Genomic and Immunophenotypic Landscape of Acquired Resistance to PD-(L)1 Blockade in Non-Small-Cell Lung Cancer.

PURPOSE: Although immune checkpoint inhibitors (ICI) have extended survival in patients with non-small-cell lung cancer (NSCLC), acquired resistance (AR) to ICI frequently develops after an initial benefit. However, the mechanisms of AR to ICI in NSCLC are largely unknown. METHODS: Comprehensive tumor genomic profiling, machine learning-based assessment of tumor-infiltrating lymphocytes, multiplexed immunofluorescence, and/or HLA-I immunohistochemistry (IHC) were performed on matched pre- and post-ICI tumor biopsies from patients with NSCLC treated with ICI at the Dana-Farber Cancer Institute who developed AR to ICI. Two additional cohorts of patients with intervening chemotherapy or targeted therapies between biopsies were included as controls. RESULTS: We performed comprehensive genomic profiling and immunophenotypic characterization on samples from 82 patients with NSCLC and matched pre- and post-ICI biopsies and compared findings with a control cohort of patients with non-ICI intervening therapies between biopsies (chemotherapy, N = 32; targeted therapies, N = 89; both, N = 17). Putative resistance mutations were identified in 27.8% of immunotherapy-treated cases and included acquired loss-of-function mutations in STK11, B2M, APC, MTOR, KEAP1, and JAK1/2; these acquired alterations were not observed in the control groups. Immunophenotyping of matched pre- and post-ICI samples demonstrated significant decreases in intratumoral lymphocytes, CD3e+ and CD8a+ T cells, and PD-L1-PD1 engagement, as well as increased distance between tumor cells and CD8+PD-1+ T cells. There was a significant decrease in HLA class I expression in the immunotherapy cohort at the time of AR compared with the chemotherapy (P = .005) and the targeted therapy (P = .01) cohorts. CONCLUSION: These findings highlight the genomic and immunophenotypic heterogeneity of ICI resistance in NSCLC, which will need to be considered when developing novel therapeutic strategies aimed at overcoming resistance.

Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states.

Clear cell renal cell carcinoma (ccRCC) is molecularly heterogeneous, immune infiltrated, and selectively sensitive to immune checkpoint inhibition (ICI). However, the joint tumor-immune states that mediate ICI response remain elusive. We develop spatially aware deep-learning models of tumor and immune features to learn representations of ccRCC tumors using diagnostic whole-slide images (WSIs) in untreated and treated contexts (n = 1,102 patients). We identify patterns of grade heterogeneity in WSIs not achievable through human pathologist analysis, and these graph-based "microheterogeneity" structures associate with PBRM1 loss of function and with patient outcomes. Joint analysis of tumor phenotypes and immune infiltration identifies a subpopulation of highly infiltrated, microheterogeneous tumors responsive to ICI. In paired multiplex immunofluorescence images of ccRCC, microheterogeneity associates with greater PD1 activation in CD8+ lymphocytes and increased tumor-immune interactions. Our work reveals spatially interacting tumor-immune structures underlying ccRCC biology that may also inform selective response to ICI.

PI-RADS 3 score: A retrospective experience of clinically significant prostate cancer detection.

