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Journal articles on the topic 'Singola molecola'

Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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1

Marcucci, Guido, Drew Watson, Shweta Kapoor, Swaminathan Rajagopalan, Rajan Parashar, Aktar Alam, Diwyanshu Sahu, et al. "Superior therapy response predictions for patients with acute myeloid leukemia (AML) using Cellworks Singula: MyCare-009-01." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e19502-e19502. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e19502.

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e19502 Background: Despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of AML patients remains relatively poor. Therapy selection is often based on information considering only cytogenetics and single molecular aberrations and ignoring other patient-specific omics data that could potentially enable more effective treatments. The Cellworks Singula™ report predicts response for physician prescribed therapies (PPT) using the novel Cellworks Omics Biology Model (CBM) to simulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. We test the hypothesis that Singula is a more accurate predictor of patient-specific therapy response than PPT. Methods: Singula’s ability to predict response was evaluated in an independent, randomly selected, retrospective cohort of 494 AML patients aged 2 to 85 years (median 54) treated with PPT. Patient omics data was available from PubMed. The accuracy of Singula was compared to that of PPT using McNemar’s test to account for the correlation between Singula and PPT. Multivariate logistic regression modeled complete response (CR) as a function of patient age, PPT, and Singula against any non-response (NR). Likelihood ratio tests were performed to further validate if Singula provides predictive information beyond PPT or patient age. Similar analyses were performed for overall survival (OS) using proportional hazards regression. Results: Singula was a better predictor for CR than PPT (McNemar’s χ2 = 72.0, p-value < 0.0001), with an overall accuracy of 88.5% (95% CI: 85.3%, 91.1%) compared to 70.2% (95% CI: 66.0%, 74.2%) for PPT. Singula exhibited a sensitivity and specificity of 97.1% and 68.0%, respectively. In multivariate regression analysis, Singula (p < 0.0001) remained an independent predictor for CR after adjusting for patient age (p = 0.0329) while PPT became not significant (p = 0.75). Singula was also an independent predictor for OS (p < 0.0001) after adjusting for patient age (p = 0.0018) and PPT (p = 0.0011). For all 100 true negatives, Singula generated alternative standard of care therapy selections with predicted clinical response. Conclusions: Singula is a superior independent predictor for CR and OS compared to PPT in AML patients. The Singula report can also validate therapy selection, correctly identify non-responders to PPT and further provide alternative therapy selections.

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Stein, Anthony Selwyn, Drew Watson, Shweta Kapoor, Kunal Ghosh Ghosh Roy, Aftab Alam, Diwyanshu Sahu, Kabya Basu, et al. "Superior therapy response predictions for patients with myelodysplastic syndrome (MDS) using Cellworks Singula: MyCare-009-02." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e19528-e19528. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e19528.

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e19528 Background: Despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of MDS patients remains relatively poor. Therapy selection is often based on information considering only cytogenetics and single molecular aberrations and ignoring other patient-specific omics data that could potentially enable more effective treatments. The Cellworks Singula™ report predicts response for physician prescribed therapies (PPT) using the novel Cellworks Omics Biology Model (CBM) to simulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. We test the hypothesis that Singula is a more accurate predictor of patient-specific therapy response than PPT. Methods: Singula’s ability to predict response was evaluated in an independent, randomly selected, retrospective cohort of 146 MDS patients aged 28 to 89 years (median 69) treated with PPT. Patient omics data was available from PubMed and TCGA. The accuracy of Singula was compared to that of PPT using McNemar’s test to account for the correlation between Singula and PPT. Multivariate logistic regression modeled complete response (CR) as a function of patient age, PPT, and Singula against any non-response (NR). Likelihood ratio tests were performed to further validate if Singula provides predictive information beyond PPT or patient age. Similar analyses were performed for overall survival (OS) using proportional hazards regression. Results: Singula was a better predictor for CR than PPT (McNemar’s χ2 = 42.0, p-value < 0.0001), with an overall accuracy of 73.3% (Exact 95% CI: 65.3%, 80.2%) compared to 37.7% (95% CI: 30.0%, 46.1%) for PPT. Singula exhibited a sensitivity and specificity of 90.9% (95% CI: 80.0%, 97.0%) and 62.6% (95% CI: 51.8%, 72.6%), respectively. In multivariate regression analysis, Singula (p < 0.0001) remained an independent predictor for CR after adjusting for patient age (p = 0.0759) and PPT (p = 0.0496). Singula provided alternative therapy selections for 17 of 53 true negative detected by Cellworks. Conclusions: Singula is a superior independent predictor for CR compared to PPT in MDS patients. The Singula report can also validate therapy selection, correctly identify non-responders to PPT and further provide alternative therapy selections.

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Wen, Patrick Y., Drew Watson, Shweta Kapoor, Aftab Alam, Aktar Alam, Deepak Anil Lala, Diwyanshu Sahu, et al. "Superior therapy response predictions for patients with glioblastoma (GBM) using Cellworks Singula: MyCare-009-03." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): 2519. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.2519.

