bluuebunny/crossref_metadata_2025 huggingface dataset:
Survival of stage 4 primary breast cancer cases after surgery by intrinsic subtype: HER2-positive subtype potentially curable.
Authors: Kamada Yoshihiko , Takigami Naoko , Uehara Kanou , Tamaki Kentaro , Tamaki Nobumitsu
Published: June 2022

e13026

Background: With the incorporation of anti-HER2 agents in the treatment of HER2 positive primary breast cancer, stage 4 disease of this subtype has become potentially curable. However, the duration of clinical CR that is needed be certain that the patient is cured, and therefore the necessary duration of anti-HER2 treatment is uncertain. We address this issue in this presentation. Methods: We present cases of stage 4 primary breast cancer who underwent breast surgery after their distant metastases were controlled, with continued post-operative systemic therapy. There were 52 cases of which the intrinsic subtype as of immunohistochemical analysis was as follows; Luminal A - 7 cases, Luminal B, - 14 cases, Luminal-HER2 - 9 cases, HER2-enriched - 7 cases, and Triple negative -15 cases. The patients were followed up for duration of treatment and overall survival. Results: The average post-operative observation period for all 52 cases was 32.6 months. The five year overall survival rate for HER2-enriched cases was 100%, for Luminal-HER2 cases 80%, for Luminal A cases 80%, for Luminal B cases 55%, and for Triple-negative cases 0%. Of the 16 HER2 positive cases (Luminal-HER2 and HER2 enriched), there was only one death at 48.4 months post-operation. The survival of 5 of 15 surviving HER2 positive cases exceed 60 months (5 years). Of the five, only one at 72.0 months has clinically observable disease (cSD). The remaining 4 cases all are clinically disease free (cCR). Of the 4 cCR cases, only 1 case which originally had multiple lung metastases is continuing anti-HER2 therapy at 90.3 months post-operation. The remaining three discontinued anti-HER2 therapy at 18.1 months (lung metastases CR, observation period 71.7 months), 72.3 months (liver and lymph node metastases CR, observation period 90.3 months), and 101.9 months (liver and bone metastases CR, observation period 102.3 months), respectively. The last case terminated anti-HER2 therapy after testing negative for ERBB2 related genes on liquid biopsy. Conclusions: With the proper incorporation of anti-HER2 therapy, Stage 4 HER2 positive primary breast cancer disease seems to be a potentially curable disease, and anti-HER2 therapy probably can be discontinued in cCR patients after 5 years. Liquid biopsy for tumor related genes (e.g. ctDNA, miRNA) might facilitate decision making.
Diagnosis of asymptomatic bone metastases from breast cancer.
Authors: Krokhmal Julia N. , Rozenko Lyudmila Ya , Frantsiyants Elena M. , Guskova Nailya K. , Gusareva Marina A. , Vereskunova Marina I. , Guskova Ekaterina A. , Udalenkova Irina A. , Donskaya Aliya K. , Selyutina Olesya N. , Solntseva Anna A. , Vasilieva Ekaterina O. , Tolmacheva Ekaterina A. , Karnaukhova Elena A. , Rodionova Olga G. , Shatova Iuliana S. , Vashchenko Larisa N. , Ausheva Tatiana V. , Voshedskiy Vitaliy I. , Sakun Pavel G.
Published: June 2022

e13042

Background: The purpose of this study was to analyze the prognostic value of blood levels of fibrinogen (F) in bone metastases from breast cancer (BC). Methods: The study included 160 patients with nodular BC pT2-3N0-1M0-1, including 48 patients in a prospective group (PG) and 112 patients in a retrospective group (RG). All patients underwent initial clinical and laboratory examinations, including determination of alkaline phosphatase (AP) activity (Cobas Integra 4000 plus, Switzerland) and parameters of coagulation hemostasis (STA Compact, France). Bone scintigraphy was performed for complaints of bone pain and/or in cases of elevated AP activity, regardless of SXCT data. Results were considered statistically significant at p < 0.05. Results: Patients in PG were divided into two subgroups depending on F levels: in 31 (64.6%) patients F = 3.67±0.5 g/L, insignificantly exceeding the norm (2.8±0.3 g/L); in 17 (35.4%) patients F = 6.9±0.3 g/L, being 1.9 and 2.5 times (p < 0.05) higher than in subgroup 1 and the norm, respectively. AP activity exceeded the norm by 26% and 40%, respectively (p < 0.1). Bone metastases were detected before treatment in 24 (21.4%) women of RG with pain syndrome; all these patients showed increased blood levels of F, on average up to 7.86±0.8 g/L, similarly to patients in subgroup 2 of PG. F levels in other 88 (78.6%) patients in RG were 3.4±0.6 g/L. The nature and severity of AP changes in RG patients and in both PG subgroups were similar. Initially elevated F levels in 17 PG patients remained unchanged after antitumor treatment (6.2±0.6 g/L). AP activity values were stable after treatment being only 36% and 28% (p < 0.1) higher than the norm, respectively. None of the patients with high F levels complained about bone pain. Unscheduled bone scintigraphy was appointed to 17 patients of PG without clinical symptoms or x-ray (SXCT) signs of pathology but with initially high F which did not decline after treatment. Bone scintigraphy showed metastases to the spine and pelvic bones in 12 of 17 (70.5%) patients 1 month after treatment, subsequently confirmed by MRI results. Conclusions: Initial levels of F within 6.9±0.6 g/L which do not decline after the special treatment allow predicting unfavorable BC course and the presence of metastases to the spine and/or pelvic bones without clinical signs of progression. The proposed method allows diagnosis of advanced tumor process in the absence of clinical signs of the disease with an accuracy of 87.8%, and allows timely corrections to the treatment plan.
Prevalence and survival of TNBC patients vs non-TNBC patients in India: A multi-centric real-world experience based study.
Authors: Ajaikumar Basavalinga Sadasivaiah , Gupta Roshini Das , Raju Anisha , Verma Girdhar , Gupta Anuj
Published: June 2022