Rationale and objectives: The study aims to propose an optimal workflow in patients with a PI-RADS 3 (PR-3) assessment category (AC) through determining the timing and type of pathology interrogation used for the detection of clinically significant prostate cancer (csPCa) in these men based upon a 5-year retrospective review in a large academic medical center. Materials and methods: This United States Health Insurance Probability and Accountability Act (HIPAA)-compliant, institutional review board-approved retrospective study included men without prior csPCa diagnosis who received PR-3 AC on magnetic resonance (MR) imaging (MRI). Subsequent incidence and time to csPCa diagnosis and number/type of prostate interventions was recorded. Categorical data were compared using Fisher's exact test and continuous data using ANOVA omnibus F-test. Results: Our cohort of 3238 men identified 332 who received PR-3 as their highest AC on MRI, 240 (72.3%) of whom had pathology follow-up within 5 years. csPCa was detected in 76/240 (32%) and non-csPCa in 109/240 (45%) within 9.0 ± 10.6 months. Using a non-targeted trans-rectal ultrasound biopsy as the initial approach (n = 55), another diagnostic procedure was required to diagnose csPCa in 42/55 (76.4%) of men, compared with 3/21(14.3%) men with an initial MR targeted-biopsy approach (n = 21); (p < 0.0001). Those with csPCa had higher median serum prostate-specific antigen (PSA) and PSA density, and lower median prostate volume (p < 0.003) compared with non-csPCa/no PCa. Conclusion: Most patients with PR-3 AC underwent prostate pathology exams within 5 years, 32% of whom were found to have csPCa within 1 year of MRI, most often with a higher PSA density and a prior non-csPCa diagnosis. Addition of a targeted biopsy approach initially reduced the need for a second biopsy to reach a for csPCa diagnosis. Thus, a combination of systematic and targeted biopsy is advised in men with PR-3 and a co-existing abnormal PSA and PSA density.

A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories.

Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer.

Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states.

Clear cell renal cell carcinoma (ccRCC) is molecularly heterogeneous, immune infiltrated, and selectively sensitive to immune checkpoint inhibition (ICI). Established histopathology paradigms like nuclear grade have baseline prognostic relevance for ccRCC, although whether existing or novel histologic features encode additional heterogeneous biological and clinical states in ccRCC is uncertain. Here, we developed spatially aware deep learning models of tumor- and immune-related features to learn representations of ccRCC tumors using diagnostic whole-slide images (WSI) in untreated and treated contexts (n = 1102 patients). We discovered patterns of nuclear grade heterogeneity in WSI not achievable through human pathologist analysis, and these graph-based "microheterogeneity" structures associated with PBRM1 loss of function, adverse clinical factors, and selective patient response to ICI. Joint computer vision analysis of tumor phenotypes with inferred tumor infiltrating lymphocyte density identified a further subpopulation of highly infiltrated, microheterogeneous tumors responsive to ICI. In paired multiplex immunofluorescence images of ccRCC, microheterogeneity associated with greater PD1 activation in CD8+ lymphocytes and increased tumor-immune interactions. Thus, our work reveals novel spatially interacting tumor-immune structures underlying ccRCC biology that can also inform selective response to ICI.

Federated benchmarking of medical artificial intelligence with MedPerf.

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.

Circulating U13 Small Nucleolar RNA as a Potential Biomarker in Huntington's Disease: A Pilot Study.

Plasma small RNAs have been recently explored as biomarkers in Huntington's disease (HD). We performed an exploratory study on nine HD patients, eight healthy subjects (HS), and five psychiatric patients (PP; to control for iatrogenic confounder effects) through an Affymetrix-Gene-Chip-miRNA-Array. We validated the results in an independent population of 23 HD, 15 pre-HD, 24 PP, 28 Alzheimer's disease (AD) patients (to control the disease-specificity) and 22 HS through real-time PCR. The microarray results showed higher levels of U13 small nucleolar RNA (SNORD13) in HD patients than controls (fold change 1.54, p = 0.003 HD vs. HS, and 1.44, p = 0.0026 HD vs. PP). In the validation population, a significant increase emerged with respect to both pre-HD and the control groups (p < 0.0001). SNORD13 correlated with the status of the mutant huntingtin carrier (r = 0.73; p < 0.001) and the disease duration (r = 0.59; p = 0.003). The receiver operating characteristic (ROC) curve analysis showed the high accuracy of SNORD13 in discriminating HD patients from other groups (AUC = 0.963). An interactome and pathway analysis on SNORD13 revealed enrichments for factors relevant to HD pathogenesis. We report the unprecedented finding of a potential disease-specific role of SNORD13 in HD. It seems to peripherally report a 'tipping point' in the pathogenic cascade at the neuronal level.

Association of High Tumor Mutation Burden in Non-Small Cell Lung Cancers With Increased Immune Infiltration and Improved Clinical Outcomes of PD-L1 Blockade Across PD-L1 Expression Levels.

