ESR1 and p53 interactome alteration defines mechanisms of tamoxifen response in luminal breast cancer.

The canonical mechanism behind tamoxifen's therapeutic effect on estrogen receptor α/ESR1+ breast cancers is inhibition of ESR1-dependent estrogen signaling. Although ESR1+ tumors expressing wild-type p53 were reported to be more responsive to tamoxifen (Tam) therapy, p53 has not been factored into choice of this therapy and the mechanism underlying the role of p53 in Tam response remains unclear. In a window-of-opportunity trial on patients with newly diagnosed stage I-III ESR1+/HER2/wild-type p53 breast cancer who were randomized to arms with or without Tam prior to surgery, we reveal that the ESR1-p53 interaction in tumors was inhibited by Tam. This resulted in functional reactivation of p53 leading to transcriptional reprogramming that favors tumor-suppressive signaling, as well as downregulation of oncogenic pathways. These findings illustrating the convergence of ESR1 and p53 signaling during Tam therapy enrich mechanistic understanding of the impact of p53 on the response to Tam therapy.

A robust Spearman correlation coefficient permutation test.

In this work, we show that Spearman's correlation coefficient test about H0:ρs=0 found in most statistical software is theoretically incorrect and performs poorly when bivariate normality assumptions are not met or the sample size is small. There is common misconception that the tests about ρs=0 are robust to deviations from bivariate normality. However, we found under certain scenarios violation of the bivariate normality assumption has severe effects on type I error control for the common tests. To address this issue, we developed a robust permutation test for testing the hypothesis H0:ρs=0 based on an appropriately studentized statistic. We will show that the test is asymptotically valid in general settings. This was demonstrated by a comprehensive set of simulation studies, where the proposed test exhibits robust type I error control, even when the sample size is small. We also demonstrated the application of this test in two real world examples.

The generalized sigmoidal quantile function.

In this note we introduce a new smooth nonparametric quantile function estimator based on a newly defined generalized expectile function and termed the sigmoidal quantile function estimator. We also introduce a hybrid quantile function estimator, which combines the optimal properties of the classic kernel quantile function estimator with our new generalized sigmoidal quantile function estimator. The generalized sigmoidal quantile function can estimate quantiles beyond the range of the data, which is important for certain applications given smaller sample sizes. This property of extrapolation is illustrated in order to improve standard bootstrap smoothing resampling methods.

Frameshift mutations in peripheral blood as a biomarker for surveillance of Lynch syndrome.

BACKGROUND: Lynch syndrome is a hereditary cancer predisposition syndrome caused by germline mutations in DNA mismatch repair genes, which lead to high microsatellite instability and frameshift mutations at coding mononucleotide repeats in the genome. Recurrent frameshift mutations in these regions are thought to play a central role in the increased risk of various cancers, but no biomarkers are currently available for the surveillance of high microsatellite instability-associated cancers. METHODS: A frameshift mutation-based biomarker panel was developed and validated by targeted next-generation sequencing of supernatant DNA from cultured high microsatellite instability colorectal cancer cells. This panel supported selection of 122 frameshift mutation targets as potential biomarkers. This biomarker panel was then tested using matched tumor, adjacent normal tissue, and buffy coat samples (53 samples) and blood-derived cell-free DNA (cfDNA) (38 samples) obtained from 45 high microsatellite instability and mismatch repair-deficient patients. We also sequenced cfDNA from 84 healthy participants to assess background noise. RESULTS: Recurrent frameshift mutations at coding mononucleotide repeats were detectable not only in tumors but also in cfDNA from high microsatellite instability and mismatch repair-deficient patients, including a Lynch syndrome carrier, with a varying range of target detection (up to 85.2%), whereas they were virtually undetectable in healthy participants. Receiver operating characteristic curve analysis showed high sensitivity and specificity (area under the curve = 0.94) of the investigated panel. CONCLUSIONS: We demonstrated that frameshift mutations can be detected in cfDNA from high microsatellite instability and mismatch repair-deficient patients and asymptomatic carriers. The 122-target frameshift mutation panel described here has promise as a tool for improved surveillance of high microsatellite instability and mismatch repair-deficient patients, with the potential to reduce the frequency of invasive screening methods for this high-cancer-risk cohort.

A Characterization of Most(More) Powerful Test Statistics with Simple Nonparametric Applications.