e13064

Background: Triple-Negative Breast cancer (TNBC) is a highly aggressive form of breast cancer, characterized by the lack of expression of Estrogen Receptor (ER), Progesterone Receptor (PR), and Human Epidermal Growth Factor-2 (HER-2). Patients with TNBC do not benefit from hormonal or Trastuzumab therapy due to the lack of appropriate targets for these drugs. The current retrospective multicentric hospital-based study at HCG, India, aims to analyze the available treatment paradigms in TNBC patients in real-world evidence. Methods: Data from 3180 female breast cancer patients was collected and compiled from various medical records department (MRD) and hospital database from three HCG centers. The data comprised demographic and clinicopathological details, treatment details and survival information. In addition, the clinical and pathological parameters and survival outcomes were compared between TNBC and non-TNBC patients. Results: About 22.92% (729/3180) patients had TNBC, and 77.08% (2451/3180) patients had designated as non-TNBC. In the TNBC subgroup, 22.36% (163/729) patients belonged to the younger age group (< 40 years), 65.98% (481/729) patients were in the 41-65 age group, while 11.66% (85/729) patients were > 65 years old. Whereas the age-wise distribution in the non-TNBC group was as follows: < 40 years- 15.46% (379/2451) patients, 41-65 years- 70.34% (1724/2451) patients, and > 65 years- 14.2% (348/2451) patients. In the present cohort, TNBC and non-TNBC groups' mean age was 51.06 ± 12.15 years and 53.29 ± 11.55 years, with no statistical significance. About 11.8% (86/729) of patients in the TNBC group and 10.4% (255/2451) of patients in the non-TNBC group opted for neo-adjuvant chemotherapy. About 32.37% of patients in TNBC and 37.45% of patients in the non-TNBC group underwent surgery. The 5-year OS was 68.58% and 83.41% for TNBC and non-TNBC patients, respectively, with a median 5-year survival of 2.13 years (95% CI 2.528-3.204) for TNBC patients whereas 5-year median survival of 2.096 (95% CI 1.36-4.368). The mean survival in TNBC patients < 40 years was 10.14 years, which was significantly lower when compared to patients in the 41-65 years (14.12 years) and > 65 years (19.45 years). Also, patients in the TNBC group who underwent surgery had a higher mean survival of 16.72 years than those who did not opt for surgery (14.49 years). Further, chemotherapy and radiotherapy also increased the life expectancy in patients in both the TNBC and non-TNBC groups. Conclusions: In the present study cohort, TNBC distribution in the at the HCG center in the Indian subcontinent is similar to that of the western population (23% in the US and the UK). Adjuvant chemotherapy and radiotherapy were significantly higher among TNBC cases than non-TNBC cases. Lack of patient consent or proper patient follow-up further reduces survival
Real-life data of antiandrogens (AA) use in metastatic androgen receptor positive triple negative breast cancer (AR+ TNBC): The ATOVAT retrospective French cohort.
Authors: Rhanine Yasmine , Bonnefoi Herve R. , Goncalves Anthony , Debled Marc , Le Moulec Sylvestre , Bonichon-Lamichhane Nathalie , Dubroca-Dehez Benedicte , Grellety Thomas
Published: June 2022

e13074

Background: AR+ TNBC account for around 25% of all TNBC. Identification of those patients is difficult since AR expression (IHC) testing is not routinely recommended. Published data of 3 trials using different antiandrogens (AA) found clinical benefit rates (CBR) at 6 months ranging from 19 to 29% with excellent toxicity profiles. The aim of this retrospective trial was to assess the clinical benefit of AA in real life. Methods: Patients with metastatic AR+ (IHC staining > 10% assessed by local laboratories) TNBC treated with an antiandrogen in metastatic setting were eligible. Patients should have received at least one dose of the following AA: abiraterone acetate, enzalutamide or bicalutamide. Antiandrogens had to be given after documented progression and must not be carried out as part of a clinical trial. Patients could be chemotherapy naïve for their metastatic disease or have received any number of previous line. 30 oncology centers involved in clinical research in France were screened by questionnaire and data were collected in each patient files. The aim was to describe the 6-months CBR and safety in real-life patients. Assessable patients received at least 4 weeks of AA and present at least one disease assessment. Results: 26 patients from 5 French sites were deemed eligible and 24 patients were assessable. Treatment were conducted between January 2002 and January 2021. Median age at initiation of AA was 70 years (range 50-90). 50 % (N = 13) presented liver and/or lung mets and 27% (N = 7) non progressing and non-symptomatic cerebral mets. Median number of previous line of chemotherapy was 3 (range 0-10). AA used were: abiraterone acetate (62%), enzalutamide (8%) and bicalutamide (30%). Median time from mets diagnosis to AA treatment initiation was 18 months (range 0-168). 6-months CBR were 29% (N = 7) with 5 objective responses (2 CR, 3 PR) and 2 SD. 4-months CBR were 33% (N = 8). PFS and OS were 3.2 months (0.8- 36.1) and 9.5 (1.3-63.8) months, respectively. 2 pts further received second line AA with 1 with SD as a best response. 57% (4/7) of patients presenting 6-months clinical benefit received AA in first line versus 18% (3/17).There were no grade 3 or more side effects reported. Conclusions: Real-life data of antiandrogens use in metastatic AR+ TNBC are in line with data from published clinical trials using the same drugs. There were no new safety signal in this retrospective cohort supporting the use of antiandrogens in AR+ (> 10%) TNBC in the absence of other therapeutic opportunity or available clinical trials.
Use of systemic cancer treatments based on a validated survival prediction model in metastatic cancer.
Authors: Roy Mohana , Gensheimer Michael F. , Chang Daniel Tandel , Singhal Surbhi , Khaki Ali Raza
Published: June 2022