Importance: Although tumor mutation burden (TMB) has been explored as a potential biomarker of immunotherapy efficacy in solid tumors, there still is a lack of consensus about the optimal TMB threshold that best discriminates improved outcomes of immune checkpoint inhibitor therapy among patients with non-small cell lung cancer (NSCLC). Objectives: To determine the association between increasing TMB levels and immunotherapy efficacy across clinically relevant programmed death ligand-1 (PD-L1) levels in patients with NSCLC. Design, Setting, and Participants: This multicenter cohort study included patients with advanced NSCLC treated with immunotherapy who received programmed cell death-1 (PD-1) or PD-L1 inhibition in the Dana-Farber Cancer Institute (DFCI), Memorial Sloan Kettering Cancer Center (MSKCC), and in the Stand Up To Cancer (SU2C)/Mark Foundation data sets. Clinicopathological and genomic data were collected from patients between September 2013 and September 2020. Data analysis was performed from November 2021 to February 2022. Exposures: Treatment with PD-1/PD-L1 inhibition without chemotherapy. Main Outcomes and Measures: Association of TMB levels with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Results: In the entire cohort of 1552 patients with advanced NSCLC who received PD-1/PD-L1 blockade, the median (range) age was 66 (22-92) years, 830 (53.5%) were women, and 1347 (86.8%) had cancer with nonsquamous histologic profile. A regression tree modeling ORR as a function of TMB identified 2 TMB groupings in the discovery cohort (MSKCC), defined as low TMB (≤19.0 mutations per megabase) and high TMB (>19.0 mutations per megabase), which were associated with increasing improvements in ORR, PFS, and OS in the discovery cohort and in 2 independent cohorts (DFCI and SU2C/Mark Foundation). These TMB levels also were associated with significant improvements in outcomes of immunotherapy in each PD-L1 tumor proportion score subgroup of less than 1%, 1% to 49%, and 50% or higher. The ORR to PD-1/PD-L1 inhibition was as high as 57% in patients with high TMB and PD-L1 expression 50% or higher and as low as 8.7% in patients with low TMB and PD-L1 expression less than 1%. Multiplexed immunofluorescence and transcriptomic profiling revealed that high TMB levels were associated with increased CD8-positive, PD-L1-positive T-cell infiltration, increased PD-L1 expression on tumor and immune cells, and upregulation of innate and adaptive immune response signatures. Conclusions and Relevance: These findings suggest that increasing TMB levels are associated with immune cell infiltration and an inflammatory T-cell-mediated response, resulting in increased sensitivity to PD-1/PD-L1 blockade in NSCLC across PD-L1 expression subgroups.

Associations Between Family Member Involvement and Outcomes of Patients Admitted to the Intensive Care Unit: Retrospective Cohort Study.

BACKGROUND: Little is known about family member involvement, by relationship status, for patients treated in the intensive care unit (ICU). OBJECTIVE: Using documentation of family interactions in clinical notes, we examined associations between child and spousal involvement and ICU patient outcomes, including goals of care conversations (GOCCs), limitations in life-sustaining therapy (LLST), and 3-month mortality. METHODS: Using a retrospective cohort design, the study included a total of 858 adult patients treated between 2008 and 2012 in the medical ICU at a tertiary care center in northeastern United States. Clinical notes generated within the first 48 hours of admission to the ICU were used with standard machine learning methods to predict patient outcomes. We used natural language processing methods to identify family-related documentation and abstracted sociodemographic and clinical characteristics of the patients from the medical record. RESULTS: Most of the 858 patients were White (n=650, 75.8%); 437 (50.9%) were male, 479 (55.8%) were married, and the median age was 68.4 (IQR 56.5-79.4) years. Most patients had documented GOCC (n=651, 75.9%). In adjusted regression analyses, child involvement (odds ratio [OR] 0.81; 95% CI 0.49-1.34; P=.41) and child plus spouse involvement (OR 1.28; 95% CI 0.8-2.03; P=.3) were not associated with GOCCs compared to spouse involvement. Child involvement was not associated with LLST when compared to spouse involvement (OR 1.49; 95% CI 0.89-2.52; P=.13). However, child plus spouse involvement was associated with LLST (OR 1.6; 95% CI 1.02-2.52; P=.04). Compared to spouse involvement, there were no significant differences in the 3-month mortality by family member type, including child plus spouse involvement (OR 1.38; 95% CI 0.91-2.09; P=.13) and child involvement (OR 1.47; 95% CI 0.9-2.41; P=.12). CONCLUSIONS: Our findings demonstrate that statistical models derived from text analysis in the first 48 hours of ICU admission can predict patient outcomes. Early child plus spouse involvement was associated with LLST, suggesting that decisions about LLST were more likely to occur when the child and spouse were both involved compared to the involvement of only the spouse. More research is needed to further understand the involvement of different family members in ICU care and its association with patient outcomes.