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2519 Background: Despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of GBM patients remains relatively poor. Therapy selection is often based on information considering only a single aberration and ignoring other patient-specific omics data which could potentially enable more effective treatment selection. The Cellworks Singula™ report predicts response for physician prescribed therapies (PPT) using the novel Cellworks Omics Biology Model (CBM) to simulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. We test the hypothesis that Singula is a superior predictor of progression-free survival (PFS) and overall survival (OS) compared to PPT. Methods: Singula’s ability to predict response was evaluated in an independent, randomly selected, retrospective cohort of 109 GBM patients aged 17 to 83 years treated with PPT. Patient omics data was available from TCGA. Singula uses PubMed to generate protein interaction network activated and inactivated disease pathways. We simulated PPT for each patient and calculated the quantitative drug effect on a composite GBM disease inhibition score based on specific phenotypes while blinded to clinical response. Univariate and multivariate proportional hazards (PH) regression analyses were performed to determine if Singula provides predictive information for PFS and OS, respectively, above and beyond age and PPT. Results: In univariate analyses, Singula was a significant predictor of both PFS (HR = 4.130, p < 0.000) and OS (HR = 2.418, p < 0.0001). In multivariate PH regression analyses, Singula (HR = 4.033, p < 0.0001) remained an independent predictor of PFS after adjustment for PPT (p = 0.1453) and patient age (p = 0.4273). Singula (HR = 1.852, p = 0.0070) was also a significant independent predictor of OS after adjustment for PPT (p = 0.4127) and patient age (p = 0.0003). Results indicate that Singula is a superior predictor of both PFS and OS compared to PPT. Singula provided alternative therapy selections for 29 of 52 disease progressors detected by Cellworks. Conclusions: Singula is a superior predictor of PFS and OS in GBM patients compared to PPT. Singula can identify non-responders to PPT and provide alternative therapy selections.

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Ahluwalia, Manmeet Singh, Drew Watson, Shweta Kapoor, Rajan Parashar, Kunal Ghosh Ghosh Roy, Aftab Alam, Swaminathan Rajagopalan, et al. "Superior therapy response predictions for patients with low-grade glioma (LGG) using Cellworks Singula: MyCare-009-04." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): 2569. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.2569.

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2569 Background: Despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of LGG patients remains relatively poor. Therapy selection is often based on information considering only a single aberration and ignoring other patient-specific omics data which could potentially enable more effective treatments. The Cellworks Singula report predicts response for physician prescribed therapies (PPT) using the novel Cellworks Omics Biology Model (CBM) to simulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. We test the hypothesis that Singula is a superior predictor of progression-free survival (PFS) and overall survival (OS) compared to PPT. Methods: Singula’s ability to predict response was evaluated in an independent, randomly selected, retrospective cohort of 137 LGG patients aged 14 to 73 years treated with PPT. Patient omics data was available from TCGA. Singula uses PubMed to generate protein interaction network activated and inactivated disease pathways. We simulated the PPT for each patient and calculated the quantitative drug effect on a composite LGG disease inhibition score based on specific phenotypes while blinded to clinical response. Univariate and multivariate proportional hazards (PH) regression analyses were performed to determine if Singula provides predictive information for PFS and OS, respectively, above and beyond age and PPT. Results: In univariate analyses, Singula was a significant predictor of both PFS (HR = 3.587, p < 0.0001) and OS (HR = 3.044, p = 0.0007). In multivariate PH regression analyses, Singula (HR = 3.707, p < 0.0001) remained an independent predictor of PFS after adjustment for PPT (p = 0.3821) and patient age (p = 0.0020). Singula (HR = 2.970, p = 0.0013) was also a significant independent predictor of OS after adjustment for PPT (p = 0.0540) and patient age (p < 0.0001). Results indicate that Singula is a superior predictor of both PFS and OS compared to PPT. Singula provided alternative standard of care therapy selections for all 34 disease progressors. Conclusions: Singula is a superior predictor of PFS and OS in LGG patients compared to PPT. Singula can correctly identify non-responders to PPT and provide alternative therapy selections.

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Marcucci, Guido, Drew Watson, Prashant Ramachandran Nair, Kabya Basu, Yashaswini S. Ullal, Adity Ghosh, Yugandhara Narvekar, et al. "Assessment of Cellworks Omics Biosimulation Therapy Response Predictions for Patients with Acute Myeloid Leukemia (AML) Using Cellworks Singula™: Mycare-020-01." Blood 136, Supplement 1 (November 5, 2020): 35. http://dx.doi.org/10.1182/blood-2020-142184.