e13515

Background: Use of systemic anti-cancer treatment near the end of life (EOL) is recognized as a low value practice with limited benefit to patients. Machine learning (ML) models that identify patients in close proximity to death can help prospectively assess oncology practice of systemic therapy use. We hypothesized that systemic therapy use would be higher based on predicted survival compared with actual survival. Methods: We calculated prevalence of systemic therapy use based on predicted and actual survival among patients with metastatic cancer at Stanford Healthcare from 2008-2019. Patients were included if they were in the test set of the ML model, had an eligible outpatient oncology clinic visit for which a predicted survival was calculated and were deceased . Median predicted survival was calculated from the ML model at each outpatient oncology visit and treatment was linked to a visit date if within 14 days of each other. Prevalence of systemic therapy was calculated for patients with a predicted or actual survival of < 6 months, 6-12 months, 12-18 months and 18-24 months. The five categories of treatment were: chemotherapy, targeted/antibody, hormone, immunotherapy, and other. Results: A total of 951 deceased patients who received anticancer treatment are included and a total of 21,283 doses of treatment were administered with a mean of 22 doses per patient. The median age at metastatic cancer diagnosis was 58 years, 53% of patients were female and most patients identified as White (55%) or Asian (23%). The most common disease groups were gastrointestinal (21.6%), thoracic (18.6%) and breast (14.9%). Overall, the use of different treatment types did not differ based on either predicted or actual survival (Table). In all the survival groupings, chemotherapy remained the predominant medication type, however with a trend of decreasing use with longer predicted and actual survival. Conclusions: The use of cancer medications and the type of medication given did not change based on predicted or actual survival in a large group of patients with metastatic cancer. There was a trend of decreasing chemotherapy use with longer prognosis. Further investigation into use in time intervals closer to (predicted or actual) death and inclusion of those who did not receive any systemic therapy are underway.[Table: see text]
Characteristics and information needs of patients with cancer contacting My Cancer Navigator (MCN): A personalized information service of the Anticancer Fund (ACF).
Authors: Kuijten Gabry A.M. , Battisti Nicolo Matteo Luca , Vandeborne Liese , Buyens Guy , Crispino Sergio
Published: June 2022

e13521

Background: The ACF is an independent private research foundation, based in Belgium and active internationally. Since its inception in 2013, patients and their relatives spontaneously contact the Fund to request information on treatment options including clinical trials. MCN consists of a dedicated team of physicians and scientists, which provides patients and healthcare professionals with free, evidence-based, unbiased, and personalised information to support shared decision-making. All patients receive a summary report, which is discussed in a (video)call with one of our physicians. The objective of the current study was to analyse the characteristics and information requests of people contacting us in 2020-2021 in order to better understand their needs. Methods: For every information request through MCN, the patient agreed to the collection of anonymised data such as sex, age, geographic location and tumor type. We tracked questions asked in 8 categories, tracking a category only once per patient and including only those requiring a literature search. Only descriptive statistics were applied. Results: In 2020-2021, we received information requests for 511 unique patients: 483 adults (95%) and 28 children (5%). In 46% of the cases the patient established contact; for 51% it was a relative or friend, and in 3% the physician. 377 patients (73%) were from Europe, of whom 222 (43%) from Belgium, 56 (11%) from the UK and 23 (5%) from the Netherlands. 109 patients (21%) came from all other continents – of those, 60 (12%) were from the USA. Tumor diagnoses included: gastro-intestinal (114, 22%), breast (110, 22%), central nervous system (77, 15%), gynecological (36, 7%), hematologic (35, 7%), genito-urinary (29, 6%), with sarcomas, lung, head and neck, skin and unknown cancers making up the remainder. Most patients had more than 1 question requiring research, resulting in a total of 880 questions. The Table shows the number and percentage for each category. Conclusions: Within this population, evidence-based information on treatment options, clinical trials, complementary treatments and repurposed drugs were key information needs. Healthcare institutions may lack resources to address all information needs, and My Cancer Navigator may contribute to closing that gap and support patients and healthcare providers. Although the service seems to be well perceived, we are now developing a method to formally assess satisfaction with and impact of our service.[Table: see text]
Strategies for implementation cancer care delivery studies in a community setting.
Authors: Chun Courtney , Braun-Inglis Christa M , Ferguson Valerie , Peterson Laura , Berenberg Jeffrey L.
Published: June 2022

