bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep neural networks to predict pathological invasiveness through surgical resection images: A computer vision based prospective clinical trial in pulmonary nodules.
Authors: Yao Henian , Yao Lintong , Zhou HaiYu , Zhan Weijie , Daipeng Xie , Huang Zekai , Lin Hongwei 
Published: June 2023

3068 Background: Pathological subtypes associate with surgical strategies of pulmonary nodule, during which thoracic surgeons make surgical decision through intraoperative frozen resection diagnosis. However, frozen section is time-consuming, vague and maybe misleading due to the limited sample and subjective judgements from pathologists. This study developed a predictive deep neural network for pathological invasiveness of pulmonary nodules based on the surgical resection images of gross specimens. Methods: We prospectively collected the specimen image under standardized lighting conditions in operating theaters from patients with pulmonary nodules treated by surgery resection from June 2020 to September 2021 in Guangdong Provincial People’s Hospital. Images were assigned into training cohort, validating cohort and test cohort as 8:1:1. With data augmentation, DenseNet was applied to classify the pulmonary nodules into high-risk and low-risk group. High-risk group was defined as grade 2 and grade 3 invasive pulmonary adenocarcinoma according to IASLC, while low-risk group was defined as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and grade 1 invasive pulmonary adenocarcinoma. Predictive efficiency was evaluated through area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, accuracy. Model performance would be compared with frozen section pathological diagnosis. Results: Among 1022 patients with pulmonary nodules enrolled, we have acquired 1080 images of the resected nodules in Guangdong Provincial People’s Hospital, with a mean age of 56 years and 39.1% of male patients. The median age of patients were 57 years in training cohort and 56 years in validating cohort, with 38.1% male patients in training cohort and 42.5% male patients in test cohort. This study included 864 images in training cohort, 108 images in validating cohort and 108 images in test cohort. Mean diameter of the nodules were 14.6mm in training cohort and 17.2mm in test cohort. In terms of predictive performance, the AUC value of ROC curves was 96.0% in training cohort and 95.0% in test cohort. In training cohort, the sensitivity of model was 97.6%, with the accuracy of 97.5% and specificity of 97.6%. In test cohort, the sensitivity of model was 97.6%, with the specificity of 90.2% and accuracy of 95.4%. Compared with frozen resection in testing cohort, our deep neural network had higher accuracy in pathological prediction (95.4% vs 88.9%). Conclusions: This study developed and validated a predictive algorithm on pathological invasiveness of pulmonary nodules through computer vision technique with highly sensitivity and specificity, which could assist the rapid evaluation for pathological invasiveness and intraoperative surgical decisions.
Multimodal machine learning analysis of more than 220,000 tumor profiles to diagnose molecular and morphological subtypes of cancer.
Authors: Abraham Jim , Klimov Sergey , Amidi Eghbal , Heimberger Amy B. , Marshall John , Heath Elisabeth I. , Drabick Joseph J. , Rubin Brian , Ali-Fehmi Rouba , Braxton David R. , Sledge George W. , Bryant David , Johnston Curtis , Ghani Hassan , Oberley Matthew James , Spetzler David 
Published: June 2023

3078 Background: The diagnosis of a malignancy is typically informed by clinical presentation and tumor tissue features including cell morphology, immunohistochemistry, and molecular markers. Additionally, multi-omic approaches (Abraham 2021 Transl Oncol) and deep learning models using digital pathology (da Silva 2021 J Pathol) have augmented expert pathologists and led to improved diagnoses but are often not employed together on the same patient. The opportunity exists for a truly multimodal, multi-omic machine learning classifier that comprehensively assesses all aspects of a tumor from the molecular underpinnings to the morphological and histological phenotypic presentation to provide the most accurate diagnosis while at the same time providing predictive biomarker data from the same specimen. Methods: Whole transcriptome data from 220,246 tumor profiles, large panel and whole exome data from over 170,000 tumor profiles, and digital pathology features from over 50,000 tumors were used to construct a multi lineage classifier. The classifier was trained on 256 Oncotree classifications corresponding to established WHO diagnoses where a tumor of each class has been observed at least 30 times in our dataset. The dataset was split 50% for training and the other 50% for testing, UMAP was employed for dimensionality reduction, and ensemble models were used for making the final calls. The performance of the classifier was evaluated by comparing with traditional pathologist-directed diagnostic workup. Results: Tumor lineage classifiers predicted the correct classifications where the primary site was known with accuracies ranging between 97% and 100% when using the 32 highest level Oncotree categories corresponding to human tissues. Accuracy on the most granular Oncotree categories varied with many between 90 and 95%. When applied to Cancer of Unknown Primary (CUP) cases (n = 3589), an unequivocal Oncotree classification could be obtained over 90% of the time. Conclusions: Combining multi-omic and digital pathology information into a multimodal artificial intelligence platform can provide comprehensive information to pathologists to aid in diagnosis. This tool can be used to meet an unmet clinical need to define the lineage of CUP cases, which when coupled with biomarker data, will provide an opportunity to examine whether this information can be used to improve the outcomes of patient with CUP.