Data-driven most powerful tests are statistical hypothesis decision-making tools that deliver the greatest power against a fixed null hypothesis among all corresponding data-based tests of a given size. When the underlying data distributions are known, the likelihood ratio principle can be applied to conduct most powerful tests. Reversing this notion, we consider the following questions. (a) Assuming a test statistic, say T, is given, how can we transform T to improve the power of the test? (b) Can T be used to generate the most powerful test? (c) How does one compare test statistics with respect to an attribute of the desired most powerful decision-making procedure? To examine these questions, we propose one-to-one mapping of the term "most powerful" to the distribution properties of a given test statistic via matching characterization. This form of characterization has practical applicability and aligns well with the general principle of sufficiency. Findings indicate that to improve a given test, we can employ relevant ancillary statistics that do not have changes in their distributions with respect to tested hypotheses. As an example, the present method is illustrated by modifying the usual t-test under nonparametric settings. Numerical studies based on generated data and a real-data set confirm that the proposed approach can be useful in practice.

Inferential procedures based on the weighted Pearson correlation coefficient test statistic.

In this note, we evaluated the type I error control of the commonly used t-test found in most statistical software packages for testing the hypothesis on H0:ρ=0 vs. H1:ρ>0 based on the sample weighted Pearson correlation coefficient. We found the type I error rate is severely inflated in general cases, even under bivariate normality. To address this issue, we derived the large sample variance of the weighted Pearson correlation. Based on this result, we proposed an asymptotic test and a set of studentized permutation tests. A comprehensive set of simulation studies with a range of sample sizes and a variety of underlying distributions were conducted. The studentized permutation test based on Fisher's Z statistic was shown to robustly control the type I error even in the small sample and non-normality settings. The method was demonstrated with an example data of country-level preterm birth rates.

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance.

Distinguishing hypervirulent (hvKp) from classical Klebsiella pneumoniae (cKp) strains is important for clinical care, surveillance, and research. Some combinations of iucA, iroB, peg-344, rmpA, and rmpA2 are most commonly used, but it is unclear what combination of genotypic or phenotypic markers (e.g., siderophore concentration, mucoviscosity) most accurately predicts the hypervirulent phenotype. Furthermore, acquisition of antimicrobial resistance may affect virulence and confound identification. Therefore, 49 K. pneumoniae strains that possessed some combinations of iucA, iroB, peg-344, rmpA, and rmpA2 and had acquired resistance were assembled and categorized as hypervirulent hvKp (hvKp) (N = 16) or cKp (N = 33) via a murine infection model. Biomarker number, siderophore production, mucoviscosity, virulence plasmid's Mash/Jaccard distances to the canonical pLVPK, and Kleborate virulence score were measured and evaluated to accurately differentiate these pathotypes. Both stepwise logistic regression and a CART model were used to determine which variable was most predictive of the strain cohorts. The biomarker count alone was the strongest predictor for both analyses. For logistic regression, the area under the curve for biomarker count was 0.962 (P = 0.004). The CART model generated the classification rule that a biomarker count = 5 would classify the strain as hvKP, resulting in a sensitivity for predicting hvKP of 94% (15/16), a specificity of 94% (31/33), and an overall accuracy of 94% (46/49). Although a count of ≥4 was 100% (16/16) sensitive for predicting hvKP, the specificity and accuracy decreased to 76% (25/33) and 84% (41/49), respectively. These findings can be used to inform the identification of hvKp.IMPORTANCEHypervirulent Klebsiella pneumoniae (hvKp) is a concerning pathogen that can cause life-threatening infections in otherwise healthy individuals. Importantly, although strains of hvKp have been acquiring antimicrobial resistance, the effect on virulence is unclear. Therefore, it is of critical importance to determine whether a given antimicrobial resistant K. pneumoniae isolate is hypervirulent. This report determined which combination of genotypic and phenotypic markers could most accurately identify hvKp strains with acquired resistance. Both logistic regression and a machine-learning prediction model demonstrated that biomarker count alone was the strongest predictor. The presence of all five of the biomarkers iucA, iroB, peg-344, rmpA, and rmpA2 was most accurate (94%); the presence of ≥4 of these biomarkers was most sensitive (100%). Accurately identifying hvKp is vital for surveillance and research, and the availability of biomarker data could alert the clinician that hvKp is a consideration, which, in turn, would assist in optimizing patient care.

ReUseData: an R/Bioconductor tool for reusable and reproducible genomic data management.