e13525

Background: Cancer care delivery Research (CCDR) is a multidisciplinary science that seeks to improve clinical outcomes and patient well-being by intervening on patient, clinician and organizational factors that influence care delivery. Many CCDR protocols also use implementation science to evaluate how best to introduce promising and previously established interventions into everyday care. These types of studies do not typically utilize research and clinic staff in the same ways as traditional treatment trials. One of the main goals of CCDR is for clinics to continue to use the intervention past the time of protocol participation. Even though these studies intend to improve cancer care delivery in the community, it is often difficult for community sites to embrace and prioritize CCDR due to their differences from typical treatment trials. Our recent observatory research highlights one Minority/Underserved (M/U) NCORPs key strategies for successful implementation of a decision aid CCDR trial in a community setting. Methods: HI M/U NCORP identified multiple steps prior to opening and enrolling patients onto a CCDR implementation trial at our community site. Step 1) Identifying an engaged physician and an advanced practice provider (APP) to be champions for the trial at the clinic. 2) Identifying and engaging key clinic staff and ensuring the workflow is feasible in the clinic. Key processes utilized during the entire course of the trial were also identified. 3) Research staff only coordinating and completing purely study related activities and not implementation activities. 4) Engaging other non-traditionally research involved administration staff and department management. 5) Utilizing ongoing clinic staff input to enhance trial success and optimize clinic workflow. Results: Using the strategies mentioned above, Hawai’i M/U NCORP was able to successfully open and enroll to an internet based CCDR implantation trial at a community clinic from 2019 through 2021. Not only were we able to achieve the highest accrual to a CCDR implementation trial to date within our NCORP, but also managed to do so at a clinic that had previously had minimal involvement in clinical trials in the past. Providers within the clinic received very positive reviews from their patients regarding the trial elements and the clinic has been working toward continuing the use of the material introduced by this CCDR implantation trial. Conclusions: Our successful use of these strategies described have not only been beneficial for our NCORP, but has made a large impact on improving the quality of clinic care at a busy community site. Our involved multi-step opening strategies set the stage for a seamless clinic workflow throughout the trial. This research has proven that these strategies can lead to the success of CCDR implementation studies in a community practice.
Multifactorial drug response modeling based on cancer organoid data.
Authors: Zhu Yitan , Brettin Thomas S. , Partin Alexander , Xia Fangfang , Shukla Maulik , Yoo Hyunseung , Cancino Andrea , Larsen Brian , Shaxted Jenna , Salahudeen Ameen , White Kevin , Stevens Rick L.
Published: June 2022

e13544

Background: Prediction of drug response based on cancer molecular profiles is of paramount importance for precision oncology. Most existing drug response prediction models are built using drug screening data of immortalized cancer cell lines, which usually have altered genomic profiles compared with patient tumors. Recently, patient-derived organoids (PDOs) are emerging as a promising platform better representing patient tumors. We build computational drug response prediction models based on PDO drug screening data, which is the first study of its type to our knowledge. Methods: We successfully developed 27 PDO lines of colorectal cancer and 20 PDO lines of head and neck (H&N) cancer. Transcriptomics, copy number variation (CNV), and targeted DNA mutation data were generated for the PDO lines. The PDO lines were screened with 36 drugs of diversified mechanisms. The area under the dose response curve was taken as the response measurement. We used the LightGBM algorithm to build response prediction models based on cancer molecular data and drug chemical descriptors/fingerprints. To investigate the influence of different factors on the prediction performance, including different cancer types, cancer molecular features, drug features, data preprocessing methods, and others, we applied a multifactorial analysis scenario to build and evaluate 3,384 prediction models constructed with all possible combinations of the factors. For example, we built prediction models for H&N and colorectal PDOs separately and jointly. Results: A prediction model built for H&N PDOs achieved the highest prediction performance among all prediction models, which was R2 of 0.790 in 10-fold cross-validation. The model was built using drug descriptors, CNVs, and expressions of “landmark” genes well-representing cellular transcriptomic changes identified in the LINCS project. The table below includes all the factorial differences that caused an average R2 change larger than 1%. All R2 changes are statistically significant (p-values < 1×10–50), evaluated by pair-wise t-tests comparing models built with the status of the factor changed. The prediction performance increased, from colorectal cancer to two cancer types combined, and to H&N cancer. Gene expression data, either whole-transcriptome or the subset of LINCS genes, boosted the prediction performance. Between the two different dyes used to stain dead cells, TO-PRO-3 provided a higher prediction performance than Caspase-3/7. Conclusions: The highest drug response prediction performance achieved is R2 of 0.790. Cancer type, dye, and whether gene expressions are used in modeling are the factors most influential on prediction performance.[Table: see text]
QOPI Clinical Informatics: A digital platform to enable real-time quality reporting, clinical decision support, and rapid learning.
Authors: Williams Casey B. , Karagoz Kubra , Elsey Rachel , Meißner Tobias , Higashi Mitchell , Schadt Eric , Jun Tomi , Wang Xiaoyan , Chen Rong , Zhou Xiang , Guin Sunny , Lee John H. , Oh William K.
Published: June 2022

