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@article{pmid38877668,

title = {Entorhinal vessel density correlates with phosphorylated tau and TDP-43 pathology},

author = {Josu\'{e} Llamas Rodr\'{i}guez and Andr\'{e} J W van der Kouwe and Jan Oltmer and Emma Rosenblum and Nathaniel Mercaldo and Bruce Fischl and Michael Marshall and Matthew P Frosch and Jean C Augustinack},

doi = {10.1002/alz.13896},

issn = {1552-5279},

year  = {2024},

date = {2024-07-01},

urldate = {2024-06-01},

journal = {Alzheimers Dement},

volume = {20},

number = {7},

pages = {4649-4662},

abstract = {INTRODUCTION: The entorhinal cortex (EC) and perirhinal cortex (PC) are vulnerable to Alzheimer\'s disease. A triggering factor may be the interaction of vascular dysfunction and tau pathology.nnMETHODS: We imaged post mortem human tissue at 100 μm with 7 T magnetic resonance imaging and manually labeled individual blood vessels (mean = 270 slices/case). Vessel density was quantified and compared per EC subfield, between EC and PC, and in relation to tau and TAR DNA-binding protein 43 (TDP-43) semiquantitative scores.nnRESULTS: PC was more vascularized than EC and vessel densities were higher in posterior EC subfields. Tau and TDP-43 strongly correlated with vasculature density and subregions with severe tau at the preclinical stage had significantly greater vessel density than those with low tau burden.nnDISCUSSION: These data impact cerebrovascular maps, quantification of subfield vasculature, and correlation of vasculature and pathology at early stages. The ordered association of vessel density, and tau or TDP-43 pathology, may be exploited in a predictive context.nnHIGHLIGHTS: Vessel density correlates with phosphorylated tau (p-tau) burden in entorhinal and perirhinal cortices. Perirhinal area 35 and posterior entorhinal cortex showed greatest p-tau burden but also the highest vessel density in the preclinical phase of Alzheimer\'s disease. We combined an ex vivo magnetic resonance imaging model and histopathology to demonstrate the 3D reconstruction of intracortical vessels and its spatial relationship to the pathology.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38737790,

title = {Functional hemodynamic imaging markers for the prediction of pathological outcomes in breast cancer patients treated with neoadjuvant chemotherapy},

author = {Bin Deng and Ailis Muldoon and Jayne Cormier and Nathaniel D Mercaldo and Elizabeth Niehoff and Natalie Moffett and Mansi A Saksena and Steven J Isakoff and Stefan A Carp},

doi = {10.1117/1.JBO.29.6.066001},

issn = {1560-2281},

year  = {2024},

date = {2024-06-01},

journal = {J Biomed Opt},

volume = {29},

number = {6},

pages = {066001},

abstract = {SIGNIFICANCE: Achieving pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) is a significant predictor of increased likelihood of survival in breast cancer patients. Early prediction of pCR is of high clinical value as it could allow personalized adjustment of treatment regimens in non-responding patients for improved outcomes.nnAIM: We aim to assess the association between hemoglobin-based functional imaging biomarkers derived from diffuse optical tomography (DOT) and the pathological outcome represented by pCR at different timepoints along the course of NACT.nnAPPROACH: Twenty-two breast cancer patients undergoing NACT were enrolled in a multimodal DOT and X-ray digital breast tomosynthesis (DBT) imaging study in which their breasts were imaged at different compression levels. Logistic regressions were used to study the associations between DOT-derived imaging markers evaluated after the first and second cycles of chemotherapy, respectively, with pCR status determined after the conclusion of NACT at the time of surgery. Receiver operating characteristic curve analysis was also used to explore the predictive performance of selected DOT-derived markers.nnRESULTS: Normalized tumor HbT under half compression was significantly lower in the pCR group compared to the non-pCR group after two chemotherapy cycles (). In addition, the change in normalized tumor  upon reducing compression from full to half mammographic force was identified as another potential indicator of pCR at an earlier time point, i.e., after the first chemo cycle (). Exploratory predictive assessments showed that AUCs using DOT-derived functional imaging markers as predictors reach as high as 0.75 and 0.71, respectively, after the first and second chemo cycle, compared to AUCs of 0.50 and 0.53 using changes in tumor size measured on DBT and MRI.nnCONCLUSIONS: These findings suggest that breast DOT could be used to assist response assessment in women undergoing NACT, a critical but unmet clinical need, and potentially enable personalized adjustments of treatment regimens.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38805733,