Deep Learning for Cancer Symptoms Monitoring on the Basis of Electronic Health Record Unstructured Clinical Notes.

PURPOSE: Symptoms are vital outcomes for cancer clinical trials, observational research, and population-level surveillance. Patient-reported outcomes (PROs) are valuable for monitoring symptoms, yet there are many challenges to collecting PROs at scale. We sought to develop, test, and externally validate a deep learning model to extract symptoms from unstructured clinical notes in the electronic health record. METHODS: We randomly selected 1,225 outpatient progress notes from among patients treated at the Dana-Farber Cancer Institute between January 2016 and December 2019 and used 1,125 notes as our training/validation data set and 100 notes as our test data set. We evaluated the performance of 10 deep learning models for detecting 80 symptoms included in the National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) framework. Model performance as compared with manual chart abstraction was assessed using standard metrics, and the highest performer was externally validated on a sample of 100 physician notes from a different clinical context. RESULTS: In our training and test data sets, 75 of the 80 candidate symptoms were identified. The ELECTRA-small model had the highest performance for symptom identification at the token level (ie, at the individual symptom level), with an F1 of 0.87 and a processing time of 3.95 seconds per note. For the 10 most common symptoms in the test data set, the F1 score ranged from 0.98 for anxious to 0.86 for fatigue. For external validation of the same symptoms, the note-level performance ranged from F1 = 0.97 for diarrhea and dizziness to F1 = 0.73 for swelling. CONCLUSION: Training a deep learning model to identify a wide range of electronic health record-documented symptoms relevant to cancer care is feasible. This approach could be used at the health system scale to complement to electronic PROs.

Multiple sclerosis genetic and non-genetic factors interact through the transient transcriptome.

A clinically actionable understanding of multiple sclerosis (MS) etiology goes through GWAS interpretation, prompting research on new gene regulatory models. Our previous investigations suggested heterogeneity in etiology components and stochasticity in the interaction between genetic and non-genetic factors. To find a unifying model for this evidence, we focused on the recently mapped transient transcriptome (TT), that is mostly coded by intergenic and intronic regions, with half-life of minutes. Through a colocalization analysis, here we demonstrate that genomic regions coding for the TT are significantly enriched for MS-associated GWAS variants and DNA binding sites for molecular transducers mediating putative, non-genetic, determinants of MS (vitamin D deficiency, Epstein Barr virus latent infection, B cell dysfunction), indicating TT-coding regions as MS etiopathogenetic hotspots. Future research comparing cell-specific transient and stable transcriptomes may clarify the interplay between genetic variability and non-genetic factors causing MS. To this purpose, our colocalization analysis provides a freely available data resource at www.mscoloc.com .

Identification and Management of Pathogenic Variants in BRCA1, BRCA2, and PALB2 in a Tumor-Only Genomic Testing Program.