BACKGROUND: The increasing volume and complexity of genomic data pose significant challenges for effective data management and reuse. Public genomic data often undergo similar preprocessing across projects, leading to redundant or inconsistent datasets and inefficient use of computing resources. This is especially pertinent for bioinformaticians engaged in multiple projects. Tools have been created to address challenges in managing and accessing curated genomic datasets, however, the practical utility of such tools becomes especially beneficial for users who seek to work with specific types of data or are technically inclined toward a particular programming language. Currently, there exists a gap in the availability of an R-specific solution for efficient data management and versatile data reuse. RESULTS: Here we present ReUseData, an R software tool that overcomes some of the limitations of existing solutions and provides a versatile and reproducible approach to effective data management within R. ReUseData facilitates the transformation of ad hoc scripts for data preprocessing into Common Workflow Language (CWL)-based data recipes, allowing for the reproducible generation of curated data files in their generic formats. The data recipes are standardized and self-contained, enabling them to be easily portable and reproducible across various computing platforms. ReUseData also streamlines the reuse of curated data files and their integration into downstream analysis tools and workflows with different frameworks. CONCLUSIONS: ReUseData provides a reliable and reproducible approach for genomic data management within the R environment to enhance the accessibility and reusability of genomic data. The package is available at Bioconductor ( https://bioconductor.org/packages/ReUseData/ ) with additional information on the project website ( https://rcwl.org/dataRecipes/ ).

Prevalence of depression among people living with HIV in rural hospitals in South-Western Nigeria-Association with clinico-demographic factors.

Major depression is the most common neuropsychiatric disorder among people living with HIV (PLWH) and is predictive of high morbidity and mortality among them. This study estimated the prevalence and explored factors associated with depression among PLWH in two rural secondary health facilities providing anti-retroviral therapy (ART) services in Southwestern Nigeria between September and December 2020. The Patient Health Questionnaire-9 (PHQ-9) was used to screen and identify PLWH aged 18 years or older with depression. Descriptive statistics, bivariate and multivariate analyses were performed with SPSS version 23. A total of 172 respondents were screened. The prevalence of depression was 16.3% (95% CI 11.1%, 22.7%). Mild, moderate, and moderately severe depression was identified in 17 (9.9%), 8(4.7%) and 3(1.7%) of the participants, respectively. One (0.6%) respondent had suicidal ideation. Of PLWH with any depression, 20/28(71.4%) were within the 40-59 years of age range. None of the participants was on antidepressants. The factor most associated with depression was hypertension, with adjusted odd ratios of 9.8(95% CI 3.5-27.3, p < 0.0001). The study highlights the importance of screening for the severity of depression among PLWH in rural hospitals providing ART services in Africa. PLWH with comorbid hypertension were more likely to suffer from some form of depression.

Leveraging homologous hypotheses for increased efficiency in tumor growth curve testing.

In this note, we present an innovative approach called "homologous hypothesis tests" that focuses on cross-sectional comparisons of average tumor volumes at different time-points. By leveraging the correlation structure between time-points, our method enables highly efficient per time-point comparisons, providing inferences that are highly efficient as compared to those obtained from a standard two-sample t test. The key advantage of this approach lies in its user-friendliness and accessibility, as it can be easily employed by the broader scientific community through standard statistical software packages.

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance.

Distinguishing hypervirulent (hvKp) from classical Klebsiella pneumoniae (cKp) strains is important for clinical care, surveillance, and research. Some combination of iucA, iroB, peg-344, rmpA, and rmpA2 are most commonly used, but it is unclear what combination of genotypic or phenotypic markers (e.g. siderophore concentration, mucoviscosity) most accurately predicts the hypervirulent phenotype. Further, acquisition of antimicrobial resistance may affect virulence and confound identification. Therefore, 49 K. pneumoniae strains that possessed some combination of iucA, iroB, peg-344, rmpA, and rmpA2 and had acquired resistance were assembled and categorized as hypervirulent hvKp (hvKp) (N=16) or cKp (N=33) via a murine infection model. Biomarker number, siderophore production, mucoviscosity, virulence plasmid's Mash/Jaccard distances to the canonical pLVPK, and Kleborate virulence score were measured and evaluated to accurately differentiate these pathotypes. Both stepwise logistic regression and a CART model were used to determine which variable was most predictive of the strain cohorts. The biomarker count alone was the strongest predictor for both analyses. For logistic regression the area under the curve for biomarker count was 0.962 (P = 0.004). The CART model generated the classification rule that a biomarker count = 5 would classify the strain as hvKP, resulting in a sensitivity for predicting hvKP of 94% (15/16), a specificity of 94% (31/33), and an overall accuracy of 94% (46/49). Although a count of ≥ 4 was 100% (16/16) sensitive for predicting hvKP, the specificity and accuracy decreased to 76% (25/33) and 84% (41/49) respectively. These findings can be used to inform the identification of hvKp. Importance: Hypervirulent Klebsiella pneumoniae (hvKp) is a concerning pathogen that can cause life-threatening infections in otherwise healthy individuals. Importantly, although strains of hvKp have been acquiring antimicrobial resistance, the effect on virulence is unclear. Therefore, it is of critical importance to determine whether a given antimicrobial resistant K. pneumoniae isolate is hypervirulent. This report determined which combination of genotypic and phenotypic markers could most accurately identify hvKp strains with acquired resistance. Both logistic regression and a machine-learning prediction model demonstrated that biomarker count alone was the strongest predictor. The presence of all 5 of the biomarkers iucA, iroB, peg-344, rmpA, and rmpA2 was most accurate (94%); the presence of ≥ 4 of these biomarkers was most sensitive (100%). Accurately identifying hvKp is vital for surveillance and research, and the availability of biomarker data could alert the clinician that hvKp is a consideration, which in turn would assist in optimizing patient care.