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Background. In addition to clinical considerations (e.g., age, de novo vs secondary disease, comorbidities), therapy selection for AML patients is often based on information considering only cytogenetics and/or molecular aberrations and ignoring other patient-specific omics information that could potentially enable selection of more effective treatments. In turn, despite using cytogenetic and molecular-risk stratification, the current overall outcome of AML patients remains relatively poor. The Cellworks Singula™ report predicts clinical response to physician-prescribed treatments using the novel Cellworks Omics Biology Model (CBM) that simulate in silico downstream molecular effects on cell signaling and survival of drug treatments in patient-specific diseased cells. Methods. The performance of Singula™ was evaluated in a cohort of 474 AML patients aged 2 to 85. The pre-defined Singula™ Classifier utilizes individual patients' next-generation sequencing (NGS) profiles to provide a dichotomous prediction of response or non-response to the physician prescribed treatments. The clinical outcome data for these subjects, i.e., complete response (CR) and overall survival (OS), were obtained from the TCGA and other 144 PubMed publications, each including also information on patients' cytogenetics, targeted gene mutations, and/or whole exome sequencing. Blinded to clinical outcomes, Cellworks utilized the cytogenetic and molecular data to generate a Singula™ predicted response (i.e., CR vs non-response) classification for each patient. Statistical analyses, including assessments of accuracy, sensitivity, specificity, and negative (NPV) and positive predictive (PPV) values were performed to compare the Singula™ predicted clinical response to the actual observed clinical response. Kaplan-Meier curves, associated log rank tests and median OS are provided for patients stratified by Singula™ predicted response. Multivariate Cox proportional hazards regression was used to further test the hypothesis that Singula™ is an independent predictor for OS once adjusted for patient age and actual prescribed treatment. Results. Data are summarized in Table 1. The Singula™ classifier had 92.3% (90.6%, 95.3%) accuracy in predicting correctly observed patient complete response to the prescribed treatment. with 97.3% (95.0%, 98.8%) sensitivity. Singula™ had 83.3% (76.1%, 89.1%) specificity for the non-responder patients (n=138; 29.1%). For each of the non-responders, Singula™ provided an alternative treatment therapy predicted to produce clinical response. Assuming at least 2% of the non-responders would have responded to the alternative Singula™ prescribed treatment, Singula™ improves CR rates compared to the original physician prescribed treatment (McNemar's p-value < 0.05). Figure 1 provides the Kaplan-Meier curves of Singula-predicted responders vs non-responders for a subset of 292 subjects that had OS data available. In multivariate Cox proportional hazards models, the Singula Classifier remained a significant predictor of overall survival (HR = 2.171, p-value < 0.0001) once adjusted for patient age and physician prescribed treatment. Conclusions. Cellworks Singula™ has high accuracy and sensitivity in predicting CR for AML patient. Singula also has high specificity in identifying patients who are unlikely to respond physician and may prescribed potentially effective therapies. The Singula™ predicted responders have a significantly longer OS than the predicted non responders. Thus, Cellworks Singula™ can accurately predict AML response, be used to validate or reject a physician's therapy selection decision and, eventually, provide alternative treatment recommendations. Disclosures Marcucci: Novartis: Speakers Bureau; Abbvie: Speakers Bureau; Iaso Bio: Membership on an entity's Board of Directors or advisory committees; Takeda: Other: Research Support (Investigation Initiated Clinical Trial); Pfizer: Other: Research Support (Investigation Initiated Clinical Trial); Merck: Other: Research Support (Investigation Initiated Clinical Trial). Watson:Mercy Bioanalytics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; SEER Biosciences, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; BioAI Health Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellmax Life Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellworks Group Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees. Nair:Cellworks Research India Private Limited: Current Employment. Basu:Cellworks Research India Private Limited: Current Employment. Ullal:Cellworks Research India Private Limited: Current Employment. Ghosh:Cellworks Research India Private Limited: Current Employment. Narvekar:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Sahu:Cellworks Research India Private Limited: Current Employment. Amara:Cellworks Research India Private Limited: Current Employment. Pampana:Cellworks Research India Private Limited: Current Employment. Roy:Cellworks Research India Private Limited: Current Employment. Rajagopalan:Cellworks Research India Private Limited: Current Employment. Alam:Cellworks Research India Private Limited: Current Employment. Parashar:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Christie:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Stein:Stemline: Consultancy, Speakers Bureau; Amgen: Consultancy, Speakers Bureau.

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Stein, Anthony S., Drew Watson, Prashant Ramachandran Nair, Kabya Basu, Yashaswini S. Ullal, Adity Ghosh, Yugandhara Narvekar, et al. "Superior Therapy Response Predictions for Patients with Myelodysplastic Syndrome (MDS) Using Cellworks Singula™: Mycare-020-02." Blood 136, Supplement 1 (November 5, 2020): 9–10. http://dx.doi.org/10.1182/blood-2020-142214.

