Delfi-derived cell-free DNA fragmentation patterns differentiate histologic subtypes of lung cancers in a non-invasive manner

Abstract

The present disclosure provides methods of uses thereof for improved diagnostic applications using genome-wide patterns of fragmented cell-free DNA (cfDNA) from plasma, derived by low-coverage whole-genome sequencing. In particular, the present invention provides new and effective methods for subtyping non-small cell lung cancer in a subject as low neuroendocrine or high neuroendocrine non-small cell lung cancer.

Description

This invention provides a method for improving diagnostic applications using whole-genome patterns derived from fragmented cell-free DNA (cfDNA) obtained through low-coverage whole-genome sequencing. Specifically, this invention provides a novel and efficient method for classifying non-small cell lung cancer subtypes in subjects as either hyponeuroendocrine or hyperneuroendocrine non-small cell lung cancer. Abstract

Claims

PATENTATTORNEY DOCKET NO. DELFI2150-3 WOCLAIMS1. A non-invasive method for subtyping small cell lung cancer in a subject as low neuroendocrine or high neuroendocrine small cell lung cancer comprising: processing cfDNA fragments from a sample obtained from the subject and generating sequencing libraries; subjecting the sequencing libraries to whole genome sequencing to obtain sequenced fragments, wherein genome coverage is about 30* to O.lx; mapping the sequenced fragments to a genome to obtain genomic intervals of mapped sequences at specified transcription factor binding sites; analyzing the genomic intervals of mapped sequences to determine cfDNA fragment lengths and amounts to establish a cfDNA fragment coverage score at specified transcription factor binding sites using the cfDNA fragment lengths and amounts; and subtyping the small cell lung cancer in the subject based on transcription factor activation; wherein a decrease in an aggregate cfDNA fragment coverage scores at the specified transcription factor binding sites is indicative of a high-neuroendocrine small cell lung cancer subtype.

2. The method of claim 1, wherein the specified transcription factor is ASCL1, NEURODI, POUF23, YAP1, or any combination thereof.

3. The method of claim 1, wherein the method uses machine learning to subtype small cell lung cancer in the subject.

4. The method of claim 1, wherein subtyping is performed by calculating a log2 (Read Depth Ratio) across 20bp windows starting 2kb from the specified transcription factor binding sites.

5. The method of claim 4, wherein the log2 (Read Depth Ratio) is the total coverage over a coverage correction factor calculated using the median coverage of 5' and 3' 500bp anchors at the ends of the 2kb window (i.e. log2(Read Depth / Correction Factor)).

6. The method of claim 5, wherein all coverage calculations are shifted by 1 to avoid divisions by 0.44ACTIVE\1607300700.2PATENTATTORNEY DOCKET NO. DELFI2150-3 WO7. The method of claim 5, wherein the log2 (Read Depth Ratio) is calculated for each transcription factor binding site and then aggregated by calculating the median of each window to obtain a fragment coverage score.

8. The method of claim 1, further comprising the use of a general additive model to smooth coverage and correct for GC bias.

9. The method of claim 1, wherein the genomic intervals are non-overlapping.

10. The method of claim 1, wherein the genomic intervals each comprise thousands to millions of base pairs.

11. The method of claim 1, wherein a cfDNA fragmentation profile is determined within each genomic intervals.

12. The method of claim 11, wherein the cfDNA fragmentation profile comprises a median fragment size.

13. The method of claim 11, wherein the cfDNA fragmentation profile comprises a fragment size distribution.

14. The method claim 1, further comprising administering to the subject identified as having a high-neuroendocrine small cell lung cancer subtype, a therapeutic agent suitable for the treatment of the subtype of cancer.

15. The method of claim 14 wherein the therapeutic agent is an immunotherapy.

16. A non-invasive method for subtyping non-small cell lung cancer in a subject as adenocarcinoma or squamous carcinoma comprising: processing cfDNA fragments from a sample obtained from the subject and generating sequencing libraries; subjecting the sequencing libraries to whole genome sequencing to obtain sequenced fragments, wherein genome coverage is about 30* to O.lx; mapping the sequenced fragments to a genome to obtain genomic intervals of mapped sequences at specified transcription factor binding sites; analyzing the genomic intervals of mapped sequences to determine cfDNA fragment lengths and amounts to establish a cfDNA fragment coverage score at specified transcription factor binding sites using the cfDNA fragment lengths and amounts; and45ACTIVE\1607300700.2PATENT ATTORNEY DOCKET NO. DELFI2150-3 WO subtyping the small cell lung cancer in the subject based on transcription factor activation; wherein a decrease in an aggregate cfDNA fragment coverage scores at the specified transcription factor binding sites is indicative of an adenocarcinoma or squamous carcinoma subtype.

17. The method of claim 16, wherein the specified transcription factor is ASCL1, NEURODI, POUF23, YAP1, or any combination thereof.

18. The method of claim 16, wherein the method uses machine learning to subtype small cell lung cancer in the subject.

19. The method of claim 16, wherein subtyping is performed by calculating a log2 (Read Depth Ratio) across 20bp windows starting 2kb from the specified transcription factor binding sites.

20. The method of claim 19, wherein the log2 (Read Depth Ratio) is the total coverage over a coverage correction factor calculated using the median coverage of 5' and 3' 500bp anchors at the ends of the 2kb window (i.e. log2(Read Depth / Correction Factor)).

21. The method of claim 19, wherein all coverage calculations are shifted by 1 to avoid divisions by 0.

22. The method of claim 19, wherein the log2 (Read Depth Ratio) is calculated for each transcription factor binding site and then aggregated by calculating the median of each window to obtain a fragment coverage score.

23. The method of claim 16, further comprising the use of a general additive model to smooth coverage and correct for GC bias.

24. The method of claim 16, wherein the genomic intervals are non-overlapping.

25. The method of claim 16, wherein the genomic intervals each comprise thousands to millions of base pairs.

26. The method of claim 16, wherein a cfDNA fragmentation profile is determined within each genomic intervals.

27. The method of claim 26, wherein the cfDNA fragmentation profile comprises a median fragment size.

28. The method of claim 26, wherein the cfDNA fragmentation profile comprises a fragment size distribution.46ACTIVE\1607300700.2PATENTATTORNEY DOCKET NO. DELFI2150-3 WO29. The method claim 16, further comprising administering to the subject identified as having an adenocarcinoma or squamous carcinoma subtype, a therapeutic agent suitable for the treatment of the type of cancer.

30. The method of claim 29, wherein the therapeutic agent is an immunotherapy.

31. The method of claim 16, further comprising quantifying a circulating tumor fraction, mirroring major allele frequency (MAF) performance in relation to Response Evaluation Criteria in Solid Tumors (RECIST) evaluation in both adenocarcinoma and squamous carcinoma.47ACTIVE\1607300700.2

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