title = {Effects of Low-Level Light Therapy on Resting-State Connectivity Following Moderate Traumatic Brain Injury: Secondary Analyses of a Double-blinded Placebo-controlled Study},

author = {Suk-Tak Chan and Nathaniel Mercaldo and Maria G Figueiro Longo and Jonathan Welt and Arman Avesta and Jarone Lee and Michael H Lev and Eva-Maria Ratai and Michael R Wenke and Blair A Parry and Lynn Drake and Richard R Anderson and Terry Rauch and Ramon Diaz-Arrastia and Kenneth K Kwong and Michael Hamblin and Benjamin J Vakoc and Rajiv Gupta},

doi = {10.1148/radiol.230999},

issn = {1527-1315},

year  = {2024},

date = {2024-05-01},

journal = {Radiology},

volume = {311},

number = {2},

pages = {e230999},

abstract = {Background Low-level light therapy (LLLT) has been shown to modulate recovery in patients with traumatic brain injury (TBI). However, the impact of LLLT on the functional connectivity of the brain when at rest has not been well studied. Purpose To use functional MRI to assess the effect of LLLT on whole-brain resting-state functional connectivity (RSFC) in patients with moderate TBI at acute (within 1 week), subacute (2-3 weeks), and late-subacute (3 months) recovery phases. Materials and Methods This is a secondary analysis of a prospective single-site double-blinded sham-controlled study conducted in patients presenting to the emergency department with moderate TBI from November 2015 to July 2019. Participants were randomized for LLLT and sham treatment. The primary outcome of the study was to assess structural connectivity, and RSFC was collected as the secondary outcome. MRI was used to measure RSFC in 82 brain regions in participants during the three recovery phases. Healthy individuals who did not receive treatment were imaged at a single time point to provide control values. The Pearson correlation coefficient was estimated to assess the connectivity strength for each brain region pair, and estimates of the differences in Fisher -transformed correlation coefficients (hereafter,  differences) were compared between recovery phases and treatment groups using a linear mixed-effects regression model. These analyses were repeated for all brain region pairs. False discovery rate (FDR)-adjusted  values were computed to account for multiple comparisons. Quantile mixed-effects models were constructed to quantify the association between the Rivermead Postconcussion Symptoms Questionnaire (RPQ) score, recovery phase, and treatment group. Results RSFC was evaluated in 17 LLLT-treated participants (median age, 50 years [IQR, 25-67 years]; nine female), 21 sham-treated participants (median age, 50 years [IQR, 43-59 years]; 11 female), and 23 healthy control participants (median age, 42 years [IQR, 32-54 years]; 13 male). Seven brain region pairs exhibited a greater change in connectivity in LLLT-treated participants than in sham-treated participants between the acute and subacute phases (range of  differences, 0.37 [95% CI: 0.20, 0.53] to 0.45 [95% CI: 0.24, 0.67]; FDR-adjusted  value range, .010-.047). Thirteen different brain region pairs showed an increase in connectivity in sham-treated participants between the subacute and late-subacute phases (range of  differences, 0.17 [95% CI: 0.09, 0.25] to 0.26 [95% CI: 0.14, 0.39]; FDR-adjusted  value range, .020-.047). There was no evidence of a difference in clinical outcomes between LLLT-treated and sham-treated participants (range of differences in medians, -3.54 [95% CI: -12.65, 5.57] to -0.59 [95% CI: -7.31, 8.49];  value range, .44-.99), as measured according to RPQ scores. Conclusion Despite the small sample size, the change in RSFC from the acute to subacute phases of recovery was greater in LLLT-treated than sham-treated participants, suggesting that acute-phase LLLT may have an impact on resting-state neuronal circuits in the early recovery phase of moderate TBI. ClinicalTrials.gov Identifier: NCT02233413 © RSNA, 2024 },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37991182,