Do alternative tobacco products induce less adverse respiratory risk than cigarettes?

RATIONALE: Due to the relatively short existence of alternative tobacco products, gaps exist in our current understanding of their long-term respiratory health effects. We therefore undertook the first-ever side-by-side comparison of the impact of chronic inhalation of aerosols emitted from electronic cigarettes (EC) and heated tobacco products (HTP), and combustible cigarettes (CC) smoke. OBJECTIVES: To evaluate the potential differential effects of alternative tobacco products on lung inflammatory responses and efficacy of vaccination in comparison to CC. METHODS: Mice were exposed to emissions from EC, HTP, CC, or air for 8 weeks. BAL and lung tissue were analyzed for markers of inflammation, lung damage, and oxidative stress. Another group was exposed for 12 weeks and vaccinated and challenged with a bacterial respiratory infection. Antibody titers in BAL and sera and pulmonary bacterial clearance were assessed. MAIN RESULTS: EC- and HTP-aerosols significantly augmented lung immune cell infiltrates equivalent to that achieved following CC-exposure. HTP and CC significantly increased neutrophil numbers compared to EC. All products augmented numbers of B cells, T cells, and pro-inflammatory IL17A+ T cells in the lungs. Decreased lung antioxidant activity and lung epithelial and endothelial damage was induced by all products. EC and HTP differentially augmented inflammatory cytokines/chemokines in the BAL. Generation of immunity following vaccination was impaired by EC and HTP but to a lesser extent than CC, with a CC > HTP > EC hierarchy of suppression of pulmonary bacterial clearance. CONCLUSIONS: HTP and EC-aerosols induced a proinflammatory pulmonary microenvironment, lung damage, and suppressed efficacy of vaccination.

Organoids and metastatic orthotopic mouse model for mismatch repair-deficient colorectal cancer.

Background: Genome integrity is essential for the survival of an organism. DNA mismatch repair (MMR) genes (e.g., MLH1, MSH2, MSH6, and PMS2) play a critical role in the DNA damage response pathway for genome integrity maintenance. Germline mutations of MMR genes can lead to Lynch syndrome or constitutional mismatch repair deficiency syndrome, resulting in an increased lifetime risk of developing cancer characterized by high microsatellite instability (MSI-H) and high mutation burden. Although immunotherapy has been approved for MMR-deficient (MMRd) cancer patients, the overall response rate needs to be improved and other management options are needed. Methods: To better understand the biology of MMRd cancers, elucidate the resistance mechanisms to immune modulation, and develop vaccines and therapeutic testing platforms for this high-risk population, we generated organoids and an orthotopic mouse model from intestine tumors developed in a Msh2-deficient mouse model, and followed with a detailed characterization. Results: The organoids were shown to be of epithelial origin with stem cell features, to have a high frameshift mutation frequency with MSI-H and chromosome instability, and intra- and inter-tumor heterogeneity. An orthotopic model using intra-cecal implantation of tumor fragments derived from organoids showed progressive tumor growth, resulting in the development of adenocarcinomas mixed with mucinous features and distant metastasis in liver and lymph node. Conclusions: The established organoids with characteristics of MSI-H cancers can be used to study MMRd cancer biology. The orthotopic model, with its distant metastasis and expressing frameshift peptides, is suitable for evaluating the efficacy of neoantigen-based vaccines or anticancer drugs in combination with other therapies.

