Faculty Bibliography

Activating receptor tyrosine kinase RET (rarranged during transfection) gene alterations have been identified as oncogenic in multiple malignancies. RET gene rearrangements retaining the kinase domain are oncogenic drivers in papillary thyroid cancer, non-small-cell lung cancer, and multiple other cancers. Activating RET mutations are associated with different phenotypes of multiple endocrine neoplasia type 2 as well as sporadic medullary thyroid cancer. RET is thus an attractive therapeutic target in patients with oncogenic RET alterations. Multikinase inhibitors with RET inhibitor activity, such as cabozantinib and vandetanib, have been explored in the clinic for tumors with activating RET gene alterations with modest clinical efficacy. As a result of the nonselective nature of these multikinase inhibitors, patients had off-target adverse effects, such as hypertension, rash, and diarrhea. This resulted in a narrow therapeutic index of these drugs, limiting ability to dose for clinically effective RET inhibition. In contrast, the recent discovery and clinical validation of highly potent selective RET inhibitors (pralsetinib, selpercatinib) demonstrating improved efficacy and a more favorable toxicity profile are poised to alter the landscape of RET-dependent cancers. These drugs appear to have broad activity across tumors with activating RET alterations. The mechanisms of resistance to these next-generation highly selective RET inhibitors is an area of active research. This review summarizes the current understanding of RET alterations and the state-of-the-art treatment strategies in RET-dependent cancers.

Despite substantial progress in lung cancer immunotherapy, the overall response rate in KRAS-mutant lung adenocarcinoma (ADC) patients remains low. Combining standard immunotherapy with adjuvant approaches that enhance adaptive immune responses-such as epigenetic modulation of anti-tumor immunity-is therefore an attractive strategy. To identify epigenetic regulators of tumor immunity, we constructed an epigenetic-focused sgRNA library, and performed an in vivo CRISPR screen in a KrasG12D/P53-/- (KP) lung ADC model. Our data showed that loss of the histone chaperone Asf1a in tumor cells sensitizes tumors to anti-PD-1 treatment. Mechanistic studies revealed that tumor cell-intrinsic Asf1a deficiency induced immunogenic macrophage differentiation in the tumor microenvironment by upregulating GM-CSF expression and potentiated T cell activation in combination with anti-PD-1. Our results provide rationale for a novel combination therapy consisting of ASF1A inhibition and anti-PD-1 immunotherapy.

Cyclin-dependent kinase 7 (CDK7) is a central regulator of the cell cycle and gene transcription. However, little is known about its impact on genomic instability and cancer immunity. Using a selective CDK7 inhibitor, YKL-5-124, we demonstrated that CDK7 inhibition predominately disrupts cell-cycle progression and induces DNA replication stress and genome instability in small cell lung cancer (SCLC) while simultaneously triggering immune-response signaling. These tumor-intrinsic events provoke a robust immune surveillance program elicited by T cells, which is further enhanced by the addition of immune-checkpoint blockade. Combining YKL-5-124 with anti-PD-1 offers significant survival benefit in multiple highly aggressive murine models of SCLC, providing a rationale for new combination regimens consisting of CDK7 inhibitors and immunotherapies.

No predictive biomarkers can robustly identify non-small cell lung cancer (NSCLC) patients who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a machine learning setting, we compared changes ("delta") in the radiomic texture (DelRADx) of computed tomography (CT) patterns both within and outside tumor nodules before and after 2-3 cycles of ICI therapy. We found that DelRADx patterns could predict response to ICI therapy and overall survival (OS) for patients with NSCLC. We retrospectively analyzed data acquired from 139 NSCLC patients at two institutions, who were divided into a discovery set (D1 = 50) and two independent validation sets (D2 = 62, D3 = 27). Intranodular and perinodular texture descriptors were extracted and the relative differences were computed. A linear discriminant analysis (LDA) classifier was trained with 8 DelRADx features to predict RECIST (response evaluation criteria in solid tumors)-derived response. Association of delta-radiomic risk-score (DRS) with OS was determined. The association of DelRADx features with tumor-infiltrating lymphocyte (TIL) density on the diagnostic biopsies (n = 36) was also evaluated. The LDA classifier yielded an area under the curve (AUC) of 0.88 ± 0.08 in distinguishing responders from nonresponders in D1, 0.85 and 0.81 in D2 and D3. DRS was associated with OS (hazard ratio: 1.64, 95% CI: 1.22 - 2.21, P = 0.0011, C-Index = 0.72). Peritumoral Gabor features were associated with the density of TILs on diagnostic biopsy samples. Our results show that DelRADx could be used to identify early functional responses in NSCLC patients.