e13545

Background: The ASCO Quality Oncology Practice Initiative (QOPI) was established in 2006 to facilitate quality measurement and continuous improvement of cancer care.1 ASCO and QOPI leadership encourage a continuous learning environment to develop new tools and methodologies that improve quality and maximize outcomes and value in oncology practice.2 Parallel advances in tumor molecular profiling, bioinformatics, and EMR data curation are adding new insights to support the real-world research of longitudinal patient journeys. Methods: Referencing the QOPI Certification Track 2021 Measures Summary, we employed data visualization techniques to create a precision oncology dashboard for breast cancer. This evaluation included 409 breast cancer patients for whom genomic testing was performed and presented at the Avera molecular tumor board. Patients were then matched to appropriate biomarker-based targeted therapies. Results: The dashboard identified and prioritized areas for enhanced data capture to inform strategies for care-coordination, research, and delivery of specialized care. Conclusions: An interactive, visual platform may help facilitate the integration of QOPI reporting into CancerLinQ, thereby fostering a real-time, bidirectional system for quality reporting, clinical decision support, and rapid learning.3,4 The next stage will (1) expand the tumor list; and (2) apply NLP methods to assess accuracy, outcomes and temporal reasoning to create interactive graphs for patient referral patterns and surgery decision drivers. References: 1. Neuss MN, Desch CE, McNiff KK, et al: A process for measuring the quality of cancer care: The Quality Oncology Practice Initiative. J Clin Oncol. 2005 2. Blayney DW, McNiff K, Eisenberg PD, Gilmore T, Jacobsen PB, Jacobson JO, Kadlubek PJ, Neuss MN, Simone J. Development and future of the American Society of Clinical Oncology's Quality Oncology Practice Initiative. J Clin Oncol. 2014 3. Blayney DW: Enhancing quality through innovation: American Society of Clinical Oncology presidential address 2010. J Clin Oncol. 2010 4. Schilsky R, Hauser R, Mann J, et al: Lessons learned from the development of the CancerLinQ prototype: Clinical decision support. J Clin Oncol. 31, 2013[Table: see text]
From patient generated data to precision medicine: An example of a patient-centered application in oncology.
Authors: Beutter Chantal N.L. , Martens Uwe Marc , Fegeler Christian
Published: June 2022

e13559

Background: To practice precision medicine, the inclusion of patients in therapy is becoming increasingly important. Terms such as ‘patient empowerment’ (PE), ‘patient reported outcome’, and ‘patient generated data’ (PGD) are becoming quite present in current research. In order to implement PE consistently and integrate PGD in therapy, it is necessary to provide patients with specific tools. Methods: In order to develop such a corresponding system, challenges for implementation were identified as well as the current state of PGD in oncology. The aim was to design a system suitable for everyday use that allows the measurement of individual effects like quality of life (QoL) directly at the patient. Based on the positive experience of a previous developed questionnaire tool (EQU) to support physician-patient communication and the findings of the methodology a new patient-centered system was conceptualized with an automated data collection using sensor systems. A decisive factor was to support patients in self-management between treatment periods. This solution was then discussed extensively with stakeholders in order to identify and prioritize functionality and therefore incorporate specific user needs of oncological patients. Results: A great acceptance and need for tools to support patients especially between appointments was identified. In order to enable the recognition of individual characteristics, a continuous survey is essential. Based thereon an application called Lion-App for longitudinal QoL assessment in oncology was developed. A user-centered development aligns the app with the needs of the patients while HL7 FHIR provides the framework for an interoperable exchange. To reduce the burden of continuous interaction, sensors are used for data collection. With an interpretation of this data in the context of QoL, patients are able to visualize their personal course to gain an even better understanding of their disease. With the possibility to perform self-management between appointments, patients are lifted to another level of empowerment. In addition, the gained knowledge may then be used to focus on the effects of the disease in the next appointment and thus take a self-determined role in treatment. First evaluations of the app have been started. Conclusions: There are many ways to use PGD in the manner of precision medicine. The main factor is to create awareness among patients, collect data in an exchangeable way and involve end users early into development. A great opportunity to reduce user interactions and therefore enable long-term usage is the integration of sensor technologies. As a secondary use, algorithms can be extended via the expanded data base to become even more individualized to the patient. Thus, in the long run, such a system may help to further strengthen the cooperation between patient and physician and thus to enable a personalized, QoL-based treatment in oncology.
Drug response prediction in patient-derived xenografts with data augmentation and multimodal deep learning.
Authors: Partin Alexander , Brettin Thomas S. , Zhu Yitan , Shukla Maulik , Xia Fangfang , Yoo Hyunseung , Dolezal James M , Kochanny Sara , Pearson Alexander T. , Evrard Yvonne A. , Doroshow James H. , Stevens Rick L.
Published: June 2022