PURPOSE: Tumor-only genomic testing can uncover somatic and germline pathogenic variants [pathogenic/likely pathogenic (P/LP)] in cancer predisposition genes. We describe the prevalence of P/LPs in BRCA1/2 and PALB2 (B1B2P2) across malignancies and the frequency of clinical germline testing (CGT) in patients with P/LPs in B1B2P2 identified on tumor-only testing. EXPERIMENTAL DESIGN: Among 7,575 patients with cancer tested between 2016 and 2018 with the OncoPanel tumor-only sequencing assay, we characterized P/LP frequencies by tumor type, receipt of CGT prior to or within 12 months after OncoPanel, and factors associated with CGT. RESULTS: 272 (3.6%) patients had OncoPanel-detected P/LPs in B1B2P2: 37.5% of P/LPs were in BRCA-related cancers; the remainder were in non-BRCA tumors. P/LPs were detected in ≥5% of breast, pancreatic, prostate, ovarian, nonmelanoma skin, endometrial, small cell lung, and colorectal cancers. 37.9% of patients with P/LPs received CGT prior to OncoPanel; an additional 10.7% underwent CGT within 12 months of OncoPanel. Among 132 with CGT, 88.6% had ≥1 clinical factor for CGT compared with 47.1% who did not undergo CGT. Patients with BRCA tumors were more likely to have CGT compared with those without (81.4% vs. 29.0%, P < 0.0001). Among patients with CGT, 70.5% (93/132) of P/LPs were germline. CONCLUSIONS: Tumor-only genomic testing identified P/LPs in B1B2P2 in 3.6% of patients. 52.9% of patients with tumor-detected P/LPs and without CGT did not meet personal or family history criteria for CGT. In addition, some patients with tumor-detected P/LPs were not referred for CGT, especially those with non-BRCA tumors. Given implications for treatment selection and familial cancer risk, processes to reliably trigger CGT from tumor-genomic findings are needed.

Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology.

Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use cases. PathML is publicly available at www.pathml.com.

Circulating hsa-miR-323b-3p in Huntington's Disease: A Pilot Study.

The momentum of gene therapy in Huntington's disease (HD) deserves biomarkers from easily accessible fluid. We planned a study to verify whether plasma miRNome may provide useful peripheral "reporter(s)" for the management of HD patients. We performed an exploratory microarray study of whole non-coding RNA profiles in plasma from nine patients with HD and 13 matched controls [eight healthy subjects (HS) and five psychiatric patients (PP) to minimize possible iatrogenic impact on the profile of non-coding RNAs]. We found an HD-specific signature: downregulation of hsa-miR-98 (fold change, -1.5, p = 0.0338 HD vs. HS, and fold change, 1.5, p = 0.0045 HD vs. PP) and upregulation of hsa-miR-323b-3p (fold change, 1.5, p = 0.0007 HD vs. HS, and fold change, 1.5, p = 0.0111 HD vs. PP). To validate this result in an independent cohort, we quantify by digital droplet PCR (ddPCR) the presence of the two microRNA in the plasma of 33 HD patients and 49 matched controls (25 HS and 24 PP patients). We were able to confirm that hsa-miR-323b-3p was upregulated in HD and premanifest HD vs. HS and PP: the median values (first-third quartile) were 4.1 (0.9-10.53) and 5.8 (1.9-10.70) vs. 0.69 (0.3-2.75) and 1.4 (0.78-2.70), respectively, p < 0.05. No significant difference was found for hsa-miR-98. To evaluate the biological plausibility of the hsa-miR-323b-3p as a component of the disease pathophysiology, we performed a bioinformatic analysis based on its targetome and the huntingtin (HTT) interactome. We found a statistically significant overconnectivity between the targetome of hsa-miR-323b-3p and the HTT interactome (p = 1.48e-08). Furthermore, there was a significant transcription regulation of the HTT interactome by the miR-323b-3p targetome (p = 0.02). The availability of handy, reproducible, and minimally invasive biomarkers coming from peripheral miRNome may be valuable to characterize the illness progression, to indicate new therapeutic targets, and to monitor the effect of disease-modifying treatments. Our data deserve further studies with larger sample size and longitudinal design.

Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum.

Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic profiling of tumor and of patient serum could identify biomarkers of aggressive disease and lead to the development of a less-invasive assay to perform active surveillance monitoring. Metabolomic profiling of prostate tissue and serum samples was performed. Metabolite levels and metabolite sets were compared across Gleason scores. Machine learning algorithms were trained and tuned to predict transformation or differentiation status from metabolite data. A total of 135 metabolites were significantly different (P adjusted < 0.05) in tumor versus normal tissue, and pathway analysis identified one sugar metabolism pathway (P adjusted = 0.03). Machine learning identified profiles that predicted tumor versus normal tissue (AUC of 0.82 ± 0.08). In tumor tissue, 25 metabolites were associated with Gleason score (unadjusted P < 0.05), 4 increased in high grade while the remainder were enriched in low grade. While pyroglutamine and 1,5-anhydroglucitol were correlated (0.73 and 0.72, respectively) between tissue and serum from the same patient, no metabolites were consistently associated with Gleason score in serum. Previously reported as well as novel metabolites with differing abundance were identified across tumor tissue. However, a "metabolite signature" for Gleason score was not obtained. This may be due to study design and analytic challenges that future studies should consider. IMPLICATIONS: Metabolic profiling can distinguish benign and neoplastic tissues. A novel unsupervised machine learning method can be utilized to achieve this distinction.

Automated machine learning: Review of the state-of-the-art and opportunities for healthcare.

OBJECTIVE: This work aims to provide a review of the existing literature in the field of automated machine learning (AutoML) to help healthcare professionals better utilize machine learning models "off-the-shelf" with limited data science expertise. We also identify the potential opportunities and barriers to using AutoML in healthcare, as well as existing applications of AutoML in healthcare. METHODS: Published papers, accompanied with code, describing work in the field of AutoML from both a computer science perspective or a biomedical informatics perspective were reviewed. We also provide a short summary of a series of AutoML challenges hosted by ChaLearn. RESULTS: A review of 101 papers in the field of AutoML revealed that these automated techniques can match or improve upon expert human performance in certain machine learning tasks, often in a shorter amount of time. The main limitation of AutoML at this point is the ability to get these systems to work efficiently on a large scale, i.e. beyond small- and medium-size retrospective datasets. DISCUSSION: The utilization of machine learning techniques has the demonstrated potential to improve health outcomes, cut healthcare costs, and advance clinical research. However, most hospitals are not currently deploying machine learning solutions. One reason for this is that health care professionals often lack the machine learning expertise that is necessary to build a successful model, deploy it in production, and integrate it with the clinical workflow. In order to make machine learning techniques easier to apply and to reduce the demand for human experts, automated machine learning (AutoML) has emerged as a growing field that seeks to automatically select, compose, and parametrize machine learning models, so as to achieve optimal performance on a given task and/or dataset. CONCLUSION: While there have already been some use cases of AutoML in the healthcare field, more work needs to be done in order for there to be widespread adoption of AutoML in healthcare.

Impact of DNA Damage Response and Repair (DDR) Gene Mutations on Efficacy of PD-(L)1 Immune Checkpoint Inhibition in Non-Small Cell Lung Cancer.

PURPOSE: DNA damage response and repair (DDR) gene alterations are associated with increased tumor-infiltrating lymphocytes, higher genomic instability, and higher tumor mutational burden (TMB) in cancer. Whether DDR alterations are associated with clinical outcomes to programmed death ligand 1 [PD-(L)1] blockade in non-small cell lung cancer (NSCLC) is unknown. EXPERIMENTAL DESIGN: Tumors from patients treated with PD-(L)1 inhibitors were analyzed using targeted next-generation sequencing (NGS). Cancers were categorized on the basis of the presence or absence of deleterious mutations across a panel of 53 DDR genes. Clinical outcomes to PD-(L)1 inhibitors were evaluated according to DDR mutation status. RESULTS: Of 266 patients with successful NGS who received PD-(L)1 inhibitors, 132 (49.6%) were identified as having deleterious DDR mutations (DDR-positive). DDR-positive and DDR-negative groups were similar in terms of baseline clinicopathologic characteristics. The median TMB was significantly higher in the DDR-positive group compared with the DDR-negative group (12.1 vs. 7.6 mutations/megabase; P < 0.001). Compared with DDR-negative patients (N = 134), DDR-positive patients had a significantly higher objective response rate (30.3% vs. 17.2%; P = 0.01), longer median progression-free survival [PFS; 5.4 vs. 2.2 months; HR, 0.58 (95% confidence interval (CI), 0.45-0.76); P < 0.001], and longer median overall survival [OS; 18.8 vs. 9.9 months; HR, 0.57 (95% CI, 0.42-0.77); P < 0.001] with PD-(L)1 therapy. After adjusting for PD-L1, TMB, performance status, tobacco use, and line of therapy, DDR-positive status was associated with a significantly longer PFS [HR, 0.68 (95% CI, 0.51-0.92); P = 0.01] and OS [HR, 0.60 (95% CI, 0.43-0.85); P = 0.004] in multivariate analysis. CONCLUSIONS: Deleterious DDR mutations are frequent in NSCLC and are associated with improved clinical outcomes in patients with NSCLC treated with PD-(L)1 blockade.

