analysis revealed that insulin product acted as a mediating moderator between the T1D and OC (mediated proportion, 1.07%).\u0000Conclusions: Our findings suggest that T1D may confer a risk effect to OC, mediated in part by therapeutic insulin product. Therefore, precise dosage of insulin product or an alternative to insulin in T1D patients have a profound significance in terms of the prevention of OC.\u0000Keywords: Type 1 diabetes (T1D), Insulin product ,Ovarian cancer (OC), Mendelian randomization (MR)","PeriodicalId":501437,"journal":{"name":"medRxiv - Oncology","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141775877","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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