e13572

Background: Prediction of drug response is a critical research area in precision oncology and has been previously explored with large drug screening studies of cancer cell lines (CCLs). Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies because the in vivo environment of PDXs helps preserve tumor heterogeneity and usually better mimics drug response of patients with cancer compared to CCLs. Methods: We investigate multimodal neural network (NN) and data augmentation for drug response prediction in PDXs. The multimodal NN learns to predict response using drug descriptors, gene expressions (GE), and histology whole-slide images (WSIs) where the multi-modality refers to tumor features only. The NN uses late integration where separate subnetworks are used to encode the input feature types before concatenation and prediction layers. Median tumor volume per treatment group is assessed relative to the control group to create a binary variable representing response. The data include twelve single-drug and 36 drug-pair treatments resulting in 2,556 single-drug and 2,203 drug-pair response values. Pathology and omics data from 487 PDXs from NCI's Patient Derived Models Repository are used as tumor feature model inputs. We explore whether the integration of WSIs with GE improves predictions as compared with models that use GE alone. We use two methods to address the limited number of response values in the dataset: 1) homogenize drug representations which allows to combine single-drug and drug-pairs into a single dataset, 2) augment drug-pair samples by switching the order of drug features which doubles the sample size of all drug-pair samples. These methods enable us to combine single-drug and drug-pair treatments which results in 6,962 responses, allowing us to train multimodal and unimodal NNs without changing architectures or the dataset. Results: Prediction performance of three unimodal NNs which use GE (um1, um2, and um3) are compared to assess the contribution of data augmentation methods. NN um1 that uses the full dataset which includes the original and the augmented drug-pair treatments as well as single-drug treatments significantly outperforms NNs (p-values < 0.01) that ignore either the augmented drug-pairs (um2) or the single-drug treatments (um3). In assessing the contribution of multimodal learning, results show that the multimodal NN (mm) outperforms both unimodal NNs that ignore either the GE (um4) or the WSIs (um1). However, the improvement of mm over um1 is not statistically significant (p-value < 0.26). Conclusions: Our results show that data augmentation and integration of histology images and GE can help improve prediction performance of drug response in PDXs.[Table: see text]
Using infrared spectroscopy to analyze breath of patients diagnosed with breast cancer.
Authors: Naz Farah , Groom Amy Grace , Mohiuddin MD , Sengupta Arpita , Daigle-Maloney Trisha , Burnell Margot J. , Michael James Charles Roger , Graham Stephen , Beydaghyan Gisia , Scheme Erik , Phinyomark Angkoon , Larracy Robyn
Published: June 2022

e13579

Background: Population-level screening programs aimed at early detection and treatment of breast cancer saves lives. Analyzing breath using infrared spectroscopy offers a highly sensitive, non-invasive, and cost-effective mechanism for identifying exhaled volatile organic chemicals, and it is hypothesized that it may identify differences in the “breathprint” of women with breast cancer relative to those without a breast cancer diagnosis. Methods: Alveolar breath samples (10 L) were collected using a Breathe BioMedical alveolar breath sampler onto Tenax TA sorbent tubes. Corresponding room air samples (10 L) were collected in the same manner. Absorption spectra of the samples at a desorb temperature of 75 °C were measured by infrared cavity ring-down spectroscopy (IR-CRDS), a highly sensitive method of measuring absorption coefficients due to trace volatile organic compounds (VOCs) present in exhaled breath. After subtracting room air absorption and ordering each measured spectrum by increasing wavelength, missing values were imputed using spline interpolation. The absorption spectra were then normalized using one of four techniques: min-max, vector, peak or standard normal variate normalization. The first derivatives of the normalized absorption coefficients (187 values in total) were then used as features for discriminating samples from subjects with breast cancer and controls. The most useful features were selected based on minimum redundancy and maximum relevance (mRMR) and were used to train a linear support vector machine (SVM) classifier. Performance of classification models was estimated based on two data splitting configurations, non-nested leave-one-out cross-validation (LOOCV) and nested LOOCV. These approaches provide upper and lower bounds of expected model performance. Classification performance was used for tuning the number of features included in each model. Results: The analysis of this study is based on the spectra obtained from 70 participants (38 breast cancer and 32 controls), collected at the Saint John Regional Hospital in New Brunswick, Canada. Table below shows the non-nested and nested performance characteristics of classifiers with the best performing normalization technique. The number of features given for the nested model is not an integer as it indicates an average across the cross-validation folds. Conclusions: These results suggest that the classification of alveolar breath using IR-CRDS is a promising technique for the detection of breast cancer. Performance of classification models. AUC is the area under the receiver operator characteristics curve.[Table: see text]
The need for artificial intelligence curriculum in medical education: A Canadian cross-sectional study of future oncology trainees.
Authors: Pucchio Aidan , Rathagirishnan Raahulan , Caton Natasha , Gariscsak Peter , Del Papa Joshua , Vo Vicky , Lee Wonjae , Nabhen Jacqueline J. , Ynoe de Moraes Fabio
Published: June 2022