CBX-12-101: A first-in-human study of CBX-12, an alphalex peptide drug conjugate (PDC) in patients (pts) with advanced or metastatic solid tumors.
Authors: Rodriguez Rivera Ildefonso I , Hafez Navid , Tolcher Anthony W. , LoRusso Patricia , Wilks Sharon , Tripathy Debu , Gara Michelle , Pearson Paul , DeCillis Arthur P. , Meric-Bernstam Funda 
Published: June 2023

3087 Background: Tumor-targeted drug delivery technologies are urgently needed to overcome conventional chemotherapy’s lack of tumor selectivity. Alphalex conjugates, which contain a pH-low insertion peptide (pHLIP), a linker and a payload, are designed to overcome this lack of selectivity. Unlike antibody drug conjugates, alphalex PDCs target tumors in an antigen-agnostic manner. At pH ≥7.0, the peptide is unstructured. In the low-pH tumor microenvironment the peptide forms an alpha helix and inserts directionally into the tumor cell membrane delivering the linker and payload intracellularly where the linker is cleaved releasing the payload in the cytosol. CBX-12 consists of the pH-sensitive peptide, a self-immolating linker, and the topoisomerase 1 (TOP1) inhibitor exatecan. Methods: In this Phase 1 trial, cohorts of pts were treated with escalating doses of CBX-12 in a 3 + 3 design on three dosing schedules: daily x 5 every 3 weeks, daily x 3 every 3 weeks or once weekly. The primary objectives are safety, tolerability and to determine the MTD and/or RP2D. Secondary and exploratory objectives include evaluation of plasma PK of CBX-12 and exatecan, anti-tumor activity per RECIST v1.1, and measurement of anti-drug antibodies. Results: As of 26 Jan 2023, 42 pts have been treated on three schedules. The most frequent treatment-related AEs (TRAEs) were anaemia (42.9%), nausea (38.1%), fatigue (33.3%), ANC and WBC decreased (33.3% each), vomiting (26.2%), diarrhoea (23.8%) and thrombocytopenia (19.0%). The most frequent Gr3/4 TRAEs were ANC decreased (28.6%), anemia (23.8%), WBC decrease (16.7%), thrombocytopenia (14.3%). Dose limiting toxicity was observed in 6 pts: myelosuppression (6 pts) febrile neutropenia (2 pts), and sepsis (2 pts). Responses were observed in 2 of 9 pts with ovarian cancer and 2 of 8 with breast cancer. The MTD on the daily x 3 schedule is 45mg/m2. Dose evaluation continues on the weekly schedule. Conclusions: In this FIH study of a pH-targeting alphalex PDC, CBX-12 demonstrated single-agent antitumor activity including 4 responses with the dominant toxicity of myelosuppression. Phase 2 expansion cohorts are planned in pts with platinum-resistant ovarian cancer and hormone-receptor positive, HER2 negative breast cancer. Clinical trial information: NCT04902872 .