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Background: Therapy selection for MDS patients is often based on information considering only cytogenetics and single molecular aberrations and ignoring other patient-specific omics data that could potentially enable more effective treatments. In turn, despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of MDS patients remains relatively poor. The Cellworks Singula™ report predicts response for physician prescribed treatments using the novel Cellworks Omics Biology Model (CBM) to simulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. Methods: The performance of Singula™ was evaluated in an independent, randomly selected, retrospective cohort of 144 MDS patients aged 28 to 89 years (median 69). The pre-defined Singula™ Classifier utilizes an individual's genomics profile to provide a dichotomous prediction of response or non-responses to a given physician prescribed treatment (PPT). Outcome data for these subjects, including measurement of complete response (CR), were obtained from 42 PubMed publications, each including patient genomics data of either karyotyping, targeted gene panels, and/or whole exome sequencing. Blinded to clinical outcomes, Cellworks utilized these data to generate a Singula™ classifier of responder vs non-responder in this MDS cohort. Statistical analyses, including assessments of accuracy, sensitivity, specificity, negative (NPV) and positive predictive (PPV) values were performed on the merged data to compare the Singula™ predicted response with the actual observed CR. Multivariate logistic regression models of complete response were performed incorporating covariates for patient age, PPT, and the Singula™ Classifier. Results: Table 1 reveals that the pre-defined Singula™ classifier had 90.3% (Exact 95% CI: 84.2%, 94.6%) accuracy in predicting observed patient response from the physician prescribed treatment. In this study, Singula™ was able to accurately identify responders with 90.0% (81.2%, 95.6%) sensitivity. Importantly, Singula™ had 90.6% (80.7%, 96.5%) specificity for the subset of 64 patients (44.4%) that had a non-response. For 32% (17/54) of the non-responders patients, Singula™ provided an alternative Standard of Care treatment therapy, as shown in Table 2. The remaining 37 patients were predicted to be non-responders to all remaining Standard of Care options, so did not have alternate treatment predictions. Assuming at least 4% of these non-responding patients would have responded to the alternative Singula™ prescribed therapy, then these data support that Singula™ improves prediction of CR compared to the original PPT (McNemar's p-value < 0.05). In multivariate logistic regression models of CR that included patient age and prescribed drug therapy, the Singula™ Classifier remained an independent, significant predictor of CR (OR > 100, p-value < 0.0001), while both patient age (p = 0.372) and drug therapy (p = 0.720) fell off the model. Conclusions: Cellworks Singula™ has high accuracy and sensitivity in predicting CR for MDS patient response to physician prescribed therapies. Singula™ also has high specificity in identifying patients who are unlikely to respond to physician prescribed therapies and provides alternative treatment recommendations for these patients. The Singula™ Classifier is an independent and superior predictor of CR compared with other clinical (age) or therapeutic (PPT) factors. Figure Disclosures Stein: Amgen: Consultancy, Speakers Bureau; Stemline: Consultancy, Speakers Bureau. Watson:BioAI Health Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mercy Bioanalytics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; SEER Biosciences, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellworks Group Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellmax Life Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees. Nair:Cellworks Research India Private Limited: Current Employment. Basu:Cellworks Research India Private Limited: Current Employment. Ullal:Cellworks Research India Private Limited: Current Employment. Ghosh:Cellworks Research India Private Limited: Current Employment. Narvekar:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Sahu:Cellworks Research India Private Limited: Current Employment. Prakash:Cellworks Research India Private Limited: Current Employment. Behura:Cellworks Research India Private Limited: Current Employment. Balakrishnan:Cellworks Research India Private Limited: Current Employment. Roy:Cellworks Research India Private Limited: Current Employment. Rajagopalan:Cellworks Research India Private Limited: Current Employment. Alam:Cellworks Research India Private Limited: Current Employment. Parashar:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Christie:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Marcucci:Abbvie: Speakers Bureau; Novartis: Speakers Bureau; Pfizer: Other: Research Support (Investigation Initiated Clinical Trial); Merck: Other: Research Support (Investigation Initiated Clinical Trial); Takeda: Other: Research Support (Investigation Initiated Clinical Trial); Iaso Bio: Membership on an entity's Board of Directors or advisory committees.

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Velcheti, Vamsidhar, Michael Castro, Drew Watson, Shweta Kapoor, Anuj Tyagi, Mohammed Sauban, Aftab Alam, et al. "Superior overall survival (OS), progression-free survival (PFS), and clinical response (CR) predictions for patients with non-small cell lung cancer (NSCLC) using Cellworks Singula: myCare-022-05." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 9117. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.9117.