e13583

Background: Emerging artificial intelligence (AI) technologies have diverse applications in medicine, with early evidence suggesting that AI tools can accurately perform key tasks in oncology. As AI tools advance towards clinical implementation, skills in how to use and interpret AI in a healthcare setting could become integral for physicians. This study seeks to assess exposure to AI in medical education among trainees interested in pursuing a career in oncology, and the need for AI education in medicine. Methods: A 32 question survey for Canadian undergraduate medical students was distributed to students at all 17 Canadian medical schools. The survey assessed the currently available and perceived need for learning opportunities about AI and barriers to educating about AI in medicine. Interviews were conducted with participants to provide narrative context to survey responses. Likert scale (LS) survey questions were scored from 1 (disagree) to 5 (agree), and analyzed using a two-sided one sample t-test vs a neutral value. Interview transcripts were analyzed using qualitative thematic analysis. Results are described as mean LS score ± standard deviation. Results: We received 486 responses from 17 of 17 medical schools. Of these respondents, 98 (20.2%) are willing to pursue a residency in an oncology-related field (pathology, radiology, general surgery, internal medicine, radiation oncology). Respondents agreed that AI applications in medicine would become common in the future (3.80±0.38) and would improve medicine (3.71±0.54). Further, respondents agreed that they would need to use and understand AI during their medical careers (3.76±0.572; 3.43±0.773), and that AI should be formally taught in medical education (3.43±0.756). In contrast, a significant number of participants indicated that they did not have any formal educational opportunities about AI (1.76±0.785) and that AI-related learning opportunities were inadequate (2.12±0.802). Interviews with 18 students were conducted. Emerging themes from the interviews were a lack of formal education opportunities and logistical challenges in adding AI to curriculum. Conclusions: A lack of educational opportunities about AI in medicine were identified across Canadian medical students. Given that medical students overwhelmingly believe that AI is important to the future of medicine, and AI tools are currently progressing towards clinical implementation, AI should be considered for inclusion in formal medical curriculum.
Utilizing Natural Language Processing (NLP) to identify breast cancer associated-lung metastases from pathology reports to delineate characteristics and challenges of this common site of breast cancer recurrence.
Authors: Valentín López José Carlos , Ho Alice Y. , Moy Beverly , Isakoff Steven J. , Juric Dejan , Ellisen Leif W. , Peppercorn Jeffrey M. , Bardia Aditya , Hughes Kevin S. , Vidula Neelima
Published: June 2022

e13592

Background: NLP (artificial intelligence) can automate the identification of records in large datasets. The purpose of this study was to evaluate the feasibility of NLP to identify breast cancer-associated lung metastases to understand clinical characteristics and challenges posed by this common site of breast cancer recurrence. Methods: Patients with pathologically confirmed breast cancer associated-lung metastases seen at a large academic center between 3/2012-5/2019 were identified using NLP of institutional pathology reports, with an IRB approved protocol. Chart review was performed to confirm breast cancer associated-lung metastases and determine clinical and pathological features. Results: Using NLP, 32 patients with pathology reports denoting breast cancer associated-lung metastases were identified, with pathologic confirmation of lung biopsy tissue in the majority of cases (24), and pleural fluid specimens (8) on the remainder. Ten of 32 (31%) were HR+/HER2-, 3/32 (9.3%) HER2+, and 19/32 (59%) TNBC. The majority were invasive ductal carcinoma (21/26) with the remainder invasive lobular carcinoma (2/26) or mixed histology (3/26). Median age at lung metastasis diagnosis was 62 years (range 31-88). The median time to development of lung metastasis following primary breast cancer was 5.6 years (range 0-24.8 years). Fifty six percent of lung metastases were detected on imaging and 44% by symptoms including dyspnea, cough, or pain. Tumor tissue genotyping results on the lung metastases were available for 8 patients showing PI3KCA (5), TP53 (3), SMARCA4 (2), ERBB2 (1), FGFR3 (1), ATM (1), CDK4 (1), MYC (1), and ESR1 (1). Treatment after diagnosis of lung metastases included hormone therapy (61%), chemotherapy (84%), lung irradiation (26%), and surgical resection of lung metastases (6%). Lung metastases were associated with considerable morbidity including pleural effusion (15%), dyspnea (6%), pneumothorax (3%), hemothorax (3%), and atelectasis (3%). Patients diagnosed with lung metastases had brain (32%), bone (35%), renal (6%), skin (3%) and adrenal (3%) metastases during disease course. Conclusions: NLP can help identify organ specific metastases from pathology reports, such as breast cancer associated-lung metastases, which can then facilitate observational, translational, and clinical research to characterize and address challenges posed by this common site of breast cancer recurrence. This cohort of patients highlights the morbidity of breast cancer associated-lung metastases and potential role of NLP for disease characterization and clinical research. (Support from ASCO Medical Student Rotation for Underrepresented Populations Award.)
Oncometabolite profiling identifying estrogen-responsive genes in breast cancer.
Authors: Talukdar Joyeeta , Srivastava Tryambak Pratap , Gogoi Gayatri , Rai Avdhesh , Dhar Ruby , Karmakar Subhradip
Published: June 2022