title = {Unspoken Truths: Mental Health Among Academic Surgeons},

author = {Reagan A Collins and Tianna Herman and Rebecca A Snyder and Krista L Haines and Anne Stey and Tania K Arora and Sunil K Geevarghese and Joseph D Phillips and Diego Vicente and Cornelia L Griggs and Imani E McElroy and Anji E Wall and Tasha M Hughes and Srijan Sen and Jaber Valinejad and Andres Alban and J Shannon Swan and Nathaniel Mercaldo and Mohammad S Jalali and Jagpreet Chhatwal and G Scott Gazelle and Erika Rangel and Chi-Fu Jeffrey Yang and Karen Donelan and Jessica A Gold and Colin P West and Carrie Cunningham},

doi = {10.1097/SLA.0000000000006159},

issn = {1528-1140},

year  = {2024},

date = {2024-03-01},

urldate = {2024-03-01},

journal = {Ann Surg},

volume = {279},

issue = {3},

pages = {429-436},

abstract = {OBJECTIVE: To characterize the current state of mental health within the surgical workforce in the United States (US).nnSUMMARY BACKGROUND DATA: Mental illness and suicide is a growing concern in the medical community; however, the current state is largely unknown.nnMETHODS: Cross-sectional survey of the academic surgery community assessing mental health, medical error, and suicidal ideation. The odds of suicidal ideation adjusting for sex, prior mental health diagnosis, and validated scales screening for depression, anxiety, post-traumatic stress disorder (PTSD), and alcohol use disorder were assessed.nnRESULTS: Of 622 participating medical students, trainees, and surgeons (estimated response rate=11.4-14.0%), 26.1% (141/539) reported a previous mental health diagnosis. 15.9% (83/523) of respondents screened positive for current depression, 18.4% (98/533) for anxiety, 11.0% (56/510) for alcohol use disorder, and 17.3% (36/208) for PTSD. Medical error was associated with depression (30.7% vs. 13.3%, P\<0.001), anxiety (31.6% vs. 16.2%, P=0.001), PTSD (12.8% vs. 5.6%, P=0.018), and hazardous alcohol consumption (18.7% vs. 9.7%, P=0.022). 13.2% (73/551) of respondents reported suicidal ideation in the past year and 9.6% (51/533) in the past two weeks. On adjusted analysis, a previous history of a mental health disorder (aOR: 1.97, 95% CI: 1.04-3.65, P=0.033), and screening positive for depression (aOR: 4.30, 95% CI: 2.21-8.29, P\<0.001) or PTSD (aOR: 3.93, 95% CI: 1.61-9.44, P=0.002) were associated with increased odds of suicidal ideation over the past 12 months.nnCONCLUSIONS: Nearly 1 in 7 respondents reported suicidal ideation in the past year. Mental illness and suicidal ideation are significant problems among the surgical workforce in the US.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37437883,