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9117 Background: The Cellworks Singula Therapeutic Response Index (TRI) has been developed to assist clinicians and NSCLC patients in choosing between competing therapeutic options. In contrast to approaches that consider single aberrations, which often yield limited benefit, Cellworks utilizes an individual patient’s next generation sequencing results and a mechanistic multi-omics biology model, the Cellworks Omics Biology Model (CBM), to biosimulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. For any individual patient and alternative therapy, Cellworks integrates this biologically modeled multi-omics information into a continuous Singula TRI Score, scaled from 0 (low therapeutic benefit) to 100 (high therapeutic benefit). We demonstrate that Singula is strongly associated with overall survival, progression-free survival and relative therapeutic benefit beyond standard clinical factors, including patient age, gender, and physician prescribed treatments (PPT). Methods: In this study, Singula’s ability to predict response was evaluated in a retrospective cohort of 446 NSCLC patients with OS, PFS, and CR data from The Cancer Genome Atlas (TCGA) project, treated with PPT. As a primary analysis of the CBM and TRI Score, Cox Proportional Hazards (PH) regression and likelihood ratio (LR) tests were used to assess the hypothesis that Singula is predictive of OS, PFS, and CR above and beyond standard clinical factors. A p-value < 0.05 for the corresponding likelihood ratio statistic was required to be considered significant. Results: Multivariate analyses were performed to assess the performance of the Singula Therapy Response Index above and beyond physician’s choice of treatment. The same Singula TRI algorithm and clinical cutoffs were used for all clinical outcome measures. For OS the median survival times for the high and low benefit groups were 60.16 and 28.57 months respectively, based on the median Singula value. Also, the hazard ratio per 25 Singula units for OS was 0.5103 (95% CI: 0.3337 - 0.7804) and the odds ratio for CR was 1.6161. These and further analyses, shown in Table, suggest that Singula TRI provides predictive value of OS, PFS, and CR above and beyond standard clinical factors. Conclusions: The Singula TRI Score provides a continuous measure for alternative NSCLC therapeutic options. In this retrospective cohort, Singula was strongly predictive of OS, PFS, and CR and provided predictive value of OS beyond PPT, patient age and gender. These results will be further validated in prospective clinical studies.[Table: see text]

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Wen, Patrick Y., Michael Castro, Drew Watson, Shweta Kapoor, Ashish Agrawal, Aftab Alam, Kunal Ghosh Roy, et al. "Superior overall survival (OS) and disease-free survival (DFS) predictions for patients with glioblastoma multiforme (GBM) using Cellworks Singula: myCare-022-03." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 2017. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.2017.

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2017 Background: The Cellworks Singula Therapeutic Response Index (TRI) has been developed to assist clinicians and GBM patients in choosing between competing therapeutic options. In contrast to approaches that consider single aberrations, which often yield limited benefit, Cellworks utilizes an individual patient’s next generation sequencing results and a mechanistic multi-omics biology model, the Cellworks Omics Biology Model (CBM), to biosimulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. For any individual patient and alternative therapy, Cellworks integrates this biologically modeled multi-omics information into a continuous Singula TRI Score, scaled from 0 (low therapeutic benefit) to 100 (high therapeutic benefit). We demonstrate that Singula is strongly associated with OS and DFS beyond standard clinical factors, including patient age, patient gender, and physician prescribed treatments (PPT). Methods: In this study, Singula’s ability to predict response was evaluated in a retrospective cohort of 100 GBM patients with OS and DFS data from The Cancer Genome Atlas (TCGA) project, treated with PPT. As a primary analysis of the CBM and TRI Score, Cox Proportional Hazards (PH) regression and likelihood ratio (LR) tests were used to assess the hypothesis that Singula is predictive of OS and DFS above and beyond patient age, patient gender, and PPT. A p-value < 0.05 for the corresponding likelihood ratio statistic was required to be considered significant. Results: Multivariate analyses were performed to assess the performance of the Singula Therapy Response Index after adjusting for the contribution of standard clinical factors. The same Singula TRI algorithm and clinical cutoffs were used for all clinical outcome measures. These analyses, shown in the table, suggests that the proposed Singula TRI provides predictive value of OS and DFS above and beyond patient age, patient gender, and PPT. Conclusions: The Singula TRI Score provides a continuous measure scaled from 0 (low benefit) to 100 (high benefit) for alternative GBM therapeutic options. In this retrospective cohort, Singula was strongly predictive of OS and DFS and provided predictive value beyond PPT, patient age and gender. These results will be further validated in larger scale, prospectively designed clinical studies.[Table: see text]

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Su Yuting, 苏玉婷, and 盖宏伟 Gai Hongwei. "单分子计数免疫分析." Laser & Optoelectronics Progress 59, no. 6 (2022): 0617011. http://dx.doi.org/10.3788/lop202259.0617011.

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Yanagida, Toshio. "S2h1-2 Single molecule study for elucidating the mechanism involved in utilizing fluctuations by biosystems(S2-h1: "Single Molecule Analysis of Molecular Motor",Symposia,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S127. http://dx.doi.org/10.2142/biophys.46.s127_1.

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Hayashi, Fumio. "1P540 Single-molecular behavior of rhodopsin in native disc membrane(26. Single molecule biophysics,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S281. http://dx.doi.org/10.2142/biophys.46.s281_4.

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Cuesta, Virginia, Maida Vartanian, Pilar de la Cruz, Ganesh D. Sharma, and Fernando Langa. "Molecular Engineering of Low-Bandgap Porphyrins for Highly Efficient Organic Solarcells." ECS Meeting Abstracts MA2022-01, no. 14 (July 7, 2022): 981. http://dx.doi.org/10.1149/ma2022-0114981mtgabs.