e13611

Background: Metabolomic profiling in breast cancer (BC) is an emerging avenue to identify clinically actionable biomarkers as well as targeted therapeutics. Altered metabolic pathways has been identified in BC. Studies have discovered an association between oncometabolite levels, DNA methylation and other epigenetic changes, with prognosis in BC. Changes in cancer cells metabolism may thus indicate disease aggressiveness and its response to therapy. Methods: An in-silico based oncometabolite profiling was performed in ER +ve and TNBC (ER-/PR-/Her2-) data sets using NCBO-GEO tools. . Inclusion criteria included studies on Human subjects, having at least 2 ER+ PR+ and TNBC samples. After manually filtering the top 250 most significant differentially expressed genes in all studies, those with clear metabolic functions as per the GO category were pooled together, which included both the overexpressed and downregulated genes. 15 unique genes were cherry-picked and taken for further analysis out of which six were downregulated, and nine were upregulated. The identified genes harbouring ERE in their upstream 1000bp region were further analysed to identify CpG islands. In addition, we also performed a Bio-Reader based Text Mining Tool to streamline Pubmed abstracts with the positive strings “oncometabolite,” “breast cancer,” “stem cells” “survival”. We also queried against negative strings “sequencing,” “mutations,” “infections,” “drug-resistance” to develop a list of highly focused abstracts. Results: Novel estrogen-regulated oncometabolite that were differentially expressed in ER+ve vs TNBC BC were identified. Further, we reinforced the fact that 4 out of 7 targets had upstream CpG islands within 1000bp n Both upregulated AMY2A, B3GNT5, PNPLA3 and 2HG whereas and downregulated genes in PLCD4, FBP1 and CA12 along with one target identified from the literature were selectively expressed in ER+ve BC. Conclusions: Our study has identified estrogen-responsive metabolic genes involved in BC. We are currently validating our results in a wet lab setup from breast cancer patient samples. Validated targets may be tested for therapeutic intervention in BC.
Addressing unmet needs of cancer care in a lower middle income country by establishing an electronic hospital based cancer registry.
Authors: Gunasekera Sanjeeva , Seneviratne Sanjeewa , Pramod Hasitha , Carson Laura M. , Berry Scott , Booth Christopher M. , Wijeratne Thiwanka
Published: June 2022

e13621

Background: Sri Lanka (SL) is a Lower Middle Income Country (LMIC) which has seen its cancer incidence double in the last 25 years. National Cancer Institute Sri Lanka (NCISL) is the only dedicated cancer hospital in SL and provide care to around 40% of all patients diagnosed with cancer in the country. The existing SL Cancer Registry, primarily contains only socio-demographic data and there was no systematic method to capture cancer treatment and outcome data This presents a missed opportunity in identifying and bridging gaps in cancer care delivery and health equities in an effective manner. Methods: We built an electronic hospital based cancer registry using the “free to use” cloud based District Health Information System (DHIS2) software platform and prospectively collected disease, demographic, treatment and outcome data on selected cancers treated at the NCISL from 2016 onwards. This platform is easily adaptable and will allow downloading data to standard statistical software directly for data analysis and can be integrated with other existing health information systems. its easily adaptable . The variables captured were identified through focus group sessions with both healthcare workers and patient representatives. Results: To date, data from over 5,000 breast, 1500 colorectal and 600 thyroid cancer patients have been captured. From this database data on disease characteristics, demographic details, survival rates, treatment toxicities, referral pathways, health care seeking patterns, comorbidity data, health care related quality of life, treatment guideline concordance, out of pocket expenses incurred while on treatment, palliative care delivery, cancer care equity gaps can be elicited. Conclusions: Our database demonstrates that it is feasible to build low cost, easy to maintain yet good quality custom made data bases to collect cancer care related data. Careful selection of variables will allow health systems to identify existing gaps which is the key initial step in addressing them with a view of improving cancer related outcomes. Integrating demographic and outcome data within the same database will help in identifying health system inequities. Continuous data collection will allow assessing the efficacy of any intervention launched to bridge these gaps in the future. This database can also be adapted with minimal change by other LMICs to generate real world data to inform national cancer control policy planning.
Understanding disparities in research publications in high-impact factor journals by cancer site.
Authors: Sinha Nikita , McDonald Amanda , Barry Danika , Barber Emma Longley
Published: June 2022

e13626

Background: Prior studies have shown disparities in funding allocation for gynecologic cancers after accounting for their mortality, incidence, and person-years of life lost. Whether disparities exist in high impact factor journals is unknown. The purpose of this study is to analyze research publication trends in high impact factor journals comparing gynecologic cancers to other cancers from 2000 to 2018. Methods: Abstracts from the top 55 impact factor journals, as measured by Clarivate, from 2000 to 2018 were extracted from PubMed. We developed an algorithm to search the title of the abstract to determine whether the abstract was about cancer and identify the cancer type. The algorithm was validated against the gold standard of human review in 1143 abstracts and yielded a sensitivity of 97% and a specificity of 100% for identifying cancer abstracts and an accuracy of 95% for identifying cancer site. Using NCI’s SEER database, site-specific mortality to incidence ratios were calculated and multiplied by person-years of life lost to derive cancer-specific lethality for 19 cancer sites. An article proportion for each cancer site was calculated. Article proportion was divided by lethality to calculate Article to Lethality scores for gynecologic malignancies and compared to the other cancer sites. Results: We identified 128,377 articles; 31,045 (24%) were about cancer and 1,189 (3.8%) were about gynecologic cancers. Ovarian, uterine, and cervical cancers ranked 12th (score 21.9), 13th (20.9), and 18th (10.1) out of 19 cancers for median Article to Lethality scores. The highest scores were for breast cancer (score 427) and prostate cancer (234). Ovarian and uterine cancers had lower Article to Lethality scores compared to 11 other cancers, while cervical cancer was lower than 16 other cancers (p < 0.01 for all comparisons). Analyses of the trends for gynecologic cancers over the last 18 years showed no change in Article to Lethality scores. Conclusions: Article to Lethality scores for gynecologic cancers are significantly lower than other cancer sites, indicating a disparity in research publications in high impact factor journals that persists over the last 18 years. Increased funding is essential to ensure continued advancement and progress in research for women with gynecologic cancers.[Table: see text]