title = {Early Detection of Lung Cancer using small RNAs},

author = {Tobias Sikosek and Rastislav Horos and Franziska Trudzinski and Julia Jehn and Maurice Frank and Timothy Rajakumar and Laura V Klotz and Nathaniel Mercaldo and Mustafa Kahraman and Marco Heuvelman and Yasser Taha and Jennifer Gerwing and Jasmin Skottke and Alberto Daniel-Moreno and Marta Sanchez-Delgado and Sophie Bender and Christina Rudolf and Franziska Hinkfoth and Kaja Tikk and Judith Schenz and Markus A Weigand and Peter Feindt and Christian Schumann and Petros Christopoulos and Hauke Winter and Michael Kreuter and Marc A Schneider and Thomas Muley and Stephan Walterspacher and Martin Schuler and Kaid Darwiche and Christian Taube and Balazs Hegedus and Klaus F Rabe and Kimberly Rieger-Christ and Francine L Jacobsen and Clemens Aigner and Martin Reck and Alexander A Bankier and Amita Sharma and Bruno R Steinkraus},

doi = {10.1016/j.jtho.2023.07.005},

issn = {1556-1380},

year  = {2023},

date = {2023-11-01},

urldate = {2023-07-01},

journal = {J Thorac Oncol},

volume = {18},

number = {11},

pages = {1504-1523},

abstract = {BACKGROUND: Lung cancer remains the deadliest cancer in the world and survival is heavily dependent on tumor stage at the time of detection. Low-dose computed tomography (LDCT) screening can significantly reduce mortality, however, annual screening is limited by low adherence in the USA and still not broadly implemented in Europe. As a result, \<10% of lung cancers are detected through existing programs. Thus, there is great need for additional screening tests, such as a blood test that could be deployed in the primary care setting.nnMETHODS: We prospectively recruited 1,384 individuals meeting the NLST demographic eligibility criteria for lung cancer and collected stabilized whole blood to enable the pipetting-free collection of material, thus minimizing pre-analytical noise. Ultra-deep small RNA sequencing (20 million reads/sample) was performed with the addition of a method to remove highly abundant erythroid RNAs, and thus open bandwidth for the detection of less abundant species originating from plasma or the immune cellular compartment. We utilized 100 random data splits to train and evaluate an ensemble of logistic regression classifiers using small RNA expression of 943 individuals, discovered an 18-small RNA feature consensus signature (miLung), and validated this signature in an independent cohort (441 individuals). Blood cell sorting and tumor tissue sequencing were performed to deconvolve small RNAs into their source of origin.nnRESULTS: We generated diagnostic models and report a median ROC AUC of 0.86 (95% CI 0.84-0.86) in the discovery cohort, and generalized performance of 0.83 in the validation cohort. Diagnostic performance increased in a stage-dependent manner ranging from 0.73 (95% CI 0.71-0.76) for Stage I to 0.90 (95% CI 0.89-0.90) for Stage IV in the discovery cohort, and from 0.76 to 0.86 in the validation cohort. We identified a tumor-shed, plasma-bound ribosomal RNA fragment of the L1 stalk as a dominant predictor of lung cancer. The fragment is decreased following surgery with curative intent. In additional experiments, dried blood spot collection and sequencing demonstrated that small RNA analysis could potentially be conducted via home-sampling.nnCONCLUSION: These data suggest the potential of a small RNA-based blood test as a viable alternative to LDCT screening for early detection of smoking-associated lung cancer.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37142815,

title = {CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII},

author = {Burak Kocak and Bettina Baessler and Spyridon Bakas and Renato Cuocolo and Andrey Fedorov and Lena Maier-Hein and Nathaniel Mercaldo and Henning M\"{u}ller and Fanny Orlhac and Daniel Pinto Dos Santos and Arnaldo Stanzione and Lorenzo Ugga and Alex Zwanenburg},