Leveraging Homologous Hypotheses for Increased Efficiency in Tumor Growth Curve Testing.

In this note, we present an innovative approach called "homologous hypothesis tests" that focuses on cross-sectional comparisons of average tumor volumes at different time-points. By leveraging the correlation structure between time-points, our method enables highly efficient per time-point comparisons, providing inferences that are highly efficient as compared to those obtained from a standard two-sample t-test. The key advantage of this approach lies in its user-friendliness and accessibility, as it can be easily employed by the broader scientific community through standard statistical software packages.

The Cancer Moonshot Immuno-Oncology Translational Network at 5: accelerating cancer immunotherapies.

The Immuno-Oncology Translational Network (IOTN) was established in 2018 as part of the Cancer Moonshot. In 2022, President Joe Biden set new goals to reduce the cancer death rate by half within 25 years and improve the lives of people with cancer and cancer survivors. The IOTN is focused on accelerating translation of cancer immunology research, from bench to bedside, and improving immunotherapy outcomes across a wide array of cancers in the adult population. The unique structure and team science approach of the IOTN is designed to accelerate discovery and evaluation of novel immune-based therapeutic and prevention strategies. In this article, we describe IOTN progress to date, including new initiatives and the development of a robust set of resources to advance cancer immunology research. We summarize new insights by IOTN researchers, some of which are ripe for translation for several types of cancers. Looking to the future, we identify barriers to the translation of immuno-oncology concepts into clinical trials and key areas for action and improvements that are suitable for high-yield investments. Based on these experiences, we recommend novel National Institutes of Health funding mechanisms and development of new resources to address these barriers.

Exact inference around ordinal measures of association is often not exact.

In this paper, we build upon the work of DiCiccio and Romano (2017) by extending their permutation test approach, based on the Pearson correlation coefficient in the continuous case, to ordinal measures of association. We investigate commonly used ordinal measures such as the Spearman correlation, Kendall's tau-b, and gamma, which are widely implemented in commercial and open-source software packages for exact testing routines based on generalized hypergeometric probabilities. Similar to DiCiccio and Romano's method, we apply studentization to correct the test statistic, which yields asymptotically valid inference for testing no ordinal association. We present a comprehensive theoretical framework for our approach, followed by a simulation study. Furthermore, we use toy examples to highlight the differences between the exact tests and the asymptotically valid tests. Our findings align with those of DiCiccio and Romano, indicating that exact permutation tests based on ordinal measures of association are often not exact, whereas the asymptotically correct tests perform well for moderate to large sample sizes.

The Sign Test, Paired Data, and Asymmetric Dependence: A Cautionary Tale.

In the paired data setting, the sign test is often described in statistical textbooks as a test for comparing differences between the medians of two marginal distributions. There is an implicit assumption that the median of the differences is equivalent to the difference of the medians when employing the sign test in this fashion. We demonstrate however that given asymmetry in the bivariate distribution of the paired data, there are often scenarios where the median of the differences is not equal to the difference of the medians. Further, we show that these scenarios will lead to a false interpretation of the sign test for its intended use in the paired data setting. We illustrate the false-interpretation concept via theory, a simulation study, and through a real-world example based on breast cancer RNA sequencing data obtained from the Cancer Genome Atlas (TCGA).

Application of the skew exponential power distribution to ROC curves.

The bi-Normal ROC model and corresponding metrics are commonly used in medical studies to evaluate the discriminatory ability of a biomarker. However, in practice, many clinical biomarkers tend to have skewed or other non-Normal distributions. And while the bi-Normal ROC model's AUC tends to be unbiased in this setting, providing a reasonable measure of global performance, the corresponding decision thresholds tend to be biased. To correct this bias, we propose using an ROC model based on the skew exponential power (SEP) distribution, whose additional parameters can accommodate skewed, heavy tailed, or other non-Normal distributions. Additionally, the SEP distribution can be used to evaluate whether the bi-Normal model would be appropriate. The performance of these ROC models and the non-parametric approach are evaluated via a simulation study and applied to a real data set involving infections from Klebsiella pneumoniae. The SEP based ROC-model provides some efficiency gains with respect to estimation of the AUC and provides cut-points with improved classification rates. As such, in the presence non-Normal data, we suggest using the proposed SEP ROC model.

Adherence to Cancer Prevention Lifestyle Recommendations Before, During, and 2 Years After Treatment for High-risk Breast Cancer.