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Organic solar cells (OSCs) based on solution-processed bulk heterojunction (BHJ) active layers have emerged as promising solutions for the conversion of solar energy into electrical energy in building and indoor applications due to their unique advantages, such as being lightweight and semitrans-parent and the possibility of being processed by low-cost roll-to-roll methods. The BHJ active layer employed for OSCs consists of a blend of an electron-donating material and an electron-accepting material creating internal donor-acceptor heterojunctions, and their optical and electrochemical properties are very important for the realization of a high-power conversion efficiency (PCE). The optical and electrochemical properties of porphyrins can be adjusted by molecular design and functionalization on the b or meso positions of the porphyrin ring as well as by introduction of different central metal ions. Although the pioneering use of porphyrins in OSCs was disappointing, as reported efficiencies were very low;the situation has changed over the last five years as Zn-porphyrins with ABAB structures linked to acceptor units, having relatively long-lived singlet excited states, have been successfully used as donors or acceptors, resulting in increased efficiencies. Here, I´ll present our recent work in design, synthesis, and application of porphyrin-based small molecules for highly efficient OSCs with VOC>1V and PCE>15%. References V. Cuesta, M. Vartanian, P. de la Cruz, R. Singhal, G. D. Sharma and F. Langa, J. Mater. Chem. A,2017, 5, 1057. S. Arrechea, A. Aljarilla, P. de la Cruz, M. K. Singh, G. D. Sharma, F. Langa. J. Mater. Chem. C, 2017, 5, 4742. M. Vartanian, R. Singhal, P. de la Cruz, S. Biswas, G. D. Sharma and F. Langa, ACS Appl. Energy Mater. 2018, 1, 1304. M. Vartanian, P. de la Cruz, F. Langa, S. Biswas, G. D. Sharma. Nanoscale, 2018, 10, 12100. M. Vartanian, R. Singhal, P. de la Cruz, G. D. Sharma, F. Langa, Chem. Commun, 2018, 54, 14144. V. Cuesta, R. Singhal, P. de la Cruz, G. D. Sharma, F. Langa, ACS Appl. Mater. Interfaces, 2019, 11, 7216 . Cuesta, R. Singhal, P. de la Cruz, G. D. Sharma, F. Langa, ChemSusChem 2021, 14, 3439. H. Dahiya, V. Cuesta, P. de la Cruz, F. Langa, G. D. Sharma ACS Appl. Energy Mater. 2021, 4, 4498.

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Ishii, Takaaki, Atsuto Katano, Yoshihiro Murayama, and Masaki Sano. "1P560 Observing mechanical unfolding and folding process of single molecular protein by AFM(26. Single molecule biophysics,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S286. http://dx.doi.org/10.2142/biophys.46.s286_4.

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Torisawa, Takayuki, Muneyoshi Ichikawa, Takuya Kobayashi, Takashi Murayama, and Yoko Toyoshima. "3P157 DIFFUSIVE MOVEMENT OF A SINGLE-MOLECULE MAMMALIAN CYTOPLASMIC DYNEIN(Molecular motor,The 48th Annual Meeting of the Biophysical Society of Japan)." Seibutsu Butsuri 50, supplement2 (2010): S172. http://dx.doi.org/10.2142/biophys.50.s172_4.

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LI, Chun-Biu, and Tamiki KOMATSUZAKI. "Handling Noisy Data from Single Molecule Experiments." Seibutsu Butsuri 54, no. 5 (2014): 257–58. http://dx.doi.org/10.2142/biophys.54.257.

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Luo Tingdan, 罗婷丹, and 李依明 Li Yiming. "深度学习在单分子定位显微镜中的应用." Chinese Journal of Lasers 49, no. 24 (2022): 2407206. http://dx.doi.org/10.3788/cjl202249.2407206.

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17

Iwasaki, Satoshi, Ken'ya Furuta, Toshihiko Sakuma, Masaki Edamatsu, and Yoko Y. Toyoshima. "2P227 Analysis of single molecule motility of mitotic kinesins(37. Molecular motor (II),Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S352. http://dx.doi.org/10.2142/biophys.46.s352_3.

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Ritchie, Ken. "S01H3 Single molecule imaging of diffusion in E. Coll membranes(Systems Biology of Intracellular Signaling as Studied by Single-Molecule Imaging)." Seibutsu Butsuri 47, supplement (2007): S1. http://dx.doi.org/10.2142/biophys.47.s1_3.

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Noji, Hroyuki. "SINGLE MOLECULE BIOPHYSICS OF F_1-ATPase motor protein." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2007.3 (2007): S1. http://dx.doi.org/10.1299/jsmeapbio.2007.3.s1.

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20

ZHAO Yuehan, 赵悦晗, and 郝翔 HAO Xiang. "多色单分子定位显微技术研究进展（特邀）." ACTA PHOTONICA SINICA 51, no. 8 (2022): 0851517. http://dx.doi.org/10.3788/gzxb20225108.0851517.

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21

Wang Siyuan, 王思媛, 刘虹遥 Liu Hongyao, 路鑫超 Lu Xinchao, and 黄成军 Huang Chengjun. "等离激元纳米孔用于单分子光学检测的研究进展." Chinese Journal of Lasers 50, no. 1 (2023): 0113012. http://dx.doi.org/10.3788/cjl220914.