doi = {10.1186/s13244-023-01415-8},

issn = {1869-4101},

year  = {2023},

date = {2023-05-01},

journal = {Insights Imaging},

volume = {14},

number = {1},

pages = {75},

abstract = {Even though radiomics can hold great potential for supporting clinical decision-making, its current use is mostly limited to academic research, without applications in routine clinical practice. The workflow of radiomics is complex due to several methodological steps and nuances, which often leads to inadequate reporting and evaluation, and poor reproducibility. Available reporting guidelines and checklists for artificial intelligence and predictive modeling include relevant good practices, but they are not tailored to radiomic research. There is a clear need for a complete radiomics checklist for study planning, manuscript writing, and evaluation during the review process to facilitate the repeatability and reproducibility of studies. We here present a documentation standard for radiomic research that can guide authors and reviewers. Our motivation is to improve the quality and reliability and, in turn, the reproducibility of radiomic research. We name the checklist CLEAR (CheckList for EvaluAtion of Radiomics research), to convey the idea of being more transparent. With its 58 items, the CLEAR checklist should be considered a standardization tool providing the minimum requirements for presenting clinical radiomics research. In addition to a dynamic online version of the checklist, a public repository has also been set up to allow the radiomics community to comment on the checklist items and adapt the checklist for future versions. Prepared and revised by an international group of experts using a modified Delphi method, we hope the CLEAR checklist will serve well as a single and complete scientific documentation tool for authors and reviewers to improve the radiomics literature.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid36913348,

title = {Incorporating algorithmic uncertainty into a clinical machine deep learning algorithm for urgent head CTs},

author = {Byung C Yoon and Stuart R Pomerantz and Nathaniel D Mercaldo and Swati Goyal and Eric M L'Italien and Michael H Lev and Karen A Buch and Bradley R Buchbinder and John W Chen and John Conklin and Rajiv Gupta and George J Hunter and Shahmir C Kamalian and Hillary R Kelly and Otto Rapalino and Sandra P Rincon and Javier M Romero and Julian He and Pamela W Schaefer and Synho Do and Ramon Gilberto Gonz\'{a}lez},

doi = {10.1371/journal.pone.0281900},

issn = {1932-6203},

year  = {2023},

date = {2023-03-13},

urldate = {2023-01-01},

journal = {PLoS One},

volume = {18},

number = {3},

pages = {e0281900},

abstract = {Machine learning (ML) algorithms to detect critical findings on head CTs may expedite patient management. Most ML algorithms for diagnostic imaging analysis utilize dichotomous classifications to determine whether a specific abnormality is present. However, imaging findings may be indeterminate, and algorithmic inferences may have substantial uncertainty. We incorporated awareness of uncertainty into an ML algorithm that detects intracranial hemorrhage or other urgent intracranial abnormalities and evaluated prospectively identified, 1000 consecutive noncontrast head CTs assigned to Emergency Department Neuroradiology for interpretation. The algorithm classified the scans into high (IC+) and low (IC-) probabilities for intracranial hemorrhage or other urgent abnormalities. All other cases were designated as No Prediction (NP) by the algorithm. The positive predictive value for IC+ cases (N = 103) was 0.91 (CI: 0.84-0.96), and the negative predictive value for IC- cases (N = 729) was 0.94 (0.91-0.96). Admission, neurosurgical intervention, and 30-day mortality rates for IC+ was 75% (63-84), 35% (24-47), and 10% (4-20), compared to 43% (40-47), 4% (3-6), and 3% (2-5) for IC-. There were 168 NP cases, of which 32% had intracranial hemorrhage or other urgent abnormalities, 31% had artifacts and postoperative changes, and 29% had no abnormalities. An ML algorithm incorporating uncertainty classified most head CTs into clinically relevant groups with high predictive values and may help accelerate the management of patients with intracranial hemorrhage or other urgent intracranial abnormalities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35999710,

title = {Assessing Bias and Limitations of Clinical Validation Studies of Molecular Diagnostic Tests for Indeterminate Thyroid Nodules: Systematic Review and Meta-Analysis},

author = {Catherine DiGennaro and Vahab Vahdat and Mohammad Jalali and Asmae Toumi and Tina Watson and G Scott Gazelle and Nathaniel Mercaldo and Carrie C Lubitz},

doi = {10.1089/thy.2022.0269},

issn = {1557-9077},

year  = {2022},

date = {2022-10-01},

urldate = {2022-09-26},

journal = {Thyroid},

volume = {32},

issue = {10},

pages = {1144-1157},

abstract = {BACKGROUND: Molecular tests for thyroid nodules with indeterminate fine needle aspiration results are increasingly used in clinical practice; however, true diagnostic summaries of these tests are unknown. A systematic review and meta-analysis were completed to (1) evaluate the accuracy of commercially available molecular tests for malignancy in indeterminate thyroid nodules and (2) quantify biases and limitations in studies which validate those tests.



