Comorbidities and survival of multiple myeloma patients diagnosed in Finland between 2000 and 2021.

Advances in treatment have improved the survival of multiple myeloma (MM) patients, but the disease remains incurable. Here, in this nationwide retrospective real-world evidence (RWE) study, we report the patient characteristics, incidence, overall survival outcomes, comorbidities, and healthcare resource utilization (HCRU) of all adult MM patients diagnosed between 2000 and 2021 in Finland. A total of 7070 MM patients and their 21,210 age-, sex- and region-matched controls were included in the analysis. The average MM incidence doubled from 4.11 to 8.33 per 100,000 people during the follow-up. The average age-standardized incidence also showed a significant increase over time (2.51 in 2000 to 3.53 in 2021). An increase in incidence was particularly seen in older population, indicative of improved diagnosis praxis. The median overall survival (mOS) of the MM patients and their matched controls was 3.6 and 15.6 years, respectively. The mOS of all MM patients increased significantly from 2.8 years (2000-2004) to 4.4 years (2017-2021) during the follow-up period. Distinctively, in patients who received autologous stem cell transplantation (ASCT), the mOS was 9.2 years, while in patients who did not receive ASCT, the mOS was only 2.7 years. MM patients showed more comorbidities at index and increased HCRU than their matched controls. The longer median survival and decreased risk of death indicate improved treatment outcomes in MM patients in Finland.

Real-World Evidence Study of Patients with KRAS-Mutated NSCLC in Finland.

While KRAS is the most frequently mutated oncogene in non-small cell lung cancer (NSCLC), KRAS-mutant tumors have long been considered difficult to treat and thus, an unmet need still remains. Partly due to the lack of targeted treatments, comprehensive real-world description of NSCLC patients with KRAS mutation is still largely missing in Finland. In this study, all adult patients diagnosed with locally advanced and unresectable or metastatic NSCLC from 1 January 2018 to 31 August 2020 at the Hospital District of Helsinki and Uusimaa were first identified in this retrospective registry-based real-world study. The final cohort included only patients tested with next generation sequencing (NGS) and was stratified by the KRAS mutation status. A total of 383 patients with locally advanced and unresectable or metastatic NSCLC and with NGS testing performed were identified. Patients with KRAS mutation (KRAS G12C n = 35, other KRAS n = 74) were younger than patients without KRAS mutations, were all previous or current smokers, and had more often metastatic disease at diagnosis. Also, these patients had poorer survival, with higher age, Charlson comorbidity index (CCI) being 5 or above, and KRAS G12C being the most significant risk factors associated with poorer survival. This suggests that the patients with KRAS mutation have a more aggressive disease and/or tumors with KRAS mutation are more difficult to treat, at least without effective targeted therapies.

Observational study on the evolution of systemic treatments for advanced renal cell carcinoma in Southwest Finland between 2010 and 2021.

Background: Novel receptor tyrosine kinase inhibitors and immune checkpoint inhibitors have been introduced to the treatment of advanced renal cell carcinoma (aRCC) during the past decade. However, the adoption of novel treatments into clinical practice has been unknown in Finland. Objectives: Our aim was to evaluate the use of systemic treatments and treatment outcomes of aRCC patients in Southwest Finland during 2010-2021. Design and Methods: Clinical characteristics, treatments for aRCC, healthcare resource utilization, and overall survival (OS) were retrospectively obtained from electronic medical records. Patients were stratified using the International Metastatic RCC Database Consortium (IMDC) risk classification. Results: In total, 1112 RCC patients were identified, 336 (30%) patients presented with aRCC, and 57% of them (n = 191) had received systemic treatment. Pre-2018, sunitinib (79%) was the most common first-line treatment, and pazopanib (17%), axitinib (17%), and cabozantinib (5%) were frequently used in the second-line. Post-2018, sunitinib (52%), cabozantinib (31%), and the combination of ipilimumab and nivolumab (10%) were most commonly used in the first-line, and cabozantinib (23%) in the second-line. Median OS for patients with favorable, intermediate, and poor risk were 61.9, 28.6, and 8.1 months, respectively. A total of 73%, 74%, and 35% of the patients with favorable, intermediate, and poor risk had received second-line systemic treatment. In poor-risk patients, the number of hospital inpatient days was twofold higher compared to intermediate and fourfold higher compared to favorable-risk patients. Conclusion: New treatment options were readily adopted into routine clinical practice after becoming reimbursed in Finland. OS and the need for hospitalization depended significantly on the IMDC risk category. Upfront combination treatments are warranted for poor-risk patients as the proportion of patients receiving second-line treatment is low. Registration: Clinical trial identifier: ClinicalTrials.gov NCT05363072.

