COVID-19 and Cancer: A Comparison of the Two Important Pandemic Waves in an Indian Cancer Patients' Cohort.

Ullas BatraIt is well known that patients with cancer are at an increased risk of severe COVID-19. There are no reports that depict the differences in outcomes in cancer patients between the two waves of the pandemic. This is a real-world experience aimed at characterizing the differences in demographics, clinical features, treatment details, and outcomes in COVID-19-positive cancer patients between the two pandemic waves. This was a prospective study of all COVID-19-positive cancer patients attending our specialty out-patient department at Rajiv Gandhi Cancer Institute and Research Centre between March 2020 and November 2020 (1st wave) and April 2021 and June 2021 (second wave). All patients diagnosed to have COVID-19 by real-time polymerase chain reaction (RT-PCR) with a biopsy-proven solid organ malignancy attending the medical oncology out-patient department were included during both the waves. A total of 300 patients with proven SARS-CoV-2 infection by either RT-PCR or cartridge based nucleic acid amplification test were encountered, of which 123 were encountered during the first wave of the pandemic and 177 during the second wave. The case fatality rate of the first wave was 9.8%, with a 15-day case fatality rate of 5.6%, whereas for the second wave, it was 13% and 7.2%, respectively. Twelve patients succumbed to COVID-19 disease in the first wave and 23 succumbed in the second. There were no statistically significant correlations; however, the death in the second wave tended to occur more in younger male patients, with comorbidities and history of smoking. There was no relation with ongoing cancer-directed treatment or chemotherapy. Our study is unique in comparing characteristics of the two most important COVID-19 waves and treatment patterns in cancer patients from a single center. The second wave showed a higher CFR, hospital admission rate, and higher frequency of respiratory complications; however, there was no relation to cancer-directed therapy and COVID-19, thus reiterating the fact that cancer treatment should not be halted in the event of a COVID-19 infection.

AI-based pipeline for early screening of lung cancer: integrating radiology, clinical, and genomics data.

Background: The prognosis of lung carcinoma has changed since the discovery of molecular targets and their specific drugs. Somatic Epidermal Growth Factor Receptor (EGFR) mutations have been reported in lung carcinoma, and these mutant proteins act as substrates for targeted therapies. However, in a resource-constrained country like India, panel-based next-generation sequencing cannot be made available to the population at large. Additional challenges such as adequacy of tissue in case of lung core biopsies and locating suitable tumour tissues as a result of innate intratumoral heterogeneity indicate the necessity of an AI-based end-to-end pipeline capable of automatically detecting and learning more effective lung nodule features from CT images and predicting the probability of the EGFR-mutant. This will help the oncologists and patients in resource-limited settings to achieve near-optimal care and appropriate therapy. Methods: The EGFR gene sequencing and CT imaging data of 2277 patients with lung carcinoma were included from three cohorts in India and a White population cohort collected from TCIA. Another cohort LIDC-IDRI was used to train the AIPS-Nodule (AIPS-N) model for automatic detection and characterisation of lung nodules. We explored the value of combining the results of the AIPS-N with the clinical factors in the AIPS-Mutation (AIPS-M) model for predicting EGFR genotype, and it was evaluated by area under the curve (AUC). Findings: AIPS-N achieved an average AP50 of 70.19% in detecting the location of nodules within the lung region of interest during validation and predicted the score of five lung nodule properties. The AIPS-M machine learning (ML) and deep learning (DL) models achieved AUCs ranging from 0.587 to 0.910. Interpretation: The AIPS suggests that CT imaging combined with a fully automated lung-nodule analysis AI system can predict EGFR genotype and identify patients with an EGFR mutation in a cost-effective and non-invasive manner. Funding: This work was supported by a grant provided by Conquer Cancer Foundation of ASCO [2021IIG-5555960128] and Pfizer Products India Pvt. Ltd.

KRAS mutated Non-Small Lung Carcinoma: A Real World Context from the Indian subcontinent.

BACKGROUND: KRAS, although a common variant of occurrence (~20% of non-small-cell lung carcinoma [NSCLC]) has been untargetable, owing to the molecular structure which inherently prevents drug binding. KRAS mutations in NSCLC are associated with distinct clinical profiles including smokers and mucinous histology. KRAS G12C mutations account for ~40% KRAS altered NSCLC, but NSCLC being a geographically diverse disease, the features may be distinct in this part of the world. This is a single-center experience of KRAS-mutated NSCLC including clinical, imaging, pathologic features, and treatment patterns and outcomes. METHODS: This is a single-center retrospective study of KRAS-mutated NSCLC. The clinicopathological features and outcomes were retrieved and collated from the medical record archives of the hospital. RESULTS: Fifty (30.6%) patients with advanced-stage NSCLC with alterations in the KRAS gene were enrolled in the 163 patients who were tested for KRAS alterations. The median age was 61 years. Molecular detection revealed three main types of KRAS mutations viz-a-vis: G12C in 17 (34%), G12V in 9 (18%), and G12D in 6 (12%) patients. Comparing G12C versus the non-G12C mutated cases, co-mutations were common in the non-G12C subgroup (p < 0.05). Among the 36, who were treated at our center, all received chemotherapy as the first line with a median progression-free survival (PFS)of 5.4 months. The PFS of G12C was higher than the non-G12C subgroup (6.4 vs 3.8 months). CONCLUSION: This is the largest single-center experience from the Indian subcontinent for KRAS-mutated NSCLC with distinct clinical features. It highlights the unmet need for G12C inhibitors in our country, where prevalence is equivalent to the West.