METHODS: PubMed, EMBASE, and Web of Science were systematically searched through July 2021. English language articles that reported original clinical validation attempts of molecular tests for indeterminate thyroid nodules were included if they reported counts of true negative, true positive, false negative, and false positive results. We performed screening and full text review, followed by assessment of eight common biases and limitations, extraction of diagnostic and histopathological information, and meta-analysis of clinical validity using a bivariate linear mixed-effects model.



RESULTS: 49 studies were included. Meta-analysis of Afirma GEC (n=38 studies) revealed a sensitivity of 0.92 (95% CI: 0.90-0.94), specificity of 0.26 (0.20-0.32), negative likelihood ratio (LR-) of 0.32 (0.23-0.44), positive likelihood ratio (LR+) of 1.24 (1.15-1.35), and AUC of 0.83 (0.74-0.89). Afirma GSC (n=10) had a sensitivity of 0.94 (0.89-0.96), specificity of 0.38 (0.27-0.50), LR- of 0.18 (0.10-0.30), LR+ of 1.52 (1.28-1.87), and AUC of 0.91 (0.62-0.92). ThyroSeq v1 and v2 (n=10) had a sensitivity of 0.86 (0.82-0.90), specificity of 0.74 (0.59-0.85), LR- of 0.19 (0.13-0.26), LR+ of 3.52 (2.08-5.92), and AUC of 0.86 (0.81-0.90). ThyroSeq v3 (n=6) had a sensitivity of 0.92 (0.86-0.95), specificity of 0.41 (0.18-0.69), LR- of 0.24 (0.09-0.62), LR+ of 1.67 (1.09-2.98), and AUC of 0.90 (0.63-0.92). 14% of studies conducted blinded histopathologic review of excised thyroid nodules, and 8% made the decision to go to surgery blind to molecular test results.



CONCLUSIONS: Meta-analyses reveal high diagnostic accuracy of molecular tests for thyroid nodule assessment of malignancy risk; however, these studies are subject to several limitations. Limitations and their potential clinical impacts must be addressed and, when feasible, adjusted for using valid statistical methodologies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35880086,

title = {Brief Report: A Blood-Based MicroRNA Complementary Diagnostic Predicts Immunotherapy Efficacy in Advanced-Stage NSCLC With High Programmed Death-Ligand 1 Expression},

author = {Timothy Rajakumar and Rastislav Horos and Paul Kittner and Mustafa Kahraman and Tobias Sikosek and Franziska Hinkfoth and Kaja Tikk and Nathaniel D Mercaldo and Albrecht Stenzinger and Klaus F Rabe and Martin Reck and Michael Thomas and Petros Christopoulos and Bruno R Steinkraus},

doi = {10.1016/j.jtocrr.2022.100369},

issn = {2666-3643},

year  = {2022},

date = {2022-08-01},

urldate = {2022-08-01},

journal = {JTO Clin Res Rep},

volume = {3},

number = {8},

pages = {100369},

abstract = {Introduction: Patients with advanced, non-oncogene-driven NSCLC with high programmed death-ligand 1 (PD-L1) expression are eligible for treatment with immunotherapy. There is, however, an urgent medical need for biomarkers identifying cases that require additional combination with chemotherapy. We previously uncovered a myeloid-based 5-microRNA (5-miRNA) signature that identified responders to immunotherapy in PD-L1 unstratified patients; however, its potential utility in treatment guidance for patients with PD-L1 high tumors remained unclear.