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22

Cao, Jianshu. "1S5-5 Generic models for single molecule biological processes : Generic models for single molecule biological processes(1S5 Linking single molecule spectroscopy and energy landscape perspectives,The 46th Annual Meeting of the Biophysical Society of Japan)." Seibutsu Butsuri 48, supplement (2008): S5. http://dx.doi.org/10.2142/biophys.48.s5_1.

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23

Ishiwata, Shin'ichi. "S2h1-4 Hierarchical Construction of Biological Motility System(S2-h1: "Single Molecule Analysis of Molecular Motor",Symposia,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S127. http://dx.doi.org/10.2142/biophys.46.s127_3.

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Fernandez, Julio M. "S3B1 Protein mechanics studied with single molecule AFM techniques.(Single Molecure Dynamics and Reactions)." Seibutsu Butsuri 42, supplement2 (2002): S13. http://dx.doi.org/10.2142/biophys.42.s13_4.

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25

Sei, Kazuto, Akinori Baba, Chun Biu Li, and Tamiki Komatsuzaki. "1P537 Randomness and Memory in Single Molecule Time Series(26. Single molecule biophysics,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S281. http://dx.doi.org/10.2142/biophys.46.s281_1.

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26

Yang Jianyu, 杨建宇, 董浩 Dong Hao, 邢福临 Xing Fulin, 胡芬 Hu Fen, 潘雷霆 Pan Leiting, and 许京军 Xu Jingjun. "单分子定位超分辨成像技术进展及应用." Laser & Optoelectronics Progress 58, no. 12 (2021): 1200001. http://dx.doi.org/10.3788/lop202158.1200001.

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27

Kinosita, Kazuhiko. "S2h1-1 Probing motor dynamics with huge and small tags(S2-h1: "Single Molecule Analysis of Molecular Motor",Symposia,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S126. http://dx.doi.org/10.2142/biophys.46.s126_4.

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Hirokawa, Nobutaka. "S2h1-3 Mechanism of Motility of Monomeric Motor, KIF 1A(S2-h1: "Single Molecule Analysis of Molecular Motor",Symposia,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S127. http://dx.doi.org/10.2142/biophys.46.s127_2.

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29

Takeda, Mizuho, Hiromi Imamura, Katsuya Shimabukuro, Chiyo Ikeda, Masasuke Yoshida, and Ken Yokoyama. "1P533 Mechanism of Inhibition of the V-type Molecular Motor by Tributyltin Chloride(26. Single molecule biophysics,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S280. http://dx.doi.org/10.2142/biophys.46.s280_1.

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Gopich, Irina V. "1S5-4 Decoding the pattern of photon colors in single-molecule FRET : Decoding the pattern of photon colors in single-molecule FRET(1S5 Linking single molecule spectroscopy and energy landscape perspectives,The 46th Annual Meeting of the Biophysical Society of Japan)." Seibutsu Butsuri 48, supplement (2008): S4—S5. http://dx.doi.org/10.2142/biophys.48.s4_6.

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31

Fujisawa, Ryo, Daichi Okuno, and Hiroyuki Noji. "1P526 Single-molecule analysis of F_1-motor loaded with nonhydrolyzable substrate(26. Single molecule biophysics,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S278. http://dx.doi.org/10.2142/biophys.46.s278_2.

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32

Ueno, Taro, Takashi Tanii, Naonobu Shimamoto, Takeo Miyake, Hironori Sonobe, Iwao Odomari, and Takashi Funatsu. "1P542 Single molecule imaging of chaperonin functions using zero-mode waveguides(26. Single molecule biophysics,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S282. http://dx.doi.org/10.2142/biophys.46.s282_2.

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Otsuka, Shotaro, Hirohide Takahashi, and Shige H. Yoshimura. "1P543 Single-molecule structural and functional analyses of nuclear pore complex(26. Single molecule biophysics,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S282. http://dx.doi.org/10.2142/biophys.46.s282_3.

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34

Morimatsu, Miki, Hiroaki Takagi, Kosuke Ohta, Toshio Yanagida, and Yasushi Sako. "1P547 Kinetic analysis of EGFR/Grb2 interactions using single-molecule imaging(26. Single molecule biophysics,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S283. http://dx.doi.org/10.2142/biophys.46.s283_3.

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35

Nguyen, Anh Thi Van, Y. Kamio, and H. Higuchi. "1H1430 Single-Molecule Visualization of Hemolysin Assembly on Erythrocyte Membranes." Seibutsu Butsuri 42, supplement2 (2002): S44. http://dx.doi.org/10.2142/biophys.42.s44_3.

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36

Ueno, Hiroshi, Kazuhito Tabata, Toshiharu Suzuki, Toru Ide, Masasuke Yoshida, and Hiroyuki Noji. "1P528 Development of the Single Molecule Imaging System of the F_0 Motor(26. Single molecule biophysics,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S278. http://dx.doi.org/10.2142/biophys.46.s278_4.