Machine learning-based algorithm demonstrates differences in del19 and L858R EGFR subgroups in non-small cell lung cancer: a single center experience.

BACKGROUND: Exon del19 and L858R mutations account for 90% of EGFR mutant non-small cell lung cancer (NSCLC). LUX lung 3 and 6 initially reported a survival difference between these two. However, other studies did not demonstrate the same. By using machine learning (ML), it is possible to discover novel patterns for cancer susceptibility, recurrence, prognostication, and therapy. We evaluate the effect of these two molecular subtypes on overall survival/progression-free survival (OS/PFS). METHODS: 413 patients with stage IV EGFR mutant NSCLC were analyzed for clinicopathologic features, treatment details, and survival outcome. PFS prediction models were built using ensemble decision trees, and random forest. Ensemble decision trees were built and validation was performed using survival analysis. Clustering regression techniques were then applied to train and test the prediction of the 1st PFS of patients. RESULTS: The median age of the cohort was 59 years comprising 53% males and 47% females. 275 (66.5%) patients showed a del19 mutation type and 138 (33.5%) harbored L858R. After clustering, the most important variables were age (P<0.05), ECOG performance status (PS) (P<0.04), PDL1 (P<0.09), smoking status (P<0.01) and to a lesser extent, number of extrathoracic metastasis (ETM) sites (median 1.2, P<0.06), brain metastasis (P<0.06) and gender (P<0.08). The prediction for 1st PFS for del19 showed mean absolute error of 2.6 months and 4.72 months for L858R. The accuracy was 79.8% with 82% sensitivity, 79% specificity and AUC: 0.72. The precision was 92% with a Mathews correlation coefficient of 0.59. CONCLUSION: This study used machine learning modeling with fair accuracy to demonstrate that ECOG PS, age at diagnosis, and smoking status are the three main predictive factors of PFS in these patients.

Unusual Resistance Mechanisms in a Case of ROS 1-Rearranged NSCLC: A Case Report.

The unprecedented growth of the high-throughput next-generation sequencing has facilitated the identification of rare oncogene fusions such as ROS1 for NSCLC. ROS1 rearrangement has been observed in only 2% of cases of NSCLC and has been successfully targeted using various tyrosine kinase inhibitors including crizotinib. However, the on-target and off-target mechanisms of the resistance are still vague. Here, we report a case of a patient with ROS1 rearranged NSCLC presenting primary resistance to crizotinib.

Clinicopathological aspects of V-Raf murine sarcoma viral oncogene homolog B1 (BRAF) mutated non-small cell lung carcinoma in an Indian cohort: is there a difference?

INTRODUCTION: Activating mutations in the BRAF gene have been reported in 0.8%-8% cases of NSCLC. Traditionally, diagnostics have mainly focused on detection of V600E and modalities like mutation specific IHC, allele specific real-time PCR have been utilized. This may underestimate true prevalence of the non-V600E variants. Broader panel NGS testing offers a one stop solution and may identify newer potentially targetable variants. This is a retrospective single center experience of patients with BRAF mutated NSCLC characterizing the molecular spectrum and clinicopathologic characteristics. METHODS: 260 patients underwent panel based NGS testing at our center, between 2017-2020. 13 BRAF mutant cases, were detected and were clinically reviewed. RESULTS: Thirteen cases of BRAF alterations were seen in out of 260 (5%) patients. Median age of the cohort was 62 years (range: 39-86 years) with a female predilection). Canonical BRAF V600E mutation was seen in 6 (46.2%) patients and 7 (53.8%) harbored a non-V600E alteration. Spectrum of non V600E alterations included G466E, G469A, N581I, V600_K601delins, D594G, L597Q, G649V and were commonly female (P>0.01) with a higher trend for liver metastases (P=0.09). Median PFS was 4.8 months on chemotherapy (P=0.8). All patients (13/13, 100%) were never smokers with an adenocarcinoma histology. CONCLUSION: This is a single center experience from an Indian NSCLC cohort and shows higher prevalence of non-V600E than V600E mutation reported in literature. This may be attributed to increased use of NGS testing revealing otherwise missed alterations on sequential single gene testing.