EZH2/EZH1 inhibitor tulmimetostat (CPI-0209) in patients with advanced solid tumors or hematologic malignancies: Preliminary phase II results.
Authors: Drescher Charles , Walter Harriet S. , Gastinne Thomas , Lakhani Nehal J. , Ribrag Vincent , Rasco Drew W. , Gutierrez Martin , Sullivan Ryan J. , Harvey R. Donald , Banda Kalyan , Kwiatek Michal , Garcia-Sancho Alejandro Martin , Duska Linda R. , Zinzani Pier Luigi , Thakur Anjali , Kann Lennart , Faulhaber Nicola , Jauch-Lembach Julia , Kindler Hedy L. 
Published: June 2023

3094 Background: Many preclinical tumor models with mutations in ARID1A and BAP1 are highly sensitive to EZH2 inhibition. Tulmimetostat (CPI-0209) is an investigational oral, next-generation, dual EZH2/EZH1 inhibitor with durable target coverage. Phase I dose escalation established a recommended Phase II dose of 350 mg (Lakhani et al. ASCO 2021). We report preliminary Phase II findings from the ongoing Phase I/II study (NCT04104776). Methods: Phase II is evaluating tulmimetostat 350 mg once daily in 28-day cycles in 6 disease-based cohorts (Table). The Simon 2-stage study design requires 1 response (complete [CR] or partial [PR]) in stage 1 (n = 10 patients [pts]) for expansion to stage 2 (plus n = 19) (except in M4, see table). Primary endpoint is objective response rate (ORR) based on disease-specific criteria; secondary objectives include safety. Results: As of Nov 08, 2022, 62 pts received ≥1 dose (safety evaluable); 48 had ≥1 postbaseline tumor assessment (efficacy evaluable). Pts had median 3.2 years (0.6–19.6) since diagnosis; 77.4% had ≥2 prior lines of therapy. Cohorts M2, M5 and M6 had completed stage 1 enrollment. At cut-off, best responses among evaluable patients (Table) show 4 cohorts have at least 1 confirmed response, including 2 CR and 1 PR in 7 pts with peripheral T-cell lymphoma (PTCL). 47 pts (75.8%) experienced TEAEs leading to dose modifications, 20 (32.3%) had serious TEAEs, and 5 (8.1%) discontinued treatment due to TEAEs. The most frequent TEAEs (≥15% of pts) (any Grade/Grade ≥3) considered at least possibly related to tulmimetostat were thrombocytopenia (51.6%/27.4%), diarrhea (45.2%/12.9%), nausea (37.1%/0%), anemia (30.6%/16.1%), fatigue (29.0%/0%), alopecia (25.8%/1.6%), dysgeusia (24.2%/0%), vomiting (21.0%/0%), and neutropenia (17.7%/14.5%). Conclusions: Based on response criteria of ORR ≥1/10 pts, the ovarian (M2), endometrial (M3), and mesothelioma (M5) cohorts achieved eligibility for stage 2 expansion (not relevant in M4). The safety profile of tulmimetostat is consistent with EZH2 inhibition and previous data. These preliminary findings in heavily pretreated pts with multiple tumor types, including tumors with ARID1A alterations or BAP1 loss, support ongoing investigation of tulmimetostat. Clinical trial information: NCT04104776 . [Table: see text]
Dose optimization of novel BRAF inhibitor FORE8394 based on PK and efficacy results.