Aim: To determine real-world outcomes with first-line pembrolizumab monotherapy for metastatic non-small-cell lung cancer with PD-L1 tumor expression ≥50%. Methods: This retrospective study included adults with ECOG 0-1 initiating first-line pembrolizumab monotherapy on/after 24 October 2016 (EHR cohort) or from 1 December 2016 through 30 November 2017 (spotlight cohort) with ≥6-month follow-up. We estimated Kaplan-Meier overall survival (OS, both cohorts), and, for spotlight, real-world progression-free survival (rwPFS) by Kaplan-Meier and real-world tumor response (rwTR). Results: For 423 patients in the EHR cohort and 188 in spotlight, median OS was 18.9 months (95% CI: 14.9-25.5) and 19.1 months (12.6-not reached), respectively. For spotlight, median rwPFS was 6.8 months (5.3-8.1); rwTR of complete/partial response was 48% (41-56%). Conclusion: Observed OS, rwPFS and rwTR were consistent with clinical trial findings.

In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in digital pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for digital pathology, focusing on deep neural networks and 'hand-crafted' feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Background: Early-Stage non-small cell lung cancer (ES-NSCLC) accounts for approximately 40% of NSCLC cases, with 5-year survival rates varying between 31-49%. Radiomic textural features from pre-treatment CT scans and QH features from H&E stained WSIs have been shown to be independently prognostic of outcome. With diagnostic CT scans and surgical resection, the standard of care in ES-NSCLC, in this work we seek to take a multimodality approach using routine imaging to improve the predictive performance in determining DFS following resection.
Method(s): A retrospective chart review of Stage I and II (ES-NSCLC) pts undergoing surgical resection between 2005-14 with available CT and resected tissue yielded 70 pts. A total of 248 radiomic CT textural features from inside the tumor (Intratumoral -IT) and outside the tumor (Peritumoral - PT) and 242 QH features related to the nuclear shape, texture and spatial orientation and architecture from H&E WSI were extracted. We developed two risk models, Radiomic and QH using the most stable, discriminative and uncorrelated features from CT and WSI respectively determined by Lasso-regularized Cox regression to predict Disease free survival (DFS). Model performances were analyzed using Hazard Ratios (HR), Concordance Index (C-index) and Decision curve analysis. We built a nomogram to calculate the DFS based around the individual models as well as an integration of the QH and Radiomic models.
Result(s): Top 6 Radiomic features included 2 IT and 4 PT features from the Haralick and Collage families. The QH model comprised 6 nuclear shape and graph features. In predicting DFS, While the Radiomic model had a HR of 2.4 (p <0.01) with C-index - 0.67, the QH model had