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37

Thumkeo, Dean, Takuji Yoshihara, Toshio Yanagida, and Masahiro Ueda. "1P538 Single-molecule imaging of Ras-PI3K signaling in chemotaxing Dictyostelium cells(26. Single molecule biophysics,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S281. http://dx.doi.org/10.2142/biophys.46.s281_2.

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38

Baba, Akinori, and Tamiki Komatsuzaki. "1P539 Applicability of local ergodic state analysis of single molecule time series(26. Single molecule biophysics,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S281. http://dx.doi.org/10.2142/biophys.46.s281_3.

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39

Yokota, Hiroaki, Yong-Woon Han, Jean-Francois Allemand, Xugang Xi, Vincent Croquette, David Bensimon, and Yoshie Harada. "1P556 Novel microscopy for simultaneous single molecule measurement of DNA/protein interaction(26. Single molecule biophysics,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S285. http://dx.doi.org/10.2142/biophys.46.s285_4.

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40

Taniguchi, Masateru. "1SBP-04 Single Molecule Electrical Sequencing of DNA and microRNA(1SBP Advanced Single Molecule Sequencing System,Symposium,The 51th Annual Meeting of the Biophysical Society of Japan)." Seibutsu Butsuri 53, supplement1-2 (2013): S87. http://dx.doi.org/10.2142/biophys.53.s87_5.

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41

Nakamura, Mariko, Hiroshi Ueno, Hiromi Imamura, and Hiroyuki Noji. "1P525 Designing a mutant F_1-ATPase for easy and rapid single molecule analysis(26. Single molecule biophysics,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S278. http://dx.doi.org/10.2142/biophys.46.s278_1.

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42

Kobayashi, T., M. Murakami, T. Kawasaki, A. Yoshimura, and A. Kusumi. "S2L1 Single molecule analysis of intracellular signal transduction in living cells." Seibutsu Butsuri 42, supplement2 (2002): S11. http://dx.doi.org/10.2142/biophys.42.s11_1.

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43

Miyashita, Takuya, Takuya Kobayashi, Hatsuha Kajita, and Yoko Y. Toyoshima. "2P159 A novel role of dynactin for dynein motility revealed by single-molecule assay(11. Molecular motor,Poster,The 52nd Annual Meeting of the Biophysical Society of Japan(BSJ2014))." Seibutsu Butsuri 54, supplement1-2 (2014): S221. http://dx.doi.org/10.2142/biophys.54.s221_3.

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44

Tirtom, Naciye Esma, Yoshihiro Nishikawa, Daichi Okuno, Masahiro Nakano, Ken Yokoyama, and Hiroyuki Noji. "3A0948 Single Molecule Analysis of Inhibitory Pausing States of V_1-ATPase(Molecular Motors III:F1 ATPase and Mycoplasma,Oral Presentation,The 50th Annual Meeting of the Biophysical Society of Japan)." Seibutsu Butsuri 52, supplement (2012): S56. http://dx.doi.org/10.2142/biophys.52.s56_4.

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45

Okawa, Akane, Toshiki Yagi, Tomonobu Watanabe, Hideo Higuchi, and Ritsu Kamiya. "1P289 Single-molecule observation of the in vitro movement of flagellar outer dynein arm complex(9. Molecular motor (I),Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S219. http://dx.doi.org/10.2142/biophys.46.s219_1.

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46

Matsuzaki, Kouhei, and Michio Tomishige. "1P162 Single-molecule fluorescent observations of the biased binding/unbinding of the tethered kinesin head(11. Molecular motor,Poster,The 52nd Annual Meeting of the Biophysical Society of Japan(BSJ2014))." Seibutsu Butsuri 54, supplement1-2 (2014): S167. http://dx.doi.org/10.2142/biophys.54.s167_6.

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47

Mori, Teppei, Hisashi Tadakuma, Michiko Nakajima, and Michio Tomishige. "1P266 Single molecule FRET observations of the conformational intermediates of kinesin motor protein during processive movement(9. Molecular motor (I),Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S213. http://dx.doi.org/10.2142/biophys.46.s213_2.

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48

Chen, Yuhang, Xiaosong Zhu, Pengfei Lan, and Peixiang Lu. "Background-free detection of molecular chirality using a single-color beam [Invited]." Chinese Optics Letters 20, no. 10 (2022): 100004. http://dx.doi.org/10.3788/col202220.100004.

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49

Tani, Tomomi, Shalin Mehta, Rudolf Oldenbourg, and Amy Gladfelter. "2P298 Fluorescent Single Molecule Orinetation Imaging in Living Cells(27. Bioimaging,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S208. http://dx.doi.org/10.2142/biophys.53.s208_3.

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50

Nguyen, Anh Thi Van, Y. Kamio, and H. Higuchi. "2N1630 Single-Molecule Imaging of Cooperative Assembly of Hemolysin on Erythrocyte Membranes." Seibutsu Butsuri 42, supplement2 (2002): S143. http://dx.doi.org/10.2142/biophys.42.s143_3.

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