Molecular Mechanisms of Acquired Resistance to MET Tyrosine Kinase Inhibitors in Patients with MET Exon 14-Mutant NSCLC.

PURPOSE: Molecular mechanisms of acquired resistance to MET tyrosine kinase inhibitors (TKI) are poorly understood. We aimed to characterize the genomic mechanisms of resistance to type I and type II MET TKIs and their impact on sequential MET TKI therapy outcomes in patients with metastatic MET exon 14-mutant NSCLC. EXPERIMENTAL DESIGN: Genomic alterations occurring at the time of progression on MET TKIs were studied using plasma and tissue next-generation sequencing (NGS). RESULTS: A total of 20 patients had tissue or plasma available for analysis at the time of acquired resistance to a MET TKI. Genomic alterations known or suspected to be mechanisms of resistance were detected in 15 patients (75%). On-target acquired mechanisms of resistance, including single and polyclonal MET kinase domain mutations in codons H1094, G1163, L1195, D1228, Y1230, and high levels of amplification of the MET exon 14-mutant allele, were observed in 7 patients (35%). A number of off-target mechanisms of resistance were detected in 9 patients (45%), including KRAS mutations and amplifications in KRAS, EGFR, HER3, and BRAF; one case displayed both on- and off-target mechanisms of resistance. In 2 patients with on-target resistant mutations, switching between type I and type II MET TKIs resulted in second partial responses. CONCLUSIONS: On-target secondary mutations and activation of bypass signaling drive resistance to MET TKIs. A deeper understanding of these molecular mechanisms can support the development of sequential or combinatorial therapeutic strategies to overcome resistance.

Tumor Mutational Burden and PTEN Alterations as Molecular Correlates of Response to PD-1/L1 Blockade in Metastatic Triple-Negative Breast Cancer.

PURPOSE: Few patients with metastatic triple-negative breast cancer (mTNBC) benefit from immune checkpoint inhibitors (ICI). On the basis of immunotherapy response correlates in other cancers, we evaluated whether high tumor mutational burden (TMB) ≥10 nonsynonymous mutations/megabase and PTEN alterations, defined as nonsynonymous mutations or 1 or 2 copy deletions, were associated with clinical benefit to anti-PD-1/L1 therapy in mTNBC. EXPERIMENTAL DESIGN: We identified patients with mTNBC, who consented to targeted DNA sequencing and were treated with ICIs on clinical trials between April 2014 and January 2019 at Dana-Farber Cancer Institute (Boston, MA). Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were correlated with tumor genomic features. RESULTS: Sixty-two women received anti-PD-1/L1 inhibitors alone (23%) or combined with targeted therapy (19%) or chemotherapy (58%). High TMB (18%) was associated with significantly longer PFS (12.5 vs. 3.7 months; P = 0.04), while PTEN alterations (29%) were associated with significantly lower ORR (6% vs. 48%; P = 0.01), shorter PFS (2.3 vs. 6.1 months; P = 0.01), and shorter OS (9.7 vs. 20.5 months; P = 0.02). Multivariate analyses confirmed that these associations were independent of performance status, prior lines of therapy, therapy regimen, and visceral metastases. The survival associations were additionally independent of PD-L1 in patients with known PD-L1 and were not found in mTNBC cohorts treated with chemotherapy (n = 90) and non-ICI regimens (n = 169). CONCLUSIONS: Among patients with mTNBC treated with anti-PD-1/L1 therapies, high TMB and PTEN alterations were associated with longer and shorter survival, respectively. These observations warrant validation in larger datasets.