Observational study on the evolution of systemic treatments for advanced renal cell carcinoma in Southwest Finland between 2010 and 2021 The aim of the study was to evaluate the use of novel medical treatments for advanced kidney cancer in routine clinical practice in Southwest Finland from 2010 to 2021 and to study the impact of IMDC risk factors on patients' survival and healthcare resource utilization. Before 2018, sunitinib (79%) was the most common first-line treatment for advanced kidney cancer, and pazopanib (17%), axitinib (17%), and cabozantinib (5%) were frequently used in the second-line. After 2018, sunitinib (52%), cabozantinib (31%), and the combination of ipilimumab and nivolumab (10%) were most commonly used in the first-line, and cabozantinib (23%) in the second-line treatment. The IMDC risk category predicted the patient's prognosis accurately as the median overall survival times for patients with favorable, intermediate, and poor risk were 61.9 months, 28.6 months, and 8.1 months, respectively. 73­74% of the patients with favorable and intermediate risk had received second-line medical treatment for advanced disease, whereas only 35% of the patients with poor risk had received second-line treatment after disease progression on the first-line treatment. Among patients with poor risk, the number of hospital inpatient days was twofold higher compared to intermediate and fourfold higher compared to favorable-risk patients. This study demonstrated that new treatment options for advanced kidney cancer were readily adopted into clinical practice and IMDC risk scoring was a valuable tool in determining patient prognosis and healthcare resource utilization.

Detection of cultured breast cancer cells from human tumor-derived matrix by differential ion mobility spectrometry.

The primary treatment of breast cancer is the surgical removal of the tumor with an adequate healthy tissue margin. An intraoperative method for assessing surgical margins could optimize tumor resection. Differential ion mobility spectrometry (DMS) is applicable for tissue analysis and allows for the differentiation of malignant and benign tissues. However, the number of cancer cells necessary for detection remains unknown. We studied the detection threshold of DMS for cancer cell identification with a widely characterized breast cancer cell line (BT-474) dispersed in a human myoma-based tumor microenvironment mimicking matrix (Myogel). Predetermined, small numbers of cultured BT-474 cells were dispersed into Myogel. Pure Myogel was used as a zero sample. All samples were assessed with a DMS-based custom-built device described as "the automated tissue laser analysis system" (ATLAS). We used machine learning to determine the detection threshold for cancer cell densities by training binary classifiers to distinguish the reference level (zero sample) from single predetermined cancer cell density levels. Each classifier (sLDA, linear SVM, radial SVM, and CNN) was able to detect cell density of 3700 cells µL-1 and above. These results suggest that DMS combined with laser desorption can detect low densities of breast cancer cells, at levels clinically relevant for margin detection, from Myogel samples in vitro.

Laser desorption tissue imaging with Differential Mobility Spectrometry.