Importance: The American Institute for Cancer Research and American Cancer Society regularly publish modifiable lifestyle recommendations for cancer prevention. Whether these recommendations have an impact on high-risk breast cancer survival remains unknown. Objective: To investigate whether adherence to cancer prevention recommendations before, during, and 1 and 2 years after breast cancer treatment was associated with disease recurrence or mortality. Design, Setting, and Participants: The Diet, Exercise, Lifestyles, and Cancer Prognosis Study (DELCaP) was a prospective, observational cohort study designed to assess lifestyles before diagnosis, during treatment, and at 1 and 2 years after treatment completion, implemented ancillary to the Southwest Oncology Group (SWOG) S0221 trial, a multicenter trial that compared chemotherapy regimens in breast cancer. Participants were chemotherapy-naive patients with pathologic stage I to III high-risk breast cancer, defined as node-positive disease with hormone receptor-negative tumors larger than 1 cm or any tumor larger than 2 cm. Patients with poor performance status and comorbidities were excluded from S0221. The study was conducted from January 1, 2005, to December 31, 2010; mean (SD) follow-up time for those not experiencing an event was 7.7 (2.1) years through December 31, 2018. The analyses reported herein were performed from March 2022 to January 2023. Exposure: An aggregated lifestyle index score comprising data from 4 time points and 7 lifestyles, including (1) physical activity, (2) body mass index, (3) fruit and vegetable consumption, (4) red and processed meat intake, (5) sugar-sweetened beverage consumption, (6) alcohol consumption, and (7) smoking. Higher scores indicated healthier lifestyle. Main Outcomes and Measures: Disease recurrence and all-cause mortality. Results: A total of 1340 women (mean [SD] age, 51.3 [9.9] years) completed the baseline questionnaire. Most patients were diagnosed with hormone-receptor positive breast cancer (873 [65.3%]) and completed some education beyond high school (954 [71.2%]). In time-dependent multivariable analyses, patients with highest vs lowest lifestyle index scores experienced a 37.0% reduction in disease recurrence (hazard ratio, 0.63; 95% CI, 0.48-0.82) and a 58.0% reduction in mortality (hazard ratio, 0.42; 95% CI, 0.30-0.59). Conclusions and Relevance: In this observational study of patients with high-risk breast cancer, strongest collective adherence to cancer prevention lifestyle recommendations was associated with significant reductions in disease recurrence and mortality. Education and implementation strategies to help patients adhere to cancer prevention recommendations throughout the cancer care continuum may be warranted in breast cancer.

Computational Optimization of Irradiance and Fluence for Interstitial Photodynamic Therapy Treatment of Patients with Malignant Central Airway Obstruction.

There are no effective treatments for patients with extrinsic malignant central airway obstruction (MCAO). In a recent clinical study, we demonstrated that interstitial photodynamic therapy (I-PDT) is a safe and potentially effective treatment for patients with extrinsic MCAO. In previous preclinical studies, we reported that a minimum light irradiance and fluence should be maintained within a significant volume of the target tumor to obtain an effective PDT response. In this paper, we present a computational approach to personalized treatment planning of light delivery in I-PDT that simultaneously optimizes the delivered irradiance and fluence using finite element method (FEM) solvers of either Comsol Multiphysics® or Dosie™ for light propagation. The FEM simulations were validated with light dosimetry measurements in a solid phantom with tissue-like optical properties. The agreement between the treatment plans generated by two FEMs was tested using typical imaging data from four patients with extrinsic MCAO treated with I-PDT. The concordance correlation coefficient (CCC) and its 95% confidence interval (95% CI) were used to test the agreement between the simulation results and measurements, and between the two FEMs treatment plans. Dosie with CCC = 0.994 (95% CI, 0.953-0.996) and Comsol with CCC = 0.999 (95% CI, 0.985-0.999) showed excellent agreement with light measurements in the phantom. The CCC analysis showed very good agreement between Comsol and Dosie treatment plans for irradiance (95% CI, CCC: 0.996-0.999) and fluence (95% CI, CCC: 0.916-0.987) in using patients' data. In previous preclinical work, we demonstrated that effective I-PDT is associated with a computed light dose of ≥45 J/cm2 when the irradiance is ≥8.6 mW/cm2 (i.e., the effective rate-based light dose). In this paper, we show how to use Comsol and Dosie packages to optimize rate-based light dose, and we present Dosie's newly developed domination sub-maps method to improve the planning of the delivery of the effective rate-based light dose. We conclude that image-based treatment planning using Comsol or Dosie FEM-solvers is a valid approach to guide the light dosimetry in I-PDT of patients with MCAO.