Authors: Sherman Eric Jeffrey , De La Fuente Macarena Ines , Yaeger Rona , Tsai Frank Yung-Chin , Janku Filip , Butowski Nicholas A. , Allen Carl E. , Ammakkanavar Natraj Reddy , Taylor Jennie Webster , Samara Emil , Mould Diane R. , Michelson Glenn , Kline Irina , Paz Michael , Tussay-Lindenberg Alexia , Wong Kongming , Shepherd Stacie Peacock , Jiang Ping , Rodon Ahnert Jordi 
Published: June 2023

3106 Background: FORE8394 is a selective inhibitor of class 1 (V600) and 2 (activating non-V600) BRAF alterations that avoids paradoxical MAPK pathway activation. Consistent with the paradigm shift to optimal dosing vs identifying the maximal tolerated dose, integrated pharmacokinetic (PK), genomics, safety, and efficacy data, and exposure-response modeling were used to identify the recommended dose (RD). Methods: In a single arm phase 1/2a study, patients (pts) age ≥3 years with advanced solid or CNS tumors with BRAF alterations received FORE8394 900-3600 mg/day with or without the PK enhancer cobicistat (cobi) until progression. Efficacy pts had class 1 or 2 BRAF alterations & ≥1 post-baseline assessment (mITT); the BRAF V600 MAPKi naïve, non CRC subset provided a homogenous subset to also inform dose selection. PK was evaluated after single and repeated dosing. Results: To date, 110 pts (age range 4-86 years) received ≥1 dose of FORE8394; 58% had ≥2 prior lines of therapy, 25% had prior MAPKi. PK was independent of age or weight. Cobi increased FORE8394 exposure 2-3-fold. Exposure increased with dose, with less than proportional increase at doses >900 mg BID. Higher Cmax and trough levels were achieved with QD vs BID/TID. Objective (confirmed) responses were observed at all doses; however, objective response rate (ORR) was greatest (50%) at 900mg QD + cobi, with no further increases in ORR at higher doses. Dose-limiting toxicities were only observed at doses ≥1500 mg/day + cobi: 1500-1800 mg/day (4) and 2700-3600 mg/day (1). Similarly, treatment-emergent adverse events (TEAEs) ≥Grade 3 (G3) increased at the higher doses. Only 1 pt discontinued FORE8394 due to treatment-related AE (G3 bilirubin; 3600 mg/day). Conclusions: Based on the entirety of safety, PK, and efficacy data, the optimal RD of FORE8394 in Phase 2 is 900 mg QD + cobi in pts ≥10 years old. This achieved targeted efficacious exposures with robust antitumor activity and favorable safety. This dose optimization is consistent with current guidelines, avoids higher exposure that may lead to higher toxicity and compromise dose intensity. QD dosing also allows for a convenient dosing regimen. A Phase 2 study at the RD is ongoing in pts with recurrent V600E BRAF-mutated primary CNS tumors and advanced solid or CNS tumors with BRAF fusions. Clinical trial information: NCT02428712 . [Table: see text]
Impact of patient travel time on disparities in precision oncology clinical trials.
Authors: Uehara Yuji , Koyama Takafumi , Katsuya Yuki , Sato Jun , Yamamoto Noboru 
Published: June 2023

3113 Background: Precision oncology revolutionizes cancer care, allowing personalized treatments to improve outcomes. Precision oncology often entails participation in genotype-matched clinical trials, resulting in referrals from institutions providing comprehensive genomic profiling (CGP) testing to those conducting these trials. It is still being determined whether there are regional disparities in precision oncology. Methods: We conducted a retrospective review of 1127 patients (pts) referred to National Cancer Center Hospital (NCCH) for participation in genotype-matched clinical trials following CGP testing performed between June 2020 and June 2022. Travel distance and time were calculated utilizing Google Maps from the patient's residence to the NCCH. A total of 23 covariates were preselected and dichotomized per previous research or expert consensus as follows: age, sex, performance status, body mass index, tumor type, number of lines of prior therapies, number of metastatic sites, liver metastases, brain metastases, pleural or peritoneal effusions, biopsiability, neutrophil, hemoglobin, platelet, albumin, creatinine, total bilirubin, LDH, AST, CRP, place of residence (urban vs. rural), referring hospitals, and household income. All independent variables associated with participation in genotype-matched trials (P < 0.20) were included in a multivariable model, and variable importance was calculated using a machine learning (ML) model (gradient-boosted decision tree). Results: A total of 127 (11%) of 1127 pts were enrolled in the genotype-matched trials. Of 127 pts, 82 (65%) and 45 (35%) participated in