Pathological gross examination of breast carcinoma samples is sometimes laborious. A tissue pre-mapping method could indicate neoplastic areas to the pathologist and enable focused sampling. Differential Mobility Spectrometry (DMS) is a rapid and affordable technology for complex gas mixture analysis. We present an automated tissue laser analysis system for imaging approaches (iATLAS), which utilizes a computer-controlled laser evaporator unit coupled with a DMS gas analyzer. The system is demonstrated in the classification of porcine tissue samples and three human breast carcinomas. Tissue samples from eighteen landrace pigs were classified with the system based on a pre-designed matrix (spatial resolution 1-3 mm). The smoke samples were analyzed with DMS, and tissue classification was performed with several machine learning approaches. Porcine skeletal muscle (n = 1030), adipose tissue (n = 1329), normal breast tissue (n = 258), bone (n = 680), and liver (n = 264) were identified with 86% cross-validation (CV) accuracy with a convolutional neural network (CNN) model. Further, a panel tissue that comprised all five tissue types was applied as an independent validation dataset. In this test, 82% classification accuracy with CNN was achieved. An analogous procedure was applied to demonstrate the feasibility of iATLAS in breast cancer imaging according to 1) macroscopically and 2) microscopically annotated data with 10-fold CV and SVM (radial kernel). We reached a classification accuracy of 94%, specificity of 94%, and sensitivity of 93% with the macroscopically annotated data from three breast cancer specimens. The microscopic annotation was applicable to two specimens. For the first specimen, the classification accuracy was 84% (specificity 88% and sensitivity 77%). For the second, the classification accuracy was 72% (specificity 88% and sensitivity 24%). This study presents a promising method for automated tissue imaging in an animal model and lays foundation for breast cancer imaging.

Differentiation of aspirated nasal air from room air using analysis with a differential mobility spectrometry-based electronic nose: a proof-of-concept study.

Over the last few decades, breath analysis using electronic nose (eNose) technology has become a topic of intense research, as it is both non-invasive and painless, and is suitable for point-of-care use. To date, however, only a few studies have examined nasal air. As the air in the oral cavity and the lungs differs from the air in the nasal cavity, it is unknown whether aspirated nasal air could be exploited with eNose technology. Compared to traditional eNoses, differential mobility spectrometry uses an alternating electrical field to discriminate the different molecules of gas mixtures, providing analogous information. This study reports the collection of nasal air by aspiration and the subsequent analysis of the collected air using a differential mobility spectrometer. We collected nasal air from ten volunteers into breath collecting bags and compared them to bags of room air and the air aspirated through the device. Distance and dissimilarity metrics between the sample types were calculated and statistical significance evaluated with Kolmogorov-Smirnov test. After leave-one-day-out cross-validation, a shrinkage linear discriminant classifier was able to correctly classify 100% of the samples. The nasal air differed (p< 0.05) from the other sample types. The results show the feasibility of collecting nasal air by aspiration and subsequent analysis using differential mobility spectrometry, and thus increases the potential of the method to be used in disease detection studies.

Predicting lecithin concentration from differential mobility spectrometry measurements with linear regression models and neural networks.

Differential mobility spectrometry (DMS) analysis of electrosurgical smoke can be used to distinguish cancerous and healthy tissues. Mass spectrometry studies of surgical smoke have revealed phospholipids as the key compounds enabling this discrimination. Lecithin is a mixture of phospholipids encountered in tissues. We hypothesized that DMS is capable of detecting and quantifying lecithin from water solution in headspace chamber, paving way for analysis of surgical smoke. We measured different lecithin concentrations in a biologically relevant range considering healthy and cancerous tissues with DMS and trained regression models to predict the analyte concentration. The models were internally cross-validated and externally validated. The best cross-validation results were obtained with convolutional neural networks, with root mean square error (RMSE) = 0.38 mg/ml. This is the first demonstration of estimation of analyte concentration from DMS measurements with neural networks. The best external validation results were acquired with sparse linear regression methods, with RMSE varying from 0.40 mg/ml to 0.41 mg/ml. The results demonstrate that DMS is sufficiently sensitive to detect biologically relevant changes in phospholipid concentration, potentially explaining its ability to detect cancerous tissue. In the future, we aim to reproduce the results by using surgical smoke as the medium. In this scenario, the complex background of surgical smoke will be the main challenge to overcome. Predicting concentration with neural networks also lays the foundation for wider analytical usage of DMS.