Background: Early stage non-small cell lung cancer (ES-NSCLC) comprises about 45% of all NSCLC patients, with 5-year survival ranging between 30-49%. Surgical resection is the standard of care curative modality in these patients but about 30-55% of patients often recur following surgery within the first 3 years. There is currently no validated method to stratify patients based on their risk of recurrence following surgery in these patients. In this project, we develop and validate a nomogram using a combination of CT-derived radiomic textural features and clinco-pathologic factors, in order to predict DFS in ES-NSCLC.
Method(s): This study comprised 350 ES-NSCLC patients from two different institutions who underwent surgery (75 patients relapsed). Radiomic textural features were extracted from tumor region (Intratumoral - IT) as well as from the annular ring shaped peritumoral region (PT) with 3mm as a ring thickness and extending 9 mm outside the nodule. A total of 124 features from Gabor, Laws, Laplace, Haralick and Collage feature families were extracted from IT and each PT ring for all patients. The most stable, significant and uncorrelated features were selected from D1 (N=221) and used to build a Lasso-regularized multivariate Cox-regression model to generate a Radiomic Risk Score (RRS) derived from weighted Lasso coefficients. Further, RRS was integrated with clinic-pathologic variables (Lympho-vascular invasion LVI and AJCC stage) which were independently predictive on DFS in multivariate analysis to build a clinical-radiomics score (CRS). A nomogram was constructed to visually assess the CRS and RRS on DFS. Performances were evaluated using hazard ratios (HR), concordance index (C-Index) along with decision and calibration curves to show the differences between the individual and integrated risk scores.
Result(s): Top 14 radiomic features included 6 from IT and 8 from 0-9 mm PT distance. The constructed RRS could predict DFS (n=221, C-index=0.69, HR = 3.8; 95% CI- 2.7-5.6, p<0.05) on training (D1) and (n=129, C-index=0.69,

Background: No targeted therapy is currently approved for patients with RET fusion-positive non-small cell lung cancer (NSCLC). LOXO-292 is a highly selective RET inhibitor with activity against diverse RET fusions, activating RET mutations and brain metastases. Based on initial data from LIBRETTO-001, LOXO-292 received FDA Breakthrough Designation for the treatment of RET fusion-positive NSCLC in August 2018.
Method(s): This global phase 1/2 study (87 sites, 15 countries) enrolled patients with advanced RET-altered solid tumors including RET fusion-positive NSCLC (NCT03157128). LOXO-292 was dosed orally in 28-day cycles. The phase 1 portion established the MTD/RP2D (160 mg BID). The phase 2 portion enrolled patients to one of six cohorts based on tumor type, RET alteration, and prior therapies. The primary endpoint was ORR (RECIST 1.1). Secondary endpoints included DoR, CNS ORR, CNS DoR, PFS, OS, safety and PK.
Result(s): As of 17-June 2019, 247 RET fusion-positive NSCLC patients were treated. The primary analysis set (PAS) for LOXO-292 registration, as defined with the US FDA, consists of the first 105 consecutively enrolled RET fusion-positive NSCLC patients who received prior platinum-based chemotherapy; 54 patients (51%) also received prior immune checkpoint inhibitors (ICIs). The majority of PAS responders have been followed for >=6 months from first response. Of the remaining 142 patients, 74 previously treated with platinum-based chemotherapy have not had sufficient follow-up, 56 did not receive prior platinum-based chemotherapy and 12 did not have measurable disease at baseline. Among PAS patients, the investigator-assessed ORR was 70% (95% CI 60-78%, n=73/105, 3 PRs pending confirmation). Responses did not differ by fusion partner or the type or number of prior therapies, including chemotherapy, ICIs and multikinase inhibitors with anti-RET activity. The median DoR was 20.3 months (95% CI 16.6-NR) with a median follow-up of 7.5 months (range 1.9-21.1 months); as evidenced by the wide confidence interval, this DoR estimate is not statistically stable due to a low number of events (12 of 70 confirmed responders). The intracranial ORR was 90% (n=9/10: 2 confirmed CRs, 7 confirmed PRs) for patients with measurable brain metastases at baseline. The ORR in evaluable treatment naive RET fusion-positive NSCLC patients was 88% (95% CI 72-97%, n=29/33, 10 PRs pending confirmation). In the safety data set of all 247 patients, 5 treatment-related AEs occurred in >=15% of patients: dry mouth, AST increased, diarrhea, ALT increased, and hypertension. Most AEs were grade 1-2. Only 3 of 247 (1.2%) NSCLC patients discontinued LOXO-292 for treatment-related AEs. Updated data will be presented at the meeting.
Conclusion(s): LOXO-292 had marked antitumor activity in RET fusion-positive NSCLC patients and was well tolerated. These data will form the basis of an FDA NDA submission later this year. Keywords: RET fusion, selective RET inhibitor, NSCLC
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