phase 1 trials and phase 2/3 trials, respectively. The overall median travel distance and time were 38 km (interquartile range [IQR] 21–107) and 55 minutes (IQR 35–110), respectively. In multivariable regression, travel distance (≥100km vs. <100km) was not associated with the proportion of genotype-matched trial participation (9% vs. 12%; odds ratio [OR], 0.68; 95% confidence interval [CI], 0.42–1.07; P = 0.11); however, in pts with travel time ≥120 minutes, the proportion of genotype-matched trial participation was significantly lower than those with travel time < 120 minutes (7% vs. 13%; OR, 0.54; 95% CI, 0.32–0.89; P = 0.019). The proportion of genotype-matched trial participation decreased as travel time increased from <40 minutes to 40–120 minutes to ≥120 minutes (13% vs. 12% vs. 7%, respectively; OR, 0.67; 95% CI, 0.50–0.88; P = 0.006). Travel time was also identified as an important factor in the ML model, whereas low-income and residence in rural areas were of minor importance. Conclusions: Patients with travel time ≥120 minutes were less likely to participate in genotype-matched clinical trials than those with travel time <120 minutes. Regional disparities may be creating inequities in precision oncology, which warrant immediate action, including decentralization of clinical trials.
Temsirolimus (T) in patients (pts) with solid tumors with PIK3CA mutation (mut): Results from the Targeted Agent and Profiling Utilization Registry (TAPUR) study.
Authors: Calfa Carmen Julia , Rothe Michael , Srkalovic Gordan , Duvivier Herbert Leon , Behl Deepti , Straughn J Michael , Yost Kathleen J , Mehmi Inderjit , Pisick Evan P. , Sharma Bhavina B , Thota Ramya , Veljovich Dan Steven , Alese Olatunji B. , Cannon Timothy Lewis , Gaba Anu G. , Urba Walter John , von Mehren Margaret , Grantham Gina N. , Halabi Susan , Schilsky Richard L. 
Published: June 2023

3117 Background: TAPUR is a phase II basket study evaluating antitumor activity of commercially available targeted agents in pts with advanced cancers with genomic alterations. Results in cohorts of pts with breast cancer (BC) and other solid tumors with PIK3CA mut treated with T are reported. Methods: Eligible pts had BC or other solid tumors, measurable disease, ECOG performance status (PS) 0-2, adequate organ function, and no standard treatment (tx) options. Genomic testing was performed in CLIA-certified, CAP-accredited site selected labs. After antihistamine pre-tx, 25 mg of T was infused over 30-60 minutes once weekly until disease progression. Primary endpoint was disease control (DC), defined as complete or partial (PR) response, or stable disease of at least 16+ weeks (wks) duration (SD16+) per RECIST v1.1. For the BC cohort, Simon’s optimal 2-stage design with null DC rate of 15% vs. 35% (power=0.85, α=0.10) was used with stage 1 (n=10) stopping for futility if < 2/10 pts had DC. Low accruing histology-specific cohorts with PIK3CA and T tx were collapsed into 1 histology-pooled (HP) cohort. For the HP cohort, the hypothesized null DC rate of 15% was evaluated by a 1-sided exact binomial test with α=0.10. Secondary endpoints were progression-free survival (PFS), overall survival (OS), and safety. Results: 12 pts with PIK3CA mut with BC and 29 pts with PIK3CA mut in other solid tumors (across 9 tumor types) were enrolled. 2 pts (1 in each cohort) were found to be ineligible after enrolling and were not included in efficacy analyses. Demographics and outcomes for each cohort are shown. At the end of stage 1 in the BC cohort, 1 PR was observed for DC and OR rates of 9%; the cohort was closed for futility (p=0.83). For the HP cohort, 3 PR and 5 SD16+ were observed for DC rate of 29% (p=0.049) and OR rate of 11%; the null hypothesis was rejected. Cancer types in pts with OR or SD16+ included cervical, ovarian and head/neck; most common muts were H1047R/L (3), E545K (2) and E542K (2). 1 pt with ovarian and H1047R has ongoing PR at 86 wks. 11/41 pts had ≥1 tx-related grade 3-4 adverse or serious adverse event, including anemia, headache, hyperglycemia, hypertension, hypertriglyceridemia, mucositis oral, lymphocyte, neutrophil or platelet count decrease, pneumonitis, and sepsis. Conclusions: Although T does not appear to have antitumor activity in pts with BC with PIK3CA mut, it does show antitumor activity in pts with other solid tumors with PIK3CA mut and warrants further study. Clinical trial information: NCT02693535 . [Table: see text]
Pan-cancer diagnostic proteomic signature of tissue-of-origin (TOO) using data-independent acquisition mass spectrometry (DIA-MS) from 1289 human tissue samples.
Authors: Boys Emma , Liu Jia (Jenny) , Cai Zhaoxiang , Noor Zainab , Mackenzie Karen L , Aref Adel , Sykes Erin , Xavier Dylan , Lucas Natasha , Williams Steven , Bucio-Noble Daniel , Koh Jennifer , Hains Peter , Robinson Phillip J , Zhong Qing , Reddel Roger R 
Published: June 2023

3120 Background: Accurate tumour classification based upon tissue of origin (TOO) remains important to guide treatment selection and prognosis but can be challenging in patients with poorly differentiated malignancy, cancer of unknown primary (CUP) or those with prior malignancy. Data-independent acquisition mass spectrometry (DIA-MS)-based proteomics is emerging as a potential clinical diagnostic and prognostic tool. We aimed to develop a protein-based signature to identify histological subtype and adenocarcinoma TOO using DIA-MS data obtained from a pan-cancer study of human tissue samples as an adjunct to histopathological assessment in challenging clinical scenarios. Methods: We performed DIA-MS-based proteomic profiling of 795 fresh frozen tumour and 494 tumour-adjacent normal samples from the Victorian Cancer Biobank in a clinically orientated workflow. We filtered the cohort to include tumour types relevant to CUP. Protein quantification was derived from raw peptide intensity data. Random forest classifiers to identify histological subtype and adenocarcinoma TOO were subsequently trained and tested using 70% and 30% of the data respectively. Evaluation metrics included top-k accuracy (predicting how often the correct class is among the top k predicted classes) and area under the receiver operating curve (AUROC) (one-versus-rest) ± 95% confidence interval. Results: The final tumour cohort consisted of 427 tumour samples representing eight histological subtypes (adenocarcinoma, germ cell tumour, lymphoma, melanoma, renal cell carcinoma, sarcoma, squamous cell carcinoma, thyroid carcinoma) and seven adenocarcinoma TOO (breast, colorectal, liver, lung, ovary, pancreas, prostate). From 9,051 quantified proteins, 83 were identified with potential utility at identifying histological subtype and adenocarcinoma TOO for use in machine learning models. The histological subtype model identified cancer subtype in the test set with top-1 and top-2 accuracy of 0.95 ± 0.03 and 0.98 ± 0.02 respectively. Average test AUROC over all cancer types (n=8) was 0.98 ± 0.02. The adenocarcinoma TOO model identified tumour TOO in the test set with top-1 and top 2- accuracy of 0.88 ± 0.07 and 0.95 ± 0.04 respectively. Average test AUROC over all adenocarcinoma TOO (n=7) was 0.97 ± 0.02. Conclusions: Our clinically orientated DIA-MS-based proteomic workflow and supervised machine learning can identify protein signatures that classify histological subtype and TOO in tumour samples with high accuracy. This technology may assist diagnostic classification of cancer in challenging clinical scenarios, such as CUP.