Methods: We trained (n = 68) and validated (n = 56) a 5-miRNA multivariable Cox proportional hazards model predictive of overall survival on small RNA sequencing data of whole blood samples prospectively collected before the commencement of immunotherapy for stage IV NSCLC with PD-L1 tumor proportion score greater than or equal to 50%, treated with PD-1 inhibitor monotherapy (immunotherapy alone [IO]). Specificity was demonstrated in a control cohort treated with immunochemotherapy (ICT) (n = 31).



Results: The revised 5-miRNA risk score (miRisk) stratified IO-treated patients and identified a high-risk group with significantly shorter overall survival (hazard ratio = 5.24, 95% confidence interval: 2.17-12.66,  \< 0.001). There was a significant interaction between the miRisk score and type of treatment (IO or ICT,  = 0.036), indicating that the miRisk score may serve as a predictive biomarker for immunotherapy response. Furthermore, the miRisk score could identify a group of high-risk patients who may benefit from treatment with ICT as opposed to IO (hazard ratio = 0.35, 95% confidence interval: 0.15-0.82,  = 0.018).



Conclusions: The miRisk score can distinguish a group of patients with PD-L1 high, stage IV NSCLC likely to benefit from adding chemotherapy to immunotherapy and may support treatment decisions as a blood-based complementary diagnostic.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35361874,

title = {A blood-based miRNA signature with prognostic value for overall survival in advanced stage non-small cell lung cancer treated with immunotherapy},

author = {Timothy Rajakumar and Rastislav Horos and Julia Jehn and Judith Schenz and Thomas Muley and Oana Pelea and Sarah Hofmann and Paul Kittner and Mustafa Kahraman and Marco Heuvelman and Tobias Sikosek and Jennifer Feufel and Jasmin Skottke and Dennis N\"{o}tzel and Franziska Hinkfoth and Kaja Tikk and Alberto Daniel-Moreno and Jessika Ceiler and Nathaniel Mercaldo and Florian Uhle and Sandra Uhle and Markus A Weigand and Mariam Elshiaty and Fabienne Lusky and Hannah Schindler and Quentin Ferry and Tatjana Sauka-Spengler and Qianxin Wu and Klaus F Rabe and Martin Reck and Michael Thomas and Petros Christopoulos and Bruno R Steinkraus},

doi = {10.1038/s41698-022-00262-y},

issn = {2397-768X},

year  = {2022},

date = {2022-03-01},

journal = {NPJ Precis Oncol},

volume = {6},

number = {1},

pages = {19},

abstract = {Immunotherapies have recently gained traction as highly effective therapies in a subset of late-stage cancers. Unfortunately, only a minority of patients experience the remarkable benefits of immunotherapies, whilst others fail to respond or even come to harm through immune-related adverse events. For immunotherapies within the PD-1/PD-L1 inhibitor class, patient stratification is currently performed using tumor (tissue-based) PD-L1 expression. However, PD-L1 is an accurate predictor of response in only ~30% of cases. There is pressing need for more accurate biomarkers for immunotherapy response prediction. We sought to identify peripheral blood biomarkers, predictive of response to immunotherapies against lung cancer, based on whole blood microRNA profiling. Using three well-characterized cohorts consisting of a total of 334 stage IV NSCLC patients, we have defined a 5 microRNA risk score (miRisk) that is predictive of overall survival following immunotherapy in training and independent validation (HR 2.40, 95% CI 1.37-4.19; P < 0.01) cohorts. We have traced the signature to a myeloid origin and performed miRNA target prediction to make a direct mechanistic link to the PD-L1 signaling pathway and PD-L1 itself. The miRisk score offers a potential blood-based companion diagnostic for immunotherapy that outperforms tissue-based